Mos Sleep Scale A Manual For Use And Scoring

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Results Mean scores on the MOS-S Sleep Index II were significantly worse for both the MS and SCI samples than those of previously reported samples representative of the US general population (p. The critical role of sleep in maintaining health, functional ability, and quality o f life within the general population has received increasing attention in recent years but relatively little is known about the prevalence and impact of sleep disturbances in chronic neurological conditions such as multiple sclerosis (MS) and spinal cord injury (SCI). One factor that has hampered research efforts in this area has been the difficulty of accurately assessing sleep in these populations. While many aspects of sleep can be measured using polysomnography and/or actigraphy, both of these methods require relatively expensive equipment and interpretation of the results by specially trained personnel, factors which make it difficult to administer to large study samples. Self-report measures represent an additional, complementary approach to assessing sleep and allow for individuals to provide information about their own sleep experience. A number of self-report measures have been developed to assess various aspects of sleep including both objective (e.g., total sleep time, sleep onset latency) and subjective (e.g., sleep quality) characteristics of sleep. Self-report measures are relatively inexpensive to administer, do not require specialized equipment, and do not require as much time or expertise to score as either polysomnography or actigraphy.

The use of self-report measures to assess sleep is also hindered by a number of factors. One of the defining hallmarks of sleep is a diminution of awareness, making self-report of sleep an inherently difficult proposition.

Additionally, most individuals experience night-to-night variation in their sleep, complicating the task of describing a typical night’s sleep. Despite these limitations, much of the existing research examining sleep in the context of chronic neurological disorders is based on such self-report measures.

– All of these studies documented a high prevalence of disturbances of sleep and sleep-related function. However, a clear picture of sleep in the context of chronic neurological impairment has not yet emerged due in part to the diversity of self-report measures that have been used in this small number of studies. This raises the question of which instrument is best suited to assessing sleep-related disturbances in those with chronic neurological disorders. One such measure, the Medical Outcomes Study Sleep scale (MOS-S) has been used in studies of sleep of the general population as well as diagnostic groups including rheumatoid arthritis and breast cancer. The MOS-S assesses six dimensions of sleep, including sleep disturbance, snoring, shortness of breath or other respiratory issues, sleep quantity, sleep adequacy, and daytime somnolence as well as a summary measure of sleep quality. In addition, in an effort to address some of the limitations of self-report measures, the National Institutes of Health devised the Patient Reported Outcomes Information System (PROMIS) initiative with the aim of developing a set of self-report item banks for measuring multiple domains of health and well-being.

The sleep domain consists of two item banks; one which addresses sleep disturbances, while the other focuses on impairments that are related to sleep but experienced while awake. The PROMIS sleep disturbance (v1.0 8b; PROMIS-SD) and sleep-related impairments (v1.0 8a; PROMIS-SRI) short forms were developed to measure these content areas. The objective of the current study was to examine sleep in two samples of individuals with either MS or SCI using the Medical Outcomes Study Sleep (MOS-S) and the Patient Reported Outcomes Information System (PROMIS) sleep domain measures, and to compare scores for both of these groups with published normative scores. Methods Data for this study represent a cross-sectional assessment collected as part of a longitudinal study of the self-reported health of people with MS or SCI. The Human Subjects Division of the University of Washington approved all study procedures for the initial data collection, which are described in detail in a previous publication. Briefly, participants with MS were recruited through the Western Washington chapter of the National MS Society, and those with SCI were recruited either through the Northwest Regional Spinal Cord Injury Model System at the University of Washington, (Seattle, WA) or the Shepherd Center, Virginia Crawford Research Institute (Atlanta, GA).

All participants were at least 18 years of age at enrollment and reported a definitive diagnosis of either MS or SCI. Data for this study were collected at the fifth time point in the longitudinal study (approximately 16 months after study commencement), which was the only point at which the MOS-S, PROMIS-SD and PROMIS-SRI short forms were all included in the survey. A total of 700 participants (MS N=461; SCI N=239) returned surveys during that administration. Measures The MOS-S consists of twelve items measuring six dimensions of sleep, including sleep disturbance (incorporating both initiation and maintenance of sleep), snoring, shortness of breath or other respiratory issues, sleep quantity, sleep adequacy, and daytime somnolence.

