Psychometric evaluation of the medical outcomes study-sleep scale in persons with overactive bladder

Clinical Therapeutics/Volume 28, Number 12, 2006

Psychometric Evaluation of the Medical Outcomes StudySleep Scale in Persons with Overactive Bladder Denys T. Lau, PhD1; RobertJ. Morlock, PhD2; and Cheryl D. Hill, PhD 3 1The Buehler Center on Aging~ FeinbergSchool of Medicine, Northwestern University, Chicago, Illinois; 2Outcomes Research, Worldwide Development, Pfizer Global Pharmaceuticals, Ann Arbor, Michigan; and 3patient Reported Outcomes, Research Triangle Institute Health Solutions, Research Triangle Park, North Carolina ABSTRACT Background: The Medical Outcomes Study-Sleep Scale (MGS-SS) is a commonly used self-reported instrument for assessing key constructs of sleep quality and quantity. Even though the MGS-SS has successfully undergone previous validation studies in the general population, it has not been evaluated in patients with overactive bladder (GAB). Objective: The aim of this study is to evaluate the applicability of the MGS-SS to persons with a diagnosis of GAB. Methods: This study was a follow-up to a national nested case-control survey designed to provide estimates of the prevalence of GAB in the United States. GAB patients (N = 363) who consented to participate were mailed a postal survey to assess GAB symptoms and health-related quality of life. Analyses were then undertaken to assess the psychometric properties of the MGS-SS in this GAB sample. Psychometric evaluation of the MGS-SS included construct validity, internal consistency reliability, correlation between domains, floor/ceiling effects, and an examination of the factor structure. Results were compared with the original validation population of the MGS-SS by Hays and Stewart (1992). Results: Internal consistency, correlations between domains, and floor/ceiling effects were generally consistent with results from the original validation study. Factor loadings of the MGS-SS items, as well as tests of construct validity, were similar between persons with GAB and individuals in the original validation population. Conclusion: Psychometric evaluation conducted in this study supports the use of the MGS-SS instrument to assess sleep problems among persons with GAB. (Clin Ther. 2006;28:2119-2132) Copyright © 2006 Excerpta Medica, Inc. December 2006

Key words: MGS-Sleep Scale, overactive bladder, psychometric evaluation, outcomes.

INTRODUCTION Overactive bladder (GAB) is a complex syndrome of symptoms characterized by urinary urgency, with or without urge incontinence, usually with frequency and nocturia. 1 Approximately 17 million people in the United States are estimated to have GAB. 2,3 The total cost of GAB in the United States each year is estimated to be >$12 billion, with $9 billion incurring in the community and $3 billion in institutions, such as longterm care facilities. 4 The prevalence of GAB, as well as its total costs, is likely to increase with an aging population. Social, physical, and emotional burdens of OAB on individuals can be quite high. s The symptoms of GAB can have significant effect on individuals' lives due to unpredictable urges that can result in discomfort, embarrassment, withdrawal from social life, physical and mental health deterioration, and interpersonal relationship disruptions. 3,6 Daily activities are often disrupted and restricted by having to schedule around the location of facilities to avoid potentially embarrassing situations. 7 Furthermore, GAB can have adverse effects on sleep activities due to nocturia. 1,8 Such interruptions can cause inconvenience and interfere with sleep regularity, as well as decrease the quality and quantity of sleep. 9 Sleep problems have been found to be associated with, among other things, poor social Accepted for pubfication November 3, 2006. doi:l 0.1016/j.clinthera.2006.12.008 0149-2918/06/$19.00 Printed in the USA.Reproductionin wholeor part is not permitted. Copyright© 2006 ExcerptaMedica, Inc. 2119

Clinical Therapeutics

interaction and diminished work productivity and work quality. 7a° Many studies have shown that urinary incontinence and patients with OAB have lower health-related quality of life (HRQOL) than healthy populations, u - u A variety of HRQOL instruments, both general and disease-specific, have been used to assess individuals with OAB. General HRQOL instruments (eg, the Medical Outcomes Study 36-Item Short Form Health Survey [SF-36] 15 [Medical Outcomes Trust, Inc., Boston, Massachusetts[ and the Sickness Impact Profile 16) and disease-specific HRQOL instruments for OAB (eg, the Kings Health Questionnaire, 17 the Incontinence Impact Questionnaire, 18 and the Incontinence Quality of Life Questionnaire 19) have demonstrated differences in OAB from other diseases and conditions. 2°-22 Although sleep problems are important multidimensional attributes affecting an individual's ability to enjoy life, they are commonly excluded from HRQOL questionnaires or are assessed with only a single item. The relationship between OAB and sleep complaints, therefore, is not well understood and warrants further investigation. The Medical Outcomes Study-Sleep Scale (MOS-SS) is a tool designed to assess both the quantity and quality of sleep. As OAB is expected to adversely impact various dimensions of sleep, the MOS-SS may be an ideal tool to capture this concept. The MOS-SS was developed as part of the Medical Outcomes Study and found to have good overall measurement properties. 23,24 Despite its widespread use, the validity of the MOS-SS has not been examined in persons with OAB. Given the significant impact of OAB on general HRQOL and its potential negative impact on sleep quality and quantity, examining the psychometric properties of MOS-SS in an OAB sample is an important step in establishing the usefulness of the tool. 25 It is recommended that the properties of an instrument be equivalent across the populations in which it is intended for use to ensure that scores for these groups can be compared. 26 Therefore, this study evaluated the psychometric properties of the MOS-SS in persons with OAB. These results were compared with those findings yielded in the original validation population of the MOS-SS by Hays and Stewart 23 to assess the reliability, validity, and factor structure of the instrument in an OAB sample. This study tested the hypothesis that the psychometric properties of the MOS-SS are consistent between the OAB sample and a normative sample, such as the original validation 21 20

population of the MOS-SS, therefore providing empirical evidence supporting that the MOS-SS may be acceptable for use in patients with OAB. PATIENTS AND METHODS

