Quality of life in sleep disorders

Quality of life in sleep disorders

Sleep Medicine Reviews, Vol. 7, No. 4, pp 335–349, 2003 SLEEP MEDICINE doi:10.1053/smrv.2001.0220 reviews CLINICAL REVIEW Quality of life in slee...

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Sleep Medicine Reviews, Vol. 7, No. 4, pp 335–349, 2003

SLEEP MEDICINE

doi:10.1053/smrv.2001.0220

reviews

CLINICAL REVIEW

Quality of life in sleep disorders Marlene A. Reimer and W. Ward Flemons University of Calgary, Calgary AB, Canada T2N 1N4 KEYWORDS quality of life, sleep disorders

Summary Quality of life is a major outcome variable in choosing and evaluating treatment alternatives for sleep disorders. However, the number of well validated and sufficiently responsive quality of life measures for use with this population is limited. The SF-36, Nottingham Health Profile (NHP) and Sickness Impact Profile (SIP) are the most frequently used generic measures. The Functional Outcomes of Sleep Questionnaire (FOSQ) and Sleep Apnoea Quality of Life Index (SAQLI) are useful as condition/disease specific measures. However, there are not yet specific measures in common use for other sleep disorders. Results across the sleep disorders that have been studied, primarily sleep apnea, narcolepsy, restless legs and insomnia, have consistently shown poorer quality of life than population norms prior to treatment, particularly in those dimensions related to sleep, energy and fatigue. Before treatment scores typically are of similar magnitude to those found among individuals with other chronic diseases such as hypertension and chronic obstructive pulmonary disease. With treatment quality of life scores may or may not improve to the level of population norms, suggesting that currently available treatments may not fully reverse the effects of the common sleep disorders.  2003 Published by Elsevier Science Ltd

INTRODUCTION The quality of sleep is intrinsically linked to quality of life. To patients, the impact of sleep related symptoms on quality of life is usually the reason for seeking and adhering to treatment. To clinicians, the apparent impact of a sleep disorder on the patient and partner is part of decision making in selection and evaluation of the treatment plan. To researchers, quality of life measures are often a primary outcome variable. Changes in physiological parameters are known to correlate weakly with

Correspondence to be addressed to: Marlene Reimer, Professor, Faculty of Nursing, University of Calgary, Calgary AB, Canada T2N 1N4. Tel: (403) 220-5839; Fax: (403) 2844803; E-mail: [email protected]

patient and partner-oriented outcomes of symptom reduction and quality of life [1–3]. The number of instruments available for measuring quality of life in sleep disorders is limited. Two types will be considered: generic and disease/ condition specific. Guidelines for choosing quality of life measures or evaluating their use in sleep disorders research will be discussed followed by a review on current evidence as to the impact of sleep and sleep disorders on quality of life.

DEFINITION Quality of life can be defined as the overall state of well-being that individuals experience as assessed by subjective and objective measures of functioning, health, and satisfaction with the important dimensions of their lives [4]. Most measures and most

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Table 1 Quality of life measures commonly used in sleep disorders research and clinical practice Title

Type

Items

SF-36

Generic

36

Sleep Time Frame Included 4 wks (1 wk)

NHP

Generic

38

X

At the moment X

SIP EQ-5D

Generic Utility

136 5

X

Today Today

HUI2/HUI3 FOSQ

Utility Condition Specific Condition Specific

40 30

X

Varies General

X

X X

Functional emphasis

35+5

X

Last 4 wks

X

X

Shortform SAQLI

SAQLI

studies focus on health related quality of life which is limited to that which is affected by disease, trauma, or treatment thereof. The dimensions considered relevant to health related quality of life are physical function, social function, emotional or mental state, burden of symptoms, and sense of wellbeing. However, other quality of life dimensions such economic wellbeing and leisure can be profoundly impacted by sleep disorders. Thus it is important to consider this broader perspective in choosing measures and evaluating the impact of sleep disorders on quality of life.