Ten of the items on this scale are scored on a scale from 0 to 5, with lower numbers reflecting lower frequency of the sleep related complaint (0 = none of the time, 5 = all the time). One question about how long it took to fall asleep is on a scale of 1 to 5 (1=0–15 minutes, 5 = more than 60 minutes). The final item, which relates to sleep quantity, is reported as average number of hours slept each night. There is a four-week response frame for all items. Sleep Problems Index II is a summary measure of sleep quality derived from scores on 9 of the 12 items.

Scores for the summary index and for subscales measuring five of the six sleep dimensions range from 0–100, with higher scores indicating more of the attribute measured. In studies of large populations, the MOS-S scale has shown good psychometric properties. Comparison scores for this measure are from a study of the psychometric properties of the MOS-S reported by Hays et al. The PROMIS-SD short form includes eight items assessing the participant’s perception of aspects of sleep such as its quality and adequacy and the ease of both falling and staying asleep. The PROMIS-SRI short form, also consisting of eight items, assesses difficulties that are related to sleep but experienced while awake, such as sleepiness and difficulty concentrating because of poor sleep.

The response frame for both short forms is seven days. For each short form, individual items are scored on a scale from 1 to 5, and scores were summed to yield a total raw score between 8 and 40, with lower scores indicating better sleep or a lesser degree of sleep related impairments. The summed raw scores were used to find corresponding IRT-base scores using the lookup tables provided with the PROMIS scoring guides. All PROMIS scores use a T metric, i.e., the mean is 50 and the standard deviation is 10. The sample used for calibrating the items for both PROMIS short forms (N=2,252) included two cohorts.

The first N=1993) was drawn from the general population. A second cohort (N=259) was recruited from sleep clinic, creating a sample (N=2252) that included a higher proportion of individuals experiencing sleep disturbances than would be expected in the general population. We randomly selected 453 individuals from Buysee’s general population sample to create a subgroup that is matched to the 2000 US census data on age and gender. Analyses Descriptive statistics were used to summarize demographic information for each sample (MS and SCI). Demographic information on the two previously reported cohorts used for comparison with our samples are also described in.

The first of these comparison groups is the general population cohort reported by Hays et al., and the second is a subgroup of the sample used by Buysse et al. For calibrating PROMIS-SD and PROMIS-SRI scores. Of the 700 surveys received during this time point (MS N=461; SCI N=239), data on the MOS-S item about snoring while asleep was missing from 5.9% (N=27) of the MS sample and 2% (N=5) of the SCI sample; these were the only missing data for the MOS-S. This item was included in Sleep Index II and the sole item on the snoring subscale. In accordance with the scoring guidelines for the MOS-S, the Sleep Index II score was calculated as an average of the non-missing items for these two participants and the snoring subscale was not scored.

Two participants with MS had missing data for the PROMIS-SD and the PROMIS-SRI; there were no missing data for the SCI participants on either of these measures. Participants who missed any item on one of the PROMIS short forms had a missing score for that scale.

Demographic information The MS and SCI samples’ scores for the summary MOS-S scale (sleep index II), each of the MOS-S subscales, the PROMIS-SD and PROMIS-SRI were examined using histograms and Quantile-Quantile (QQ) plots and tested for normality with the Shapiro-Wilk statistic. Scores for each of the neurologically impaired samples on the MOS-S Sleep Index II (the summary measure of sleep) were compared to those of the general population cohort reported by Hays using the one-sample t-test (see ). We elected to use the one-sample t-test despite the fact that the assumptions of normality were not fully met, as the median scores which would be required to perform non-parametric tests were not available for the Hays cohort.

Results The average age of MS sample was 52.8 years, and 91.5% of these participants were white. Among those with SCI, the average age was younger (47.4 years), and the percentage of white participants was lower (78.7%). The high percentage of women among the MS sample (82%) and of men in the SCI sample (61.5%) are both consistent with the distribution in the population. For both cohorts, the average time since diagnosis was over a decade (MS = 14.5 years; SCI = 13.4 years) (see ). The Shapiro-Wilk test indicated significant departures from normality (p0.74) with both the PROMIS-SD and PROMIS-SRI.