This study was a follow-up to a national nested casecontrol survey designed to provide estimates of the prevalence of OAB in the United States. 27 The computer-assisted telephone survey collected information on nocturia, urinary frequency, urinary urgency, and incontinence with a 4-week recall period from a nationally representative sample of the US population (n = 5204) using quota sampling methods. 2,27 Of the 1769 individuals who were invited to complete the follow-up survey via postal mail, 919 (52% response rate) returned their questionnaires. Data collected via mailed surveys included information on bladder symptoms, treatment, self-care, sleep routine, and HRQOL information. 27 Institutional review board approval was obtained before the initiation of the follow-up mailed study. Demographic variables collected from the respondents included date of birth, sex, race, level of education, and location of residence. Age groups were categorized as <45, 45-<55, 55-<65, and _>65. Race was categorized into white, black, and others. Nocturia was defined as waking up at night _>2 times specifically to urinate. 27

Patient-Reported Outcomes Respondents were asked to complete 2 measures of patient-reported outcomes: the MOS-SS and the SF-36 version 1. Both instruments used a 4-week recall period. The MOS-SS is a self-reported instrument assessing key constructs of sleep quality and quantity. 23 The MOS-SS consists of 12 items that assess 6 key constructs of sleep activities within a 4-week recall period: sleep disturbance (4 items), somnolence (3 items), perceived sleep adequacy (2 items), awaken short of breath or with headache (1 item), snoring (1 item), and sleep quantity (1 item). Score was calculated according to the guidelines recommended by the developers of the MOS-SS instrument. 23 Other than quantity of sleep, scores for the other 5 domains (sleep disturbance, snoring, awaken short of breath or with headache, perceived sleep adequacy, somnolence) ranged from 0 to 100, with higher scores reflecting more of the attribute implied by the scale name (eg, greater sleep disturbance and greater sleep adequacy). Sleep quanVolume 28 Number 1 2

D.T. Lau et al.

tity is measured by the number of hours of sleep obtained per night and is translated into an optimal sleep score where 7 to 8 hours of sleep is optimal. 23 Based on the original validation work, the 9-item global index of MOS-SS (3 sleep disturbance items, 3 somnolence items, 2 adequacy items, and awaken short of breath or with headache) was calculated to provide a composite score where a higher score indicated higher impairment.23 The SF-36 is an instrument designed to assess various key constructs of a patient's view of HRQOL. 28,29 The SF-36 assesses 8 health domains with a 4-week recall period: (1) physical functioning--limitation in performing physical activities; (2) physical role functioning-problems with work and other daily activities as a result of physical health; (3) bodily pain--limitations due to physical pain; (4) general health--overall personal health; (5) emotional role functioning--problems with work or other daily activities as a result of emotional problems; (6) social functioning--interference in social activities due to physical and emotional problems; (7) mental health--self-perception of depression and nervousness, as well as happiness and peacefulness; and (8) vitality--self-perception of fatigue and vivaciousness. 29 Two summary scores were calculated based on the 8 domains: physical component score (PCS) and mental component score (MCS). For each of the domains, scores ranged from 0 to 100, where higher scores indicated a better HRQOL. Scoring was calculated according to the guidelines recommended by the developers of the SF-36. 29

Psychometric Analysis Evaluation of the MOS-SS included the following: construct validity, internal consistency reliability, correlation between domains, floor/ceiling effects, and factor analysis. All data processing and psychometric analyses were performed using SAS version 8 (SAS Institute Inc., Cary, North Carolina) and LISREL 8.50 (Scientific Software International, Inc., Lincolnwood, Illinois). Factor analysis assessed the extent to which the predefined domains of the instrument held in the OAB sample. The original validation of the MOS-SS 23 identified the dimensions of the scale as follows: (1) sleep disturbance (items 1, 3, 7, and 8), (2) snoring (item 10), (3) awaken short of breath or with headache (item 5), (4) sleep adequacy (items 4 and 12), (5) somnolence (items 6, 9, and 11), and (6) sleep quantity (item 2). For these domain scores to be applicable in December 2 0 0 6

an OAB population, it was necessary to confirm this factor structure in the present sample. If the factor structure from the original validation study is not replicated in this OAB sample, then the domain scores produced by the MOS-SS may not truly represent the intended domain, rendering scores incomparable across populations. 26 When scales are evaluated for use in alternative populations, confirmatory factor analysis (CFA) is often used to confirm that the factor structure that fit into the original validation population holds in the alternative population. Unfortunately, the complete factor structure as defined in the original validation study cannot be tested using CFA because 3 of these factors were defined by only 1 measured variable (ie, the model is unidentified and a unique solution cannot be obtained). More specifically, "having two indicators per latent variable is sufficient to identify the measurement model" when certain regularity conditions have been met. 3° Thus, factors 2, 3, and 6 cannot be measured as specified previously, and an alternative parameterization must be introduced to confirm the domain structure. Consequently, we used an analytical approach that fit a model based on findings from the original validation stud> This model contains 4 factors: (1) sleep disturbance (items 1, 3, 7, and 8), (2) sleep adequacy (items 2, 4, and 12), (3) somnolence (items 6, 9, and 11), and (4) respiratory problems (items 5 and 10). In the original validation, sleep disturbance, sleep adequacy, and somnolence were well defined by their corresponding items, and we expected a similar result with this OAB sample. This model includes item 2 (sleep quantity) as part of the sleep adequacy factor. Hays and Stewart 23 found that "correlational and factor analyses indicated that quantity and perceived adequacy are highly interrelated." Although it is preferred that sleep quantity be scored separately, adding it to the model with the sleep adequacy items allows for the interrelation between sleep quantity and sleep adequacy as found in the original validation work to be tested. Because the sleep quantity item does not use a monotonic scale similar to that of the other items (high scores and low scores both can indicate poor functioning), the dichotomous optimal sleep item was used instead, where 7 to 8 hours of sleep is optimal and values outside of this range are suboptimal. This model also includes snoring (item 10) and awaken short of breath or with headache (item 5) as 2 items 21 21