GENERIC MEASURES Generic quality of life measures are those which have been designed for use across populations. Their advantages are that they permit cross-disease comparisons and facilitate meta-analysis. Their disadvantages are that they tend to lack sensitivity to specific treatment outcomes. Most generic measures do not include sleep as a specific dimension. Some, like the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36] [5] include a related dimension (e.g. Vitality) but do not ask directly about sleep. The older Nottingham Health Profile (NHP) [6] and the Sickness Impact Profile (SIP) [7] are among the few generic measures with major sections on sleep and rest (Table 1). All of the measures presented in Table 1 have acceptable psychometric qualities. For specific information the

Profile Scores X

X

Total Score

Not recom. X X

Comments Also 12 & 8 item versions Two parts

Not sensitive to sleep or mild illness

reader is encouraged to seek out the resources listed in Table 2. In the following discussion on specific generic measures, domains which relate to sleep have been italicized for the reader’s convenience.

SF-36, SF-12 and SF-8 The short form measures developed out of the Medical Outcomes Study [8] are the most commonly used of the generic measures. All versions were designed to evaluate health related quality of life and include eight dimensions: physical functioning, role functioning – physical, role functioning – emotional, mental health, social functioning, bodily pain, vitality, and general health. A further item on the SF-36 asks about health compared to one year ago. The SF-36 at 36 items is the most comprehensive of the currently used versions. Each dimension is scored on the basis of 2 to 10 items according to scoring rules available from the distributor [9]. An alternate scoring protocol by the Rand Group is less frequently used [10]. Each dimension is reported on a scale of 0 to 100 with the higher score reflecting better quality of life. There is no total score. However summary measures for a physical health component and a mental health component can be calculated. The SF-36 has undergone extensive psychometric testing, initially in the United States [11, 12] and subsequently in more than 40 countries

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Table 2 Information sources for selected quality of life measures useful in sleep disorders Title

Key publications

SF-36,

Ware JE, Sherbourne CD. The MOS 36-Item Short-Form Health Survey (SF-36). 1. Conceptual framework and item selection. Med Care 1992; 30: 473–481.

SF-12,

Ware JE, Kosinski M, Keller SD. A 12-item short-form health survey. Med Care 1996; 34(3): 220–233.

NHP

Hunt SM, McEwen J, McKenna SP. Measuring health status: A new tool for clinicians and epidemiologists. J RColl Gen Pract 1985; 35: 185–188.

Dr. Stephen McKenna Galen Research, Enterprise House, Manchester Science Park, Lloyd St N, Manchester M156SE, UK Email [email protected] compuserve.com

SIP

Bergner M, Bobbitt RA, Pollard WE, Martin DP, Gilson BS. The Sickness Impact Profile: Validation of a health status measure. Med Care 1981; 19: 787–805.

Medical Outcomes Trust PMB #503 198 Tremont St Boston, MA 02116-4705 Tel: 617 426-4046 Fax: 617 587-4232 www.oucomes-trust.org

EQ-5D

The Euroqol Group. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy 1990; 16: 199–208. Brazier J, Jones N, Kind P. Testing the validity of the Euroqol and comparing it with the SF-36 health survey questionnaire. Quality of Life Research 1993; 2: 169–180.

Frank de Charro, EuroQol Business Manager, PO Box 4443, 3006 AK Rotterdam, The Netherlands Tel: +31 10 408 1545 Fax: +31 10 452 5303 Email: [email protected] www.eur.nl/bmg/imta/eq-net/

HUI2/ HUI3

Torrance GW, Feeny DH, Furlong WJ, Bar RD, Zhang Y, Wang Q. Multi-attribute preference functions for a comprehensive health status classification system: Health Utilities Index Mark 2. Med Care 1996; 24: 702–722.

William Furlong, Health Utilities Inc., 88 Sydenham St. Dundas ON Canada L9H 2V3 Tel: 905 525-9140 ext 22389 Fax: 905 627-7914 Email: [email protected]

FOSQ

Weaver TE, Laizner AM, Evans LK, Maislin G, Chugh DK, Lyon K, Smith PL, Schwartz AR et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997; 20: 835–843.

Terri E.Weaver University of Pennsylvania School of Nursing, Nursing Education Building, Philadelphia PA 19104-5096

SAQLI

Flemons WW, Reimer MA. Development of a diseasespecific health-related quality of life questionnaire for sleep apnoea. Am J Respir Crit Care Med 1998; 158: 494–503.