The MOS-S Sleep Disturbance and Sleep Adequacy scales each correlated very strongly (>0.7) with PROMIS-SD scores. The Cronbach’s alpha coefficient for the MOS-S sleep problem index II was 0.41 for the MS sample and 0.54 for the SCI sample. The Cronbach’s alpha coefficients were the same in both samples for MOS-S sleep disturbance (4 items, α=0.84) and sleep somnolence subscales (3 items, α=.75). The PROMIS-SD and PROMIS-SRI scales each had Cronbach’s alpha coefficients above 0.9. Discussion In this study, we examined measures of sleep and sleep-related impairments in a sample of adults with chronic central nervous system impairment due to either MS or SCI by concurrently administering the MOS-S, PROMIS-SD, and PROMIS-SRI scales. Scores for both the MS and SCI samples on the MOS-S summary measure, Sleep Index II, as well as on most of the subscales differed significantly from those of the general population cohort previously reported by Hays.

The findings from the MOS-S scale are consistent with existing literature concerning sleep in those with MS or SCI using measures other than the MOS-S., – In each of these studies, neurological impairment was associated with significantly disturbed sleep, lending face validity to the MOS-S findings from this study. Like the MOS-S, the scores on the PROMIS sleep domain short forms for the MS sample were significantly different than that of the calibration group. The responses of the SCI sample on the PROMIS-SRI also showed significantly higher amounts of impairment than the calibration cohort, but no such differences were detected on the PROMIS-SD. This finding contradicts not only previous research, but also the results of the MOS-S when administered to the same sample at the same time.

Furthermore, the magnitude of the differences between the PROMIS- SD and PROMIS-SR scores for both diagnostic samples and those of the calibration cohort, even when they reached statistical significance, are not clinically significant. When the means for these two samples are compared to those of the calibration cohort, three of the comparisons (the PROMIS-SD for both samples and the PROMIS-SRI for the SCI sample) yield an absolute difference of less than one point. Even the largest of these differences (2.55 for the MS sample’s PROMIS-SRI score) is well within one half of a standard deviation, a commonly accepted standard for minimally important clinical difference.

There are a number of possible reasons that the MOS-S showed significant sleep difficulties for those with SCI while the PROMIS did not. One of the notable differences between the instruments is the measurement window.

While the MOS-S asks about the participants’ experiences over the past four weeks, the PROMIS instruments use a seven day timeframe. While the shorter measurement window employed by the PROMIS instruments would likely increase recall accuracy, it may have resulted in under-reporting of more episodic or transitory sleep problems. The discrepancy in findings could also be due to the characteristics of the comparison groups used for the different measures. Both of these comparison groups were drawn from the general adult population, and were comparable in terms of average age (MOS-S = 46; PROMIS=45) and gender (MOS-S=51% women; PROMIS=51.88% women). Comparison of the two cohorts in terms of race is more difficult because of the different coding schemes used. Participants in the PROMIS calibration sub-group could endorse more than one race, and Hispanic/Latino was reported as a separate item.

As noted above, the calibration sample for the two PROMIS sleep domain measures included a greater proportion of individuals with sleep complaints than would be found in the general population. However, the mean scores for the subsample we used, which was drawn from the general population rather than the clinical one, matched that of Buysee’s sample as a whole. Alternatively, although the correlations between the MOS-S and PROMIS measures are statistically significant, they do not suggest that they measure the same construct other than for sleep disturbance and sleep adequacy. This may be related to the fact that important aspects of sleep in neurologic populations are not represented in PROMIS sleep instruments. The MOS-S scale includes items related to respiratory problems, snoring, and sleep quantity, which the PROMIS scales do not address.

It is also possible that the MOS-S is detecting issues related to disease characteristics that are not necessarily related to sleep. For example, fatigue, which is highly prevalent in both MS and SCI, is both commonly associated with and may be confounded with sleepiness.