Clinical Therapeutics

that define a fourth factor (respiratory problems). Hays and Stewart 23 originally hypothesized in their pilot study that these 2 items would tap into the same aspect of sleep respiratory problems but found that they were poorly correlated. Although we do not believe that these items are related enough to define a factor, a CFA that found 1 item with a high factor loading and 1 with a low factor loading would provide evidence that they, indeed, define separate domains. This model was estimated using the entire OAB sample with listwise deletion. LISREL's preprocessor, PRELIS, was used to first obtain the polychoric correlation matrix among the items along with their asymptotic covariance matrix. This matrix was then used in LISREL to fit the model using diagonally weighted least squares estimation. The model was evaluated by considering the magnitude of the factor loadings, the comparative fit index (CFI), 31 the non-normed fit index (NNFI), 32 and the standardized root mean square residual (SRMR). 31 For these measures of model fit, CFI >0.95, NNFI >0.95, and SRMR <0.08 are preferred. 33 Reliability was the extent to which a set of items were consistent with each other and reflected a single underlying construct of the MOS-SS domain. Cronbach's coefficient 0~was calculated for all multi-item domains, including sleep disturbance, sleep adequacy, somnolence, and the 9-item global index. An acceptable value for 0~ was between 0.70 and 0.90, which indicates a strongly related but not redundant set of items capable of supporting a unidimensional scoring structure. 34 We anticipated that our 0~ values would fall into this ideal range, just as they did in the original validation study. Correlations between domains were calculated to determine whether each of the domains in the MOS-SS was interrelated as hypothesized. All correlations were expected to be moderate except for snoring, which was found to have low correlations with the other domains in the original validation study. Pearson correlations were calculated between continuous measures, biserial or polyserial correlations were calculated between categoric and continuous measures, and polychoric correlations were calculated between categorical measures. The optimal sleep item was used in place of the sleep quantity domain to ensure linearity. In addition, the 9-item global index was included in the analysis to determine whether its correlation to other sleep domains was similar. Correlation coeffi2122

cients >0.50 were considered strong and those between 0.30 and 0.50 were considered moderate. 35 Floor and ceiling effects were assessed based on the percentage of people with the lowest and highest scores in each domain of the MOS-SS, respectively. Floor/ceiling effects were not analyzed for optimal sleep because the domain was scored dichotomously and each person would be either at the floor or at the ceiling. It is expected that the number of people at the floor or ceiling would be minimal (eg, no more than 10%), except for awaken short of breath, which has been shown to be a rare condition.23 Construct validity of the MOS-SS was determined by (1) association of MOS-SS domain scores to selfperceived general health status reported by the item of the SF-36, (2) correlations between MOS-SS domain scores and the health domains of the SF-36, and (3) t tests on the domain scores for known groups of OAB participants. Strength of association between MOS-SS domain scores and general health status was assessed using analysis of variance to examine the differences in domain scores for each of the general health status categories. Domain scores and general health were expected to demonstrate convergent validity by being significantly related, where poorer general health is associated with poorer sleep scores. Convergent validity also was supported if the MOS-SS scores were correlated with similar domains in the SF-36. It was anticipated that all domains except snoring and optimal sleep would be moderately correlated with the SF-36 domains and that these correlations would be strongest with the SF-36 vitality score. Pearson correlations were calculated between continuous measures, biserial or polyserial correlations were calculated between categorical and continuous measures, and polychoric correlations were calculated between categorical measures. The optimal sleep item was used in place of the sleep quantity domain to ensure linearity. For construct validation by known groups, OAB participants were defined as OAB persons with nocturia and OAB persons without nocturia. Construct validity would be supported if people with nocturia exhibited poorer sleep quality and quantity (eg, sleep disturbance, sleep adequacy, somnolence) compared with those without nocturia. No differences were expected between the groups for snoring or awaken short of breath or with a headache, which would be less affected by waking up to urinate. Volume 28 Number 12

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To avoid reprinting findings published in the original validation study by Hays and Stewart, 23 we present their results in summary terms, highlighting relevant key findings. Information on the study by Hays and Stewart can be obtained from the lead author (D.T.L.).