W.W.Flemons/M.A. Reimer University of Calgary Calgary AB Canada T2N 4N1 Tel: 403 (403) 220-8583 Fax: (403) 283-4730 Calgary AB, T2N 4N1 Email: [email protected] [email protected]

[13]. Norms are available for general and diseasespecific populations in the United States and elsewhere. Several combination scales have emerged where the SF-36 is used in conjunction with a disease-specific measure thus maximizing the strengths of both in terms of comparability and sensitivity.

Contact for further information and permission Quality Metric Inc. 640 George Washington Hwy, Lincoln RI 0288865 Tel: 402 334-8801 www.qmetric.com

In spite of the wide use of the SF-36 it does have some limitations, most notably floor and ceiling effects [14]. Data analysis procedures vary also in attempts to deal with skewed distribution and variations in types of response choices. Physical and mental health components are distinguished in the wording of many of the specific items (e.g. During

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the past four weeks, have you had any of the following problems . . . as a result of your physical health). Likewise the two summary scores dichotomize between physical and mental health. Interestingly however, vitality, the domain most responsive to sleep disorders, is entered as part of the Mental Health Summary Measure but correlates significantly with both Mental and Physical Health [13]. The availability of the SF-12 [15] and now the SF-8 [16] reflect continued efforts to reduce respondent burden, particularly when quality of life is one of multiple measures to be included in major trials.

The Nottingham Health Profile (NHP) The NHP is another generic health related quality of life measure, widely used in Europe [10]. It was designed to reflect the perceived effects of ill-health on everyday life from a lay, rather than health professional perspective [6]. Part 1 includes 38 yes/ no items in 6 domains: physical abilities, pain, sleep, social isolation, emotional reactions, and energy level. Part 2 includes 7 aspects of life affected by health: occupation, ability to do jobs around the house, social life, home relationships, sexual life, hobbies and holidays. The original scoring is weighted within each domain using the Thurstone method in which paired comparisons were rated by a general population for perceived severity. According to the developers only profile scores (i.e. representing each domain) should be reported. However, some reports are based on other simplified scoring systems and may include a total score [10]. Thus clinicians and researchers need to be alert to which scoring system was used in a particular report and whether results include the optional Part 2 component. The strengths of the NHP lie in its use of common language (albeit with a British twist e.g. “walk about”) and ease of completion (yes/ no). It has been translated into most European languages plus Arabic and others. Some norms are available. Given its basic intent of detecting impact of ill-health on everyday life it is less useful than other generic measures in health population surveys because of a ceiling effect. However, it is noted that sleep and energy are among the most frequently detected problematic domains in such surveys [10].

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The Sickness Impact Profile (SIP) The SIP is the longest of the commonly used generic measures, having 136 items distributed across 12 categories: sleep and rest, emotional behavior, body care and movement, home management, mobility, social interaction, ambulation, alertness behavior, communication, work, recreation and pastimes, and eating. It yields a total score, physical and psychosocial dimension scores, and individual category scores [7]. Generated through combined input from professionals and lay people, the SIP targets changes in behavior that respondents attribute to their health and describe them on that particular day. In spite of its length most respondents can complete it in 15–20 min. The SIP is more comprehensive than most of other generic measures, including domains such as work and leisure. From the first author’s experience it works well for populations experiencing disability (e.g. with items such as “I am very clumsy in body movements”) but somewhat lacks face validity for those who define themselves as well, including those with sleep disorders. The Sleep and Rest category gets at general sleepiness and fatigue as may be experienced with illness rather than more specific sleep disorder related symptoms.

UTILITY MEASURES The utility measures are designed specifically for economic comparisons, based on a utilitarian philosophy supportive to decision-making for the greatest good for the greatest number [17]. These measures have been developed to provide a preference-based scoring formula based on broad population-based ratings of preferred alternatives for health states. These alternatives may be based on time tradeoffs (i.e. how many years of life in a current health state would people trade off in exchange for perfect health) or a standard gamble approach (i.e. respondents balance a particular health state against probability of full health [rated as 1] versus death [rated as 0]). In the latter, the resulting utility is a number on a scale between 1 and 0 that represent the aggregated preference for a particular health state. This number can be then converted to quality adjusted life years (QALYs) to facilitate comparisons across populations and conditions. The utility measures are useful at policy making levels but have generated considerable debate. One

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major issue is whose views should be considered. (e.g. people with the disorder or people without the disorder who are therefore thought to offer unbiased judgements based on standardized presentations of the disease consequences). The Tousignant group asked 19 OSA patients on treatment with CPAP to rate their preference for that state compared to pre-treatment, full health or immediate death [18]. The mean utility for treatment was 0.87±0.17 compared to 0.63±0.29 pretreatment. The difference in these means was combined with the life expectancies of each patient yielding an average gain of 5.4 QALYs. By then figuring in the treatment costs the cost-utility ratio suggested that it cost between $3397 and $9792 (Canadian) per QALY added.