This could also help account for the low Cronbach’s alpha levels for the MOS-S scores of both of the neurologic samples. Conclusion The findings of this study highlight the difficulties involved in measuring a complex, multidimensional construct such as sleep through self-reported outcome measures and the importance of carefully selecting comparison groups. While the MOS-S clearly identified that those with either MS or SCI had significant levels of sleep disturbance, the findings from the PROMIS measures were more ambiguous. The lack of a significant difference in PROMIS-SD scores between those with SCI and the calibration cohort was particularly surprising, given the body of literature suggesting that sleep is significantly impacted in this population.

Although the differences between the MS sample for both PROMIS measures and the calibration cohort did reach statistical significance, the magnitude of these differences is unlikely to be clinically meaningful. This is also true for the SCI sample scores for the PROMIS-SRI. Based on this analysis, either the MOS-S is better able to detect difficulties with sleep in those with either MS or SCI than the PROMIS sleep domain short forms, or it includes somatic symptoms of these conditions that are not necessarily related to sleep. These findings suggest that we do not currently know enough about how the PROMIS-SD and PROMIS-SRI items should be interpreted in those with chronic neurological difficulties to use these measures with confidence, and that particular care should be used in comparing scores from individuals with chronic neurological conditions to those of the general population.

Support: The contents of this publication were developed under a grant from the Department of Education, NIDRR grant numbers H133B031129 and H133B080025. However, those contents do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government. Research reported in this publication was also supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award number 5U01AR052171.

Fogelberg was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number K01HD076183. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated and we certify that all financial and material support for this research (e.g., NIH or NHS grants) and work are clearly identified on the title page of the manuscript.

Rheumatologic disorders are associated with sleep disturbances. This study examines sleep disturbance correlates in patients with SSc. Participants are 180 SSc patients in an observational study. At baseline, patients completed the Medical Outcomes Study Sleep measure (MOS-Sleep scale).

In addition, patients were administered other patient-reported outcome (PRO) measures including the 36-item short form (SF-36), HAQ disability index (HAQ-DI), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue), Center for Epidemiologic Studies Depression (CESD) scale and a University of California at Los Angeles Scleroderma Clinical Trial Consortium Gastrointestinal Tract Questionnaire (UCLA SCTC GIT 2.0). Descriptive statistics were assessed for six scales of MOS-Sleep and the 9-item sleep problem index (SLP-9; a composite index). We computed Spearman’s rank-order correlations between the MOS-Sleep scales and the HAQ-DI, FACIT-Fatigue, CESD, SSc-SCTC GIT 2.0 and SF-36 scales. In addition, we developed a regression model to assess predictors of SLP-9 scores.

Covariates included demographics, physician variables of disease severity and patient-reported variables of worsening symptoms and the PRO measures. SSc patients reported a mean ( s.d.) of 7.1 (1.73) h of sleep a night. Patients reported worse scores on four of six scales (except for snoring and sleep quantity) compared with the US general population ( P. Introduction SSc (scleroderma) is a chronic multisystem disease characterized by immune deregulation, vasculopathy and fibrosis. This immune dysfunction may cause chronic sleep disturbance that has detrimental effects on health and life expectancy []. Brain–immune interactions are an essential component in psychiatric and medical comorbidities that significantly impact health and sleep [, ].

SSc patients are believed to be at increased risk for sleep disturbance by polysomnographic findings [, ]. In one study, 27 consecutive SSc patients underwent all-night polysomnogram and investigators found that oesophageal dysmotility, dyspnoea and restless leg syndrome were associated with sleep disturbances. In other arthritides, sleep disturbances are associated with self-reported pain, depressed mood and fatigue []. Evaluation of a sleep disturbance requires assessment of multiple dimensions of sleep [].

While not a diagnostic tool, the Medical Outcomes Study Sleep (MOS-Sleep) scale has been studied in other chronic diseases and these studies provide support for the feasibility, reliability and validity of the MOS-Sleep scale []. This study examines the correlates of sleep disturbance in patients with SSc in an effort to better understand the interventions that may improve disease outcomes. Based on previous studies, we hypothesized that: (i) patients with SSc will have greater sleep disturbance compared with the US general population; and (ii) self-reported pain, depressed mood and upper gastrointestinal tract involvement will be associated with sleep disturbances. Patients University of California at Los Angeles (UCLA) Scleroderma Quality of Life Study is a single-centre ongoing longitudinal observational study where patients with SSc are invited to participate during their clinic visits. The current analysis reports the baseline data. Potential participants were approached at the time of a scheduled clinic visit and completed written consent and Health Insurance Portability and Accountability Act (HIPAA) forms.