RES U LTS Sample Description Among the 363 individuals reported as having OAB (Table I), the mean age was 57 years, with 36% of these patients aged _>65 years. Most were women and white, and the most frequently reported health status category was "good." Slightly more than half (53% [191/363]) of the individuals with OAB reported having nocturia. Individuals with nocturia were older than those without nocturia, and there were no differences between the sex, race, and general health status proportions between these 2 groups. MOS-SS scores are presented for all OAB individuals, as well as individuals with and without nocturia

and a normative sample* (Table II). Scores for this normative sample of the US general population were reported in a recent article by Hays et al. 36 All of the sleep domain scores, except quantity of sleep, were significantly lower in the OAB sample compared with the normative sample (1-sample t test, P < 0.05), suggesting that our study sample in general had poorer sleep quality (Table II). Because sleep quality is a multidimensional construct (eg, difficulty in initiating and maintaining sleep, and feeling rested) and the psycho*Approximately 9% (n = 35) had _>1 missing score in their MOSSS questionnaire. The 2 most common missing scores were related to snoring (n = 19) and taking naps during the day (n = 13). An analysis of persons with missing items found that they did not differ significantly from those persons with all their MOS-SS data with regard to age, sex, race, level of education, location of residence, continence, nocturia, and health status, suggesting that the items' nonresponse in the MOS-SS scores were at random (Appendix I). An analytical decision, subsequently, was made that the final study population included only the 363 OAB cases with complete MOS-SS data.

Table I. Sample characteristics in patients with overactive bladder (OAB) with or without nocturia.

Characteristic Age group, no. (%) <45 y 45-<55 y 55-<65 y _>65 y

With Nocturia (n = 191)

Without Nocturia (n = 172)

Z 2 (P)*

All Patients (n = 363)

26.1 (<0.001) 29 29 47 86

(15) (15) (25) (45)

55 42 30 45

(32) (24) (1 7) (26)

Sex Female Male

110 (58) 81 (42)

82 (48) 90 (52)

Race White Black OtheH

157 (82) 18 (9) 16 (8)

1,51 (88) 14 (8) 7 (4)

84 71 77 131

(23) (20) (21) (36)

3.6 (0.06) 1 92 (53) 177 (47) 3.2(0.21)

General health Excellent Very good Good Fair Poor

26 44 59 44 18

MOS-SS: Optimal Sleep

81 (42)

308 (8S) 32 (9) 23 (6)

2.4 (0.67) (14) (23) (31) (23) (9)

23 47 58 31 13

(13) (27) (34) (18) (8)

110 (58)

49 (14) 91 (25)

117 (32) 75 (21) 31 (9) 11.8 (0.001)

191 (53)

MOS-SS = Medical Outcomes Study-Sleep Scale. *X2Test for difference between OAB sample with nocturia and OAB sample without nocturia. flncluded Native American, Asian, and Hispanic.

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Table II. Medical Outcomes Study-Sleep Scale (MOS-SS) scores in patients with overactive bladder (OAB), with or without nocturia, and a normative sample.

Scale MOS-SS domain Disturbance Somnolence Adequacy Awaken short of breathll Snoring Quantity 9-Item global

Overall OAB Mean (SD)

32.5 32.3 52.7 14.8

General Mean*f

t Test (p)t

OABwith Nocturia Mean Adjusted for Age

OAB Without Nocturia Mean Adjusted for Age

F Score (P)§

(24.1) (22.3) (26.0) (23.4)

24.5 21.9 60.5 9.5

6.37 8.93 5.67 4.33

(<0.001) (<0.001) (<0.001) (<0.001)

38.2 36.2 50.5 18.1

26.2 28.1 55.2 11.1

15.83 10.99 0.25 4.76

(<0.001) (0.001) (0.62) (0.03)

38.5 (33.7) 6.8 (1.4) 34.7 (19.6)

28.3 6.8 25.8

5.72 (<0.001) 0.11 (0.92) 7.70 (<0.001)

40.8 6.6 37.8

35.9 7.0 29.2

1.51 (0.22) 4.60 (0.03) 10.13 (0.002)

* Data on the US general population were reported by Hays and Stewart. 23 fSD was not available for the US general population. tOne-sample t test comparing OAB sample to normative mean. § Regression of MOS-SS domains on nocturia status controlling for age. IIAwaken short of-breath or with headache.

metric properties of a patient-reported outcome instrument should be equivalent across the populations of its intended use, 2s,26 the difference between this OAB sample and the normative sample highlights the need for this instrument to be validated for use in OAB populations. We also analyzed the floor and ceiling effect (lowest and highest possible scores, respectively) of the MOS-SS scores and found that 2 sleep domains had >10% of persons with OAB at the floor: snoring and awaken short of breath or with headache (Appendix II). At the ceiling, only snoring had >10% of persons with OAB.

problems, but the difference in magnitude indicates that their relationship is not strong enough to define a factor. The model fits well (CFI = 0.98, NNFI = 0.98, SRMR = 0.07), and these results are consistent with the factor pattern found in the original validation study.

Factor Analysis The results of the CFA are presented in Table III. As expected, the items that measure sleep disturbance, sleep adequacy, and somnolence load highly on their specified domain (range of factor loadings, 0.50-0.91). Awaken short of breath and snoring have very different factor loadings on the respiratory domain (0.80 and 0.27, respectively). Both factor loadings are significantly different from zero (P < 0.001), suggesting that they are both measures of respiratory

Correlations Between Domains The sleep adequacy domain among persons with OAB had strong negative correlation coefficients (r = -0.43 to -0.82) with disturbance, somnolence, optimal sleep, and the 9-item global index (Table IV). Optimal sleep also had negative correlations with the same domains as sleep adequacy but with smaller coefficients (r = -0.31 to -0.54). Sleep adequacy and optimal sleep were positively correlated (r = 0.43). Sleep disturbance had positive correlations (r = 0.50-0.90)

21 24

Reliability

Persons with OAB had high 0~ coefficients in all of the sleep domains and indices (disturbance = 0.84; somnolence = 0.77; adequacy = 0.73; 9-item global = 0.87), which are similar to the reliability indices reported in the original validation study (range, 0.75-0.86). 23