EQ-5D The EQ-5D, developed by the EuroQol Group, is a generic, single index utility measure often selected for its brevity [19, 20]. Health is measured on five dimensions: mobility, self-care, usual activity, pain/ discomfort and anxiety/depression. Each dimension is rated by one item with three levels (no problems, some problems, inability or extreme problems). None of the items relate directly to sleep. On a second page respondents are asked to rate their current health status on a thermometer, like a visual analogue scale from best imaginable (100) to worst imaginable health state (0). This self-rating of overall health state can be compared with utility scores derived from population valuations based on the combination of possible health states yielded by different combinations of responses to the five core items. Ultimately the results can be used to calculate QALYs. The brevity of the EQ-5D is its major limitation. It has not been shown to be useful in detecting the impact of sleep disorders or responsiveness to their treatment and is not recommended for use with this population [21].

Health utilities index A more comprehensive set of utility measures have been developed by the Feeny, Furlong and Torrance group [17]. The Health Utilities Index Mark 2 (HUI2) includes items covering seven health attributes: sensation (vision, hearing, speech), mobility, emotion, cognition, self-care, pain, and fertility [22]. A more recent complementary version, the HUI3

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addresses eight attributes: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. These measures are available in a variety of forms to accommodate proxies and different time periods (e.g. one week, two weeks, four weeks, usual health) from the distributors but are under copyright. The fees are such that any investigator considering their use should inquire early in budget planning as to their cost.

DISEASE/CONDITION SPECIFIC MEASURES FOR SLEEP DISORDERS Most of the specific measures in the sleep field have been developed for the disorders of excessive sleepiness. Most validation studies of the diseasespecific measures have relied in part on establishing correlations with generic measures. While there is no gold standard among generic measures the SF36, NHP, and SIP have been most commonly used, thus adding to our understanding of how specific sleep disorders perform on generic measures as well as the disease-specific ones.

Ferrans and Powers Ferrans and Powers developed one of the first sleep disorder specific quality of life measures by adapting their generic measure [23] for use in narcolepsy [24]. Their measures are among the minority that weigh satisfaction on each item by its importance to the respondent. They consist of four domains: health and functioning, socioeconomic, psychological/spiritual and family. In spite of its early appearance the Ferrans and Powers Quality of Life Index: Narcolepsy version has not been widely adopted.

Functional Outcomes of Sleep Questionnaire (FOSQ) The Functional Outcomes of Sleep Questionnaire (FOSQ) is, as its name implies, a functional measure designed to detect the impact of disorders of excessive sleepiness on physical, mental and social functioning in everyday activities [25]. As such it is not as comprehensive as other health related quality of life measures that also include burden of symptoms and overall well-being. The 30 items represent five subscales: activity level,

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vigilance, intimacy and sexual relationships, general productivity, and social outcome. The stem asks re difficulty in performing the identified activity because of being sleepy or tired. Four levels of response are possible: no difficulty, a little, moderate, or extreme. One of the strengths of this measure is the inclusion for each item of an optional “did not engage in” for reasons other than sleepiness. The scoring protocol, available from the developer, incorporates the possibility of such responses yielding a mean for each subscale and total score based on the items in which the respondent did participate. The intimacy/ sexual activity subscale is a unique feature of the FOSQ. As such it is a strength in terms of addressing an important but often neglected life experience often impacted by sleep disorders. However it is also its limitation as found in a recent survey of 184 subjects over the age of 65 years, only 30.4% of whom completed that section [26]. The authors raise the question as to whether non-completion was associated with lack of sexual activity or reluctance to answer the questions. The FOSQ is gaining increasing popularity often being used in conjunction with the SF-36 [27]. Successful testing of a Spanish version of the FOSQ has been reported [28].