The study was approved by UCLA Institutional Review Board (IRB), study number 7-07-061-01. Inclusion criteria include adult patients (≥18 years) with diagnosis of SSc.

Patients with SSc were further divided into limited SSc, diffuse SSc and overlap syndrome according to ACR criteria [, ]. Limited SSc is defined as skin thickening distal, but not proximal, to the knees and elbows, with or without facial involvement; diffuse SSc is defined as skin thickening distal and proximal to the knees and elbows, with or without facial involvement; and overlap syndrome is defined as patients with SSc and another rheumatic disease such as inflammatory myositis or RA. Patient-reported outcome measures MOS-Sleep measure, the 36-item Medical Outcome Survey Short Form (SF-36), the HAQ-disability index (HAQ-DI), the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue), the MOS-Social Support scale, the Center for Epidemiologic Studies Depression (CESD) scale and the UCLA Scleroderma Clinical Trial Consortium Gastrointestinal Tract Questionnaire (UCLA SCTC GIT 2.0) were administered to each patient. The MOS-Sleep is a 12-item instrument with six scales.

MOS-Sleep scores in the US general population and participants of the MOS For the US general population, Hays et al. Administered the MOS-Sleep measure by telephone to a nationally representative sample of 1011 US adults aged ≥18 years in January 2001 [, ]. The observations were weighted (using current population survey data) by age, gender, race, education, number of adults and number of voice/telephone lines in the household to reflect the adult (≥18 years) population. Beatcleaver Torrent. More detail on the instrument and population is available in Hays et al.

MOS participants MOS was an observational study to assess variations in physician practice styles and patient outcomes recruited in three large cities: Los Angeles, Boston and Chicago [, ]. Non-institutionalized English-speaking adult patients were screened and a subset of these patients ( n = 2471) with one or more of four conditions (hypertension, diabetes, advanced coronary artery disease and depression) were enrolled in a longitudinal study. This group formed the baseline cohort for development and validation of the MOS-Sleep instrument.

Statistics and analysis Mean scores, s.d.s, ranges and percentages of respondents scoring the minimum (floor) and maximum (ceiling) possible scores were calculated to evaluate distributions for all instruments. For easy interpretability, floor effect is presented as worst score and ceiling as best irrespective of the direction of the scale. Internal consistency reliability for HRQOL instruments was estimated using Cronbach’s α [].

An α of >0.70 is considered satisfactory. We compared the mean ( s.d.) scores of our SSc patients with the US general population and MOS population. We used standard t-test to compare the two groups. Each dimension of the MOS-Sleep questionnaire: sleep disturbance (SLPD); sleep adequacy (SLPA); daytime somnolence (SLPS); snoring (SLPSNR); awakening short of breath or with headache (SLPSOB); and quantity of sleep (SLPQ) as well as the SLP-9 were assessed by Spearman’s rank correlation with the HAQ-DI, FACIT-Fatigue, MOS-Social Support, CESD, UCLA SCTC GIT 2.0 total score, SF-36 PCS and MCS and single-item questions.

Correlations ≤ 0.29 were considered to be small, between 0.30 and 0. At Our Bodoni Regular Font here. 49 were moderate and ≥0.50 were large []. We developed multivariate regression models to assess the factors associated with sleep disturbance in SSc patients. SLP-9 was modelled as a continuous variable using a series of nested models to document the unique variance accounted for by different groups of variables, including demographics, physician- and patient-reported variables. Demographic variables (age, sex, ethnicity and BMI), physician-reported measures [patient global assessment on 0–100 visual analogue scale (VAS) and modified Rodnan skin score (MRSS)] and patient-reported measures—the HAQ-DI, FACIT-Fatigue, CESD, UCLA SCTC GIT 2.0 total score and single items (pain VAS, patient global assessment VAS, new/worsening of skin, RP or digital ulcers, and shortness of breath or chest pain) were used based on previous literature in SSc and other arthritides [, ]. Since Prado et al. [] found that reflux was associated with poor sleep in SSc, we also modelled five separate scales of the UCLA SCTC GIT 2.0 (reflux, distention/bloating, constipation, diarrhoea and faecal soilage). We did not include the GIT 2.0 social functioning and emotional well-being subscales or the SF-36 scales due to overlap with HAQ-DI, FACIT-Fatigue and CESD.