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Table III. Confirmatory factor analysis Ioadings (SE) and measures o f model fit in patients with overactive bladder (N = 363).* Factor

Disturbance

Somnolence

Adequacy

Respiratory

1. Minutes to fall asleep 3. Sleep was not quiet 7. Difficulty initiating sleep 8. Difficulty maintaining sleep 6. Drowsy during the day 9. Difficulty staying awake during the day 11. Take naps during the day 2. Optimal sleep 4. Enough sleep to feel rested 12. Get the right amount of sleep 5. Awaken short of breath/headache 10. Snoring during sleep

0.74 0.77 0.91 0.80

0 0 0 0 0.88 (0.04) 0.88 (0.04) 0.s0 (0.06) 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

o

o

0.62 (0.05) 0.6s (0.0s) 0.80 (0.04) 0 0

0 0 0 0.80 (0.13) 0.27 (0.08)

(0.03) (0.04) (0.02) (0.03) 0 0 o

0 0 0 0 0

*Zeros indicate factor Ioadings that were fixed to 0. Comparative fit index = 0.98; non-normed fit index = 0.98; standardized root mean square residual = 0.07.

Table IV. Medical O u t c o m e s Study-Sleep Scale (MOS-SS) d o m a i n - t o - d o m a i n correlations* in patients with overactive bladder (N = 363). MOS-SS Domain MOS-SS Domain Disturbancef Somnolencef Adequacyf Awaken SoBII Snoringll Optimal sleepll 9-1tem globalf

Disturbance

Somnolence

Adequacy

0.44t -0.52§ 0.50§ 0.15 -0.54§ 0.90§

-0.44t 0.41 t 0.23 -0.31 0.66§

-0.43t -0.11 0.43t

-0.77§

Awaken SoB

Snoring

Optimal Sleep

-

0.22 -0.36t 0.66§

-0.I 3 0.20

-

-0.55§

SoB = short of-breath (or with headache). *Correlations between continuous measures are Pearson, correlations between continuous and categorical measures are biserial or polyserial, and correlations between categorical measures are polychoric. All correlations are significant at P < 0.05. tContinuous measures. *r = 0.30-0.50 (absolute value). §r _>0.50 (absolute value). IICategorical measures.

with a w a k e n short of breath and the 2 global indices. Somnolence also had similar strong positive correlations (r = 0 . 4 1 - 0 . 6 6 ) with the same domains as sleep disturbance, except with slightly smaller coefficients. Sleep disturbance and somnolence were positively correlated (r = 0.44). Snoring had the weakest correlations with all the other sleep d o m a i n s (absolute value, December 2006

r = 0.13-0.23). The 9-item global index had similar patterns of correlations with the other individual MOS-SS sleep domains and the 2 global indices are strongly correlated with each other (r = 0.98). The pattern of correlations parallels those found in the original validation study in that the direction and m a g n i t u d e of the correlations were largely similar. 2125

Clinical Therapeutics

Snoring was the most distinct and had the weakest correlations with other sleep domains in this study, as well as in the original validation study.23

Table V. Medical Outcomes Study-Sleep Scale (MOS-SS) scores by self-perceived general health status in patients with overactive bladder (N = 363).

Construct Validity

Domain/General Health Status

Significant associations were found among all sleep domains of the MOS-SS, except for snoring, and the self-reported general health status scores (Table V). As hypothesized, higher scores in sleep disturbance, somnolence, awaken short of breath or with headache, sleep quantity, and the 9-item sleep index were all significantly associated with lower health status, while higher sleep adequacy scores were significantly associated with higher self-rated general health status. Snoring scores were not significantly associated with general health status. In addition, optimal sleep was significantly associated with general health status (Table VI). For the SF-36, strong correlations were found between the physical and mental domains, including the PCS and MCS, and the MOS-SS sleep domains (Table VII). Sleep disturbance, somnolence, as well as the 9-item global index had strong to moderately strong correlations with various SF-36 physical and mental domains. Snoring did not have correlations >0.30 (absolute value) with any of the SF-36 health domains, and several of the correlations were not statistically significant. As expected, sleep adequacy had positive correlations with the SF-36 health domains, while the other sleep domains (disturbance, somnolence, awaken short of breath, snoring, and the 9-item global index) had negative correlations. Parallel results were found in the original validation study, in which Hays and Stewart 23 correlated the MOS-SS sleep domains with 18 other MOS-SS health measures that included pain, physical symptoms, energy/fatigue, physical functioning, role limitations due to physical and emotional health, social activity limitations, cognitive functioning, depression, and anxiety. Similar to this study, snoring was the least correlated with other health measures, while sleep disturbance and the 2 global indices had the strongest correlations with other measures.23 Comparisons were made between the domain scores for the OAB group with nocturia and the domain scores for those without nocturia using regression (Table II). Because persons in this sample with and without nocturia differed in age (Table I), age was included in the regression model so that results con21 26

Disturbance Excellent Very good Good Fair Poor Somnolence Excellent Very good Good Fair Poor Adequacy Excellent Very good Good Fair Poor Awaken SoB Excellent Very good Good Fair Poor Snoring Excellent Very good Good Fair Poor Quantity Excellent Very good Good Fair Poor 9-1tern global Excellent Very good Good Fair Poor

Score, Mean (SD)

F Score (P) 16.4 (<0.001)

18.8 26.5 31.4 42.0 53.0

(1 6.6) (20.0) (22.2) (25.6) (27.4) 13.5 (<0.001)

23.1 (20.3) 26.9 (17.3) 31.1 (21.7) 38.0 (20.2) 53.5 (28.7) 8.1 (<0.001) 64.3 59.1 51.3 45.2 39.4

(25.9) (23.9) (24.0) (26.2) (28.0)

4.1 10.8 13.5 20.5 34.2

(9.1) (19.6) (22.7) (24.8) (33.1)

33.5 36.7 37.3 41.1 49.7

(33.0) (30.1) (32.4) (37.3) (39.6)

10.8 (<0.001)

1.3 (0.25)

4.7 (<0.001) 7.2 (1.0) 6.9 (1.3) 6.9 (1.3) 6.5 (1.3) 6.2 (1.9) 20.8 (<0.001) 21.6 27.8 33.3 41.7 52.5

(13.6) (16.3) (1 7.1 ) (20.5) (22.1)

SoB = short of-breath (or with headache).