Sleep Apnoea Quality of Life Index (SAQLI) The Calgary SAQLI was designed as a comprehensive health related quality of life measure for use in clinical trials with patients experiencing sleep apnoea [29]. As such it was based on broad-based input from sleep apnea patients and their partners as well as expert clinicians and the research literature. Of the three potential purposes for quality of life measures: discrimination, evaluation, or prediction [30] it was developed as an evaluative measure and thus emphasis was placed on responsiveness to change. The first 35 questions measure four domains: daily functioning, social interactions, emotional functioning, and symptoms. A fifth domain on treatment-related symptoms is a unique feature, capturing the potentially negative quality of life impact of treatment side-effects. The original SAQLI facilitated respondent choice in the symptom and treatment related symptom domains by asking them to select and rate the five most important to them. In spite of the potential complexity of this process

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it has been successfully used in self-completion format. A new Short SAQLI, in a self-completion format, is currently being validated by the authors. The Short SAQLI was derived from data on the original SAQLI administered before and following a four week trial of CPAP. Items were selected on the basis of their responsiveness, repeatability, readability, and representativeness of each of the first three domains. For ease of self-completion the fourth domain, Symptoms, was changed to reflect those most commonly selected in the original SAQLI. Because of the range of treatment symptoms experienced by individuals and across different treatment modalities respondents are asked for the fifth domain to enter and rate up to three troubling symptoms they experienced from treatment. As with the original SAQLI a final question was added that allows treatment related symptoms to be weighted by the respondent, relative to the improvements in quality of life recorded by the first 14 questions. Thus the Short SAQLI retains the unique feature of balancing quality of life benefits against treatment induced symptoms. Preliminary results suggest that it has similar measurement qualities as the SAQLI.

CHOOSING MEASURES WHEN PATIENTS HAVE A SLEEP DISORDER Issues to be considered in using quality of life measures for clinical and research applications have been documented in a number of articles [3, 4, 31, 32]. The clinician or researcher has to consider not only basic psychometric evidence (e.g. reliability, validity) but also the suitability of measures to the particular population. In a widely cited 1994 article, Gill and Feinstein pointed out the weaknesses in much of the quality of life research up to that point [32]. Unfortunately their criticisms are still relevant. Based on 75 randomly selected articles said to describe or employ quality of life measures and published between 1987 and 1991 they found that only 15% conceptually defined quality of life and only 36% provided rationale for selecting the particular measure(s). They found that overall quality of life was not distinguished from health related quality of life in any of the articles and that only 8.5% had provision for patients to rate the importance of

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items. They concluded “because quality of life is a uniquely personal perception, denoting the way that individual patients feel about their health status and/ or nonmedical aspects of their lives, most measurements of quality of life in the medical literature seem to aim at the wrong target” (p. 619). Other common problems, especially in earlier quality of life in sleep disorders literature, have been the use of investigator developed unvalidated measures and/or the use of measures that capture only some domains of quality of life (e.g. mood or function). More recently the trend has been to rely on generic measures but without sufficient attention to responsiveness, ceiling and floor effects that may occur with particular sub-populations. Many persons with sleep disorders are otherwise quite healthy and thus quality of life measures developed for more disabled populations may not allow for enough variability for people experiencing generally good quality of life. On the other hand those generic measures that do include a domain related to sleep or fatigue may bottom out without adequately capturing the profound impact of excessive daytime sleepiness. Choice of quality of life measures should be informed also by intended purpose. Discriminative instruments are used to measure differences among patients at a given point in time [33]. For example, the authors are currently developing a discriminative measure of sleepiness for which items have been selected according to their ability to differentiate between mild, moderate, and severe chronic sleepiness. Evaluative measures are designed to detect change over time. For example, in developing the Short SAQLI, item mean within-subject change scores after four weeks of CPAP therapy were used as a guide in deciding which items could be eliminated. Clinicians and researchers choosing a quality of life measure for use with sleep disorders patients need also consider the impact of those disorders on ability to complete them. Patients experiencing excessive sleepiness may have difficulty concentrating or following complex instructions.