We used a multivariable linear regression model for demographics (Model 1) and physician-reported measures (Model 2). For Model 3, we used stepwise regression with forward selection (variables were included if P 0.15) and forcing the variables from Models 1 and 2.

This was done as there were large numbers of variables for the patient-reported measures. The coefficient of determination, R 2, was used in the context of this model to account for the proportion of variability in the data set that is accounted for by the statistical model. It provides a measure of how well future outcomes are likely to be predicted by the model. All analyses were performed using Stata 10.2 (Statacorp, College Station, TX, USA) and P. Results A total of 180 patients completed the MOS-Sleep scale and form the cohort in this study. Of these participants, 82% were female.

The mean ( s.d.) age of the participants was 51.1 (15.2) years, disease duration was 7.5 (8.1) years, 69% were Caucasian and 50.9% had limited SSc (). Patient global assessments were excellent/very good in 24 (13.3%), good in 69 (38.3%) and fair/poor in 87 (48.3%). Patients had mild functional disability (mean HAQ-DI 0.94) and mean PCS and MCS were 1.2 and 0.2 s.d. Less than that in the US general population, respectively []. There were 65 (36.1%) patients who had depressed mood (defined as CESD ≥10). Baseline characteristics of study participants SSc patients reported a mean ( s.d.) of 7.1 (1.73) h sleep a night (range 0–11 h) and reported worse scores on four of six sleep scales (except for snoring and sleep quantity) compared with the US general population and MOS population ().

The general population had an average age of 46 years (range 18–94 years); 51% were females and 81% were white []. The MOS participants ( n = 2471) had a mean ( s.d.) age of 55.5 (16.3) years, 60.5% were women and 79.6 were white. Minimal floor effects (suggesting most severe symptoms) were noted on scales and ranged from 1% (snoring) to 8% (sleep adequacy and snoring).

There were high-ceiling effects for snoring and shortness of breath scales (41 and 59%), similar to the MOS population (33 and 64%, respectively) []. Comparison of MOS-Sleep scales between SSc patients, previous MOS participants and US general population. Discussion This study examines the quality and quantity of sleep in patients with SSc. We found that patients with SSc have detrimental effects on their sleep compared with the US general population. We also found that presence of reflux, new or recent worsening of shortness of breath and gastrointestinal symptoms, and depressed mood were independent predictors of poor sleep.

Patient-reported pain, physician assessment of disease and demographics did not predict sleep disturbances. When compared with the US general population and MOS participants, our study showed that although sleep duration is comparable to that of the general population, on average, individuals with SSc have significantly more sleep difficulties on all scales, with the exception of snoring. In particular, fatigue, depressive symptoms and upper gastrointestinal involvement are important correlates to poor sleep quality in this population. The MOS-Sleep instrument captures important psychometric aspects to assess sleep quality and quantity, and is endorsed by the OMERACT []. It is feasible, reliable and has acceptable construct validity in the US general population and in patients with other chronic diseases, such as overactive bladder, refractory partial-onset epilepsy and painful peripheral neuropathy [, ].

MOS-Sleep was found to be responsive to change in a clinical trial on pre-gabalin use for neuropathic pain []. Our study shows that the MOS-Sleep scale is feasible, shows satisfactory internal consistency (≥0.70) and construct validity in SSc. [] assessed sleep in SSc and found that oesophageal dysmotility, dyspnoea and restless leg syndrome were associated with sleep disturbances. In his study, sleep complaints were intertwined with pain, depression and inflammation in many rheumatologic disorders. [] suggest an algorithm for identifying associated sleep disorders, which highlights the importance of mood and pharmaceutical interventions. In our multivariate analysis, depressed mood, presence of reflux and new or recent onset of shortness of breath or gastrointestinal symptoms were associated with sleep disturbances.