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Table Vl. Optimal sleep frequencies (and percentages) by self-perceived general health status in patients with overactive bladder (N = 363).

General Health Status

Optimal Sleep

Sleep Not Optimal

Excellent

36 (20)

13 (7)

Very good Good Fair Poor

59 53 29 8

32 (18) 64 (36) 46 (26) 23 (13)

(32) (29) (16) (4)

Z2 (P) 30.8 (<0.001)

trolled for the effect of age, and the reported means are adjusted for age. OAB respondents with nocturia scored significantly worse on the disturbance, somnolence, awaken short of breath, and quantity domains, as well as on the 9-item global scale. The groups did not differ on adequacy or snoring. Overall, our sample found that people with OAB and nocturia had poorer sleep quality compared with those with OAB without nocturia, lending support to the construct validity of the MOS-SS for persons with OAB. DISCUSSION

Individuals with OAB are known to have low HRQOL scores and are likely to have sleep complaints. The

MOS-SS has successfully undergone previous validation studies in the general population as a measure of sleep quality and quantity. 23,36 However, it had not been evaluated in patients with OAB. Given the lower HRQOL found in the OAB population, it was important to examine the psychometric properties of the MOS-SS in patients with OAB before using the instrument to assess improvement in sleep quality in the clinic or in a clinical trial setting. This study assessed the MOS-SS by analyzing the psychometric properties of the instrument in persons with OAB and comparing them to the properties of the validated instrument in a normative population. As expected, the MOS-SS scores in each domain were significantly poorer for the OAB sample compared with the general population: lower perceived sleep adequacy and higher impairment in sleep disturbance, somnolence, awaken short of breath or with headache, snoring, and global indices were reported by persons with OAB. Construct validity, internal consistency reliability, correlations between domains, floor/ceiling effects, and the factor structure of the MOS-SS were largely consistent between this OAB sample and what had been found in the general population. Construct validity was also supported by different domain scores for OAB people with and without nocturia. The psychometric evaluation conducted in this study therefore suggests that the MOS-SS

Table Vii. Correlations between Medical Outcomes Study-Sleep Scale (MOS-SS) and Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) scores in patients with overactive bladder (N = 363). MOS-SS Domain SF-36Domain Physical function Role function Body pain General health Physical summary Role emotional Social functioning Mental health Vitality Mental summary

Disturbance ~ Somnolence ~ Adequacy ~ AwakenSoBf -0.40§ -0.36§ -0.44§ -0.50§ -0.41§ -0.42§ -0.5211 -0.5311 -0.5411 -0.50§

-0.34§ -0.36§ -0.37§ -0.44§ -0.39§ -0.40§ -0.44§ -0.32§ -0.5611 -0.40§

SoB = short of breath (or with headache). *Pearson correlation coefficients. t Polyserial correlation coefficients. t Biserial correlation coefficients.

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2006

0.25 0.26 0.28 0.40§ 0.25 0.34§ 0.42§ 0.46§ 0.5811 0.49§

-0.29 -0.32§ -0.44§ -0.42§ -0.35§ -0.35§ -0.49§ -0.38§ -0.45§ -0.40§

Snoringf

Optimalt

9-1temGIobaD

-0.16 -0.12 -0.13 -0.15 -0.18 -0.05~ -0.09~ -0.08~ -0.19 -0.05~

0.27 0.29 0.26 0.35§ 0.26 0.33§ 0.37§ 0.34§ 0.30§ 0.32§

-0.42§ -0.40§ -0.48§ -0.5611 -0.44§ -0.48§ -0.5911 -0.57 II -0.6811 -0.5911

§r = 0.30-0.50 (absolute value). IIr = >0.50 (absolute value). '~P = NS.

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is an appropriate patient-assessment tool and produces valid and reliable results for assessing sleep-related quality of life among persons with OAB diagnosis. Cronbach's 0~ coefficients and correlation coefficients demonstrated acceptable levels of internal consistency reliability and domain-to-domain correlations for persons with OAB. Convergent validity of the different MOS-SS domains to the self-perceived general health status and the SF-36 health domains was consistent for persons with OAB and the general population, but overall there was greater convergent validity of the MOS-SS for persons with OAB, especially between the MOS-SS sleep domains and the mental health domains of the SF-36. Discrepant correlations were also observed among the MOS-SS sleep domains and the SF-36 physical health domains, where the sleep domains were strongly correlated to the physical domains for persons with OAB but not in the general population. For the factor structure, the CFA results for persons with OAB followed the pattern proposed in the original validation of the MOS-SS. The factor patterns were consistent with expectations; there were clearly individual domains of disturbance, somnolence, and adequacy domains; optimal sleep was related to the adequacy domain; and awaken short of breath and snoring were both related to a respiratory domain but not strongly enough to consider it its own factor. Because the results of the CFA were in line with what was found in the original validation study, it can be concluded that the MOS-SS has a valid factor structure in this OAB sample. The snoring domain was the least correlated with other MOS-SS sleep domains and had the lowest convergent validity with any of the SF-36 health domains for persons with OAB. These findings are consistent with results of the original validation work, suggesting that snoring by itself is not a strong measure of sleep quality. 23 Sleep disturbance, somnolence, and perceived adequacy, all of which are multi-item domains, have mostly similar factor loadings as the original MOS-SS domains, strong correlations between domains, and high concurrent validation with self-perceived general health status similar for patients with OAB. These findings suggest that the 3 domains can be interpreted as the core constructs of sleep quality. The awaken short of breath domain had slightly weaker properties than sleep disturbance, somnolence, and perceived adequacy, with more moderate 21 28