This is an especially important finding since acid reflux during sleep may be associated with delayed oesophageal clearance and aspiration []. Reflux has also been linked to SSc-associated interstitial lung disease and we recently showed that reflux symptoms are independently associated with depressed mood []. It remains to be seen if treatment of reflux and depression will improve quality of sleep in SSc. Until then, patients should be advised to sleep at an angle (wedge or raise bed) with aggressive pharmacological management of their reflux disease []. A detailed review of the sleep in different arthritides showed that pain is an independent predictor of sleep disturbance []. Although our univariate analysis showed that pain during the past 7 days was associated with SLP-9 ( ρ = 0.30), multivariate analysis did not show this association.

Also, tender and swollen joint counts were not associated with SLP-9. We have previously shown that patients with SSc and RA perceive pain differently [] and we did not assess pain using a validated instrument. Also, low prevalence of the tender and swollen joint counts may have also limited the ability to show an association. Effects of sleep and sleep deprivation on cytokines and immune dysfunction are recognized []. The pain, fatigue, distress and sleep disturbance observed in cancer patients during concurrent chemoradiation therapy is thought to be due to over-expression of pro-inflammatory cytokines []. TNF-α from peripheral blood mononuclear cells has been described as an important factor in excessive daytime sleepiness [, ].

This over-expression of this cytokine and other immunological abnormalities observed in patients with SSc including chronic mononuclear cell infiltration of affected tissues, dysregulation of lymphokine and growth factor production, and autoantibody production [] are reported, but the effect of sleep and sleep deprivation on these disturbances in SSc patients has not been studied. Further studies on the correlation of cytokines with sleep disturbance, depression and gastrointestinal disease in SSc are warranted. Our study has many strengths. First, this is the first comprehensive description of sleep disturbances using the MOS-Sleep in patients with SSc. Secondly, the comprehensive HRQOL data allowed us to assess correlates of sleep disturbance. It supports previous work that suggests that upper gastrointestinal disease and depression correlate with sleep disorders in SSc patients [].

Our study is not without limitations. First, this is a cross-sectional study so causal inferences cannot be made. Although we developed a regression model to assess predictors of poor sleep, association between sleep, depressed mood, reflux symptoms and shortness of breath may be bi-directional. Longitudinal analysis will assess causal associations. Also, we did not explore the reasons for worsening shortness of breath in our patient group. This may be related to gastric aspiration, asthma or new/worsening of cardio-pulmonary disease.

Secondly, the use of immunosuppressive medications, anti-reflux medications, hypnotics and anti-depressants was not captured and could affect the MOS-Sleep, thus the effect of intervention strategies is not known. Thirdly, cytokines were not analysed as part of this study to assess relationships between the immune system and sleep disturbances. This, in particular, is an important aspect for future studies and brain–immune interactions are an essential component in psychiatric and medical comorbidities []. Lastly, complications and severity of end-organ complications were not captured using a severity index.

We relied on overall global assessment of disease severity by physician and the patient. The importance of understanding sleep in SSc patients is recognized as an important area of research []. Although the multi-casual pathway of sleep disturbance is complex, in this study we found that reflux and depressed mood are important correlates that may be amenable to intervention.

Future studies of sleep disturbance in SSc and development of successful interventions for reflux and depressed mood in this population are of great need given the high prevalence and potential effect of sleep on overall disease burden. Funding: UCLA Scleroderma Study is supported by grants from the National Institutes of Health/National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIH/NIAMS) and the Scleroderma Foundation. Disclosure statement: D.K.

Is supported by NIAMS K23 AR053858-04 and the Scleroderma Foundation (New Investigator Award). UCLA Scleroderma Quality of Life Study is supported by NIAMS. Is a consultant for and has received honoraria from Abbott, Actelion, Amgen, BMS, Biogen Idec, Centocor, Genentech, Gilead, Corrona, GSK, NIH, Nitec, Novartis, Pfizer, Roche and UCB. Has a consultancy with Gilead. All other authors have declared no conflicts of interest.