correlation with other MOS-SS sleep domains, and fairly good convergent validity with general health status and SF-36 physical health domains among persons with OAB. Awaken short of breath is a common symptom of sleep apnea occurring in a small percentage of the population, and the score distribution of this domain is highly skewed as expected (thus the observed high floor effect). Given the cross-sectional design of this study, additional analyses, such as test-retest reliability and responsiveness of the MOS-SS scores over time could not be assessed, therefore limiting the implications of this study. Previous studies have found that general H R Q O L instruments, including the SF-36, have demonstrated differences in OAB from other diseases and conditions but have not been as responsive to changes within a clinical trial as disease-specific measures for OAB. 2° Therefore, future studies are needed to investigate the responsiveness and sensitivity of the MOS-SS for persons with OAB when longitudinal data become available. CONCLUSIONS

Because OAB may have a negative association with sleep-related quality of life among persons who are experiencing nocturia, assessing the multidimensional aspect of sleep as perceived by the patient is important using a valid and reliable instrument. The psychometric evaluation conducted in this study supports the use of the MOS-SS as an appropriate instrument for assessing key constructs of sleep among persons with OAB. ACKNOWLEDGM ENTS

The authors would like to thank Mary Jarzebowski at the Buehler Center on Aging, Northwestern University's Feinberg School of Medicine, for her research assistance, and Thomas N. Taylor, PhD, Worldwide Outcomes Research, Pfizer Global Pharmaceuticals, for his helpful comments. This study began while Dr. Lau was on a Pfizer Postdoctoral Research Fellowship at the University of Michigan, School of Public Health, Ann Arbor, Michigan. He is currently an Assistant Professor at the Buehler Center on Aging, Northwestern University's Feinberg School of Medicine, Chicago, Illinois. REFERENCES 1. Abrams P, Cardozo L, Fall M, et al, for the Standardisation Sub-committee o f the International Continence Society.

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The standardisation of terminology of lower urinary tract function: Report from the Standardisation Sub-committee of the International Continence Society. Neurourol Urodyn. 2002;21:167-178. Stewart WF, Van Rooyen J B, Cundiff GW, et al. Prevalence and burden of overactive bladder in the United States. World J Urol. 2003;20:327336. Thom D. Overactive bladder: Epidemiology and impact on quality of life. Patient Care. 2000;(Winter Suppl): 6-14. Hu TVV, WagnerTH. Economic considerations in overactive bladder. Amy Manag Care. 2000;6(Suppl 11 ): $591 -$598. Jackson S. The patient with an overactive bladder-symptoms and qualityof-life issues. Urology. 1997;50(Suppl 6A):18-22, discussion 23-24. Wein AJ, Rovner ES. The overactive bladder: An overview for primary care health providers. IntJ Fertil Womens Med. 1999;44:56-66. Abrams P, Kelleher CJ, Kerr LA, Rogers RG. Overactive bladder significantly affects quality of life. AmJ Manag Care. 2000;6(Suppl 11 ):$580$590. Wein AJ. Overactive bladder: Defining the disease. Am J Manag Care. 2000;6(Suppl 11):$559-$564, discussion S607-S619. Donovan JL. Measuring the impact of nocturia on quality of life. BJU Int. 1999;84(Suppl 1 ):21-25. Manocchia M, Keller S, Ware JE. Sleep problems, health-related quality of life, work functioning and health care utilization among the chronically ill. Qual Life Res. 2001 ;10:331345. Grimby A, Svanborg A. Morbidity and health-related quality of life among ambulant elderly citizens. Aging (Milano). 1997;9:356-364. Liberman JN, Hunt TL, Stewart WF, et al. Health-related quality of life among adults with symptoms of overactive bladder: Results from a

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US community-based survey. Urology. 2001 ;57:1044-1050. Norton PA, MacDonald LD, Sedgwick PM, Stanton SL. Distress and delay associated with urinary incontinence, frequency, and urgency in women. BMJ. 1988;297:1187-1189. WymanJF, FantlJA, McClish DK, et al. Quality of life following bladder training in older women with urinary incontinence. Int Urogynecol J Pelvic Floor Dysfunct. 1997;8:223229. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30:473-483. Bergner M, Bobbitt IRA, Carter WB, et al. The Sickness Impact Profile: Development and final revision of a health status measure. Med Care. 1981 ;19:787-805. Kelleher CJ, Cardozo LD, KhullarV, Salvatore S. A new questionnaire to assess the quality of life of urinary incontinent women. Br J Obstet Gynaeco[. 1997; 104:1374-1379. Uebersax JS, Wyman if, Shumaker SA, et al. Short forms to assess life quality and symptom distress for urinary incontinence in women: The Incontinence Impact Questionnaire and the Urogenital Distress Inventory. Continence Program for Women Research Group. Neurouro[ Urodyn. 1995;14:131-139. Wagner TH, Patrick DL, Bavendam TG, et al. Quality of life of persons with urinary incontinence: Development of a new measure. Urology. 1996;47:67-71. Kobelt G, Kirchberger I, Malone-LeeJ. Review. Quality-of-life aspects of the overactive bladder and the effect of treatment with tolterodine. BJU Int. 1999;83:583-590. Reese PR, Pleil AM, Okano GJ, Kelleher CJ. Multinational study of reliability and validity of the King's Health Questionnaire in patients with overactive bladder. Qual Life Res. 2003;12:427-442.

22. Shumaker SA, Wyman JF, Uebersax JS, et al, for the Continence Program in Women (CPW) Research Group. Health-related quality of life measures for women with urinary incontinence: The Incontinence Impact Questionnaire and the Urogenital Distress Inventory. Qual Life Res. 1994;3:291-306. 23. Hays RD, Stewart AL. Sleep measures. In: Stewart AL, Ware JE, eds. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press; 1992:235-259. 24. Marquis P. Sleep disturbance. A component of health status. PharmacoEconomics. 1996;10(Suppl 1 ):25-28. 25. Leidy NK, Revicki D, Geneste B. Recommendations for evaluating the validity of quality of life claims for labeling and promotion. Value Health. 1999;2:113-127. 26. Fayers P, Hays R. Assessing Quafity of Life in Clinical Trials: Methods and Practice. New York, NY: Oxford University Press; 2005. 27. Coyne K, Revicki D, Hunt T, et al. Psychometric validation of an overactive bladder symptom and healthrelated quality of life questionnaire: The OAB-q. Qua[ Life Res. 2002;11 : 563-574. 28. Essink-Bot ML, Krabbe PF, Bonsel GJ, Aaronson NK. An empirical comparison of four generic health status measures. The Nottingham Health Profile, the Medical Outcomes Study 36-item Short-Form Health Survey, the C O O P / W O N C A charts, and the EuroQol instrument. Med Care. 1997;35:522-537. 29. WareJE, Kosinski M, Keller S. SF-36 Physical and Mental Summary Scales: A User's Manual. Boston, Mass: The Health Institute, New England Medical Center; 1994. 30. Bollen K. Structural Equations with Latent Variables. New York, NY: John Wiley &Sons, Inc; 1989. 31. Bender PM. Comparative fit indexes in structural models. PsycholBull. 1990; 107:238-246. 2129

Clinical Therapeutics 32. Bender PM, Bonnett DG. Significance tests and goodness-of-fit in the analysis of covariance structures. Psyckol Bull. 1980;88:588606. 33. Hu L, Bender PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol Methods. 1998;3:424-453. 34. Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to Their Development and Use. 2nd ed.

New York, NY: Oxford University Press; 1995. 35. Cohen J. Statistical Power Analyses for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988. 36. Hays RD, Martin SA, Sesti AM, Spritzer KL. Psychometric properties of the Medical Outcomes Study Sleep measure. Sleep Med. 2005;6: 41-44. (con[inuecl on next page)

Address c o r r e s p o n d e n c e to: D e n y s T. Lau, PhD, H e a l t h Services Evaluation and Policy Research, The Buehler Center on Aging, Feinberg School of Medicine, N o r t h w e s t e r n University, 750 N o r t h Lake Shore Drive, Suite 601, Chicago, IL 60611. E-mail: D - L a u @ N o r t h w e s t e r n . e d u 2130

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Appendix I. Sample characteristics by any missing data in Medical Outcomes Study-Sleep Scale domains. Characteristic

No. of Patients

No Missing Data, No. (%)

Continence Continent Incontinent

230 168

210 (91) 153 (91)

20 (9) 15 (9)

Nocturia Without nocturia With nocturia

192 206

172 (90) 191 (93)

20 (10) 15 (7)

55-<65 _>65

86 79 83 150

84 71 77 131

2 8 6 19

Sex Female Male

210 188

192 (91) 171 (91)

18 (9) 17 (9)

Race White Black Other*

336 35 27

308 (92) 32 (91) 23 (85)

28 (8) 3 (9) 4 (15)

Education Less than high school High school Some college College degree

52 160 91 95

46 144 85 88

(88) (90) (93) (93)

6 16 6 7

(12) (10) (7) (7)

Location of US Residence Northeast Midwest West South

60 89 101 148

53 80 92 138

(88) (90) (91) (93)

7 9 9 10

(12) (10) (9) (7)

General health Excellent Very good Good Fair Poor

56 97 129 80 36

49 91 117 75 31

(88) (94) (91) (94) (86)

7 (13) 6 (6) 12 (9) 5 (6) .5 (14)

Total

393

363 (91)

Age group, y <45 45-<55

Missing Data, No. (%)

Z 2 (P) 0.01 (0.94)

1.2 (0.27)

7.7 (0.0S) (98) (90) (93) (87)

(2) (10) (7) (13)

0.3 (0.87)

1.3 (0.82)

1.6 (0.67)

1.6 (0.66)

3.6 (0.46)

3.5 (9)

*Included Native American, Asian, and Hispanic. (continued on next page)

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Appendix II. No. (%) of patients with overactive bladder at floor and ceiling (N = 363). At Floor 1.3

At Ceiling 2.9

Disturbance Somnolence Adequacy

21 (6) 18 (5) 18 (5)

4 (1) 5 (1) 9 (2)

Awaken SoB Snoring 9-Item global

224 (62) 97 (27) 1 (0)

4 (1) 39 (11) 1 (0)

MOS-SS Domain

MOS-SS = Medical Outcomes Study-Sleep Scale; SoB = short of breath (or with headache).

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