IMPACT OF SLEEP AND SLEEP DISORDERS ON QUALITY OF LIFE The general impact of sleep on the quality of life of healthy individuals has received little attention. This

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observation is not surprising given that most research on what is called health related quality of life is actually about ill health. Sleep quality and quantity and related states such as energy levels are sensitive indicators of many types of ill health, hence their inclusion in at least some of the generic scales. Very few quality of life scales include positive health. As noted previously, ceiling effects are a common problem in using these measures in healthy populations under normal everyday conditions (i.e. without induced sleep deprivation, shift work or other circadian challenges). In one study done to explore the reciprocal associations between daily sleep characteristics and self-reports of pre and post sense of well-being, 30 employed adults rated current mood, symptoms, and social interaction experience every two waking hours for 14 consecutive days using a pocket computer [34]. Sleep diaries were also maintained throughout the two week period. Significant associations were found between earlier time of sleep onset and next day cheerfulness, alertness, and satisfaction with time spent alone. Shorter sleep latency was also associated with next day cheerfulness. The only previous day variable that was predictive of sleep quality in this study was physical symptoms, which were associated with longer latency, later onset, and more awakenings. The strength of the evidence from this study is limited however by the lack of psychometric information on the measures used and the need for replication. While not a replication it is of note that sleep quality rather than quantity was also associated with better quality of life in a population-based study of 273 adults, aged 40–64, using the Quality of WellBeing Scale [35]. One of the early studies on the relationship between sleepiness and general health status was that conducted by Briones and others in which they examined the degree to which excessive daytime sleepiness (EDS), as verified using the Epworth Sleepiness Scale (ESS) and Multiple Sleep Latency Test (MSLT), affected each of the SF-36 subscales [36]. The 129 adults with mild to moderate sleep apnea or snoring, all of whom had a respiratory disturbance index (RDI) <30, included 40 individuals with an Epworth Sleepiness Scale (ESS) score >12. Overall these sleepier patients scored lower on all SF-36 subscales but with wide variability. Significant differences were detected in general health perceptions and vitality for which mean scores were

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10 or more points lower on the 100 point scale. The authors concluded that sleepiness affects perceptions of quality of life. The much larger Sleep Heart Health Study has provided important evidence that supports and expands these findings [37]. Ninety per cent (n= 5816) of the participants in this national population based US study have had an in-home overnight polysomnogram and completed the SF-36, ESS and a Sleep Habits Questionnaire that elicits symptoms of EDS, difficulty initiating and maintaining sleep (DIMS), and demographic characteristics. All subjects were over 40 years of age (mean 63 years ±11). Those subjects with EDS, defined as ESS [11, had significantly poorer quality of life in all sub-scales analyzed. The authors note that Role Emotional had insufficient variance to be dichotomized at the 25th percentile as was done with all other sub-scales. Age, gender, marital status, BMI, educational level, use of pills to sleep, cardiovascular and respiratory conditions were controlled in this and all other analyses. Thus even though the Sleep Heart Health group took a more conservative cut point on the ESS than Briones and others they both found that EDS significantly impacted all domains of quality of life measured. The latter team also found a significant gender interaction between ESS and the Mental Health sub-scale. Males with ESS scores of [11 were 1.83 times more likely than males below that score to have poorer Mental Health scores whereas for women it was 1.31 times more likely. In the Sleep Heart Health Study subjects with severe sleep disordered breathing (SDB), defined as RDI [30, had significantly poorer scores on Vitality, General Health Perceptions, Physical Functioning and Social Functioning but with wide variability. Mean differences from the General USA population ranged from 12.61 for Physical Functioning to 4.3 for General Health on the 100 point scale. Those with mild (RDI 5 to <15) or moderate (RDI 15 to 30) had significantly poorer quality of life on the Vitality scale only [37]. Subjects categorized as having DIMS, based on answering “Almost Always” or “Frequently” with respect to trouble falling asleep, staying asleep, or early morning wakening with inability to return to sleep, had poorer quality of life on all subscales analyzed. Most mean scores were in a similar range to those of subjects with SDB except for Physical Functioning where the mean difference

from population norms was only 9.56 points. The odds of having a poor Mental Health or Vitality score were twice as high for subjects with DIMS. Women were significantly more likely to report DIMS than men. In summary, there is strong evidence to indicate that individuals with a range of sleep problems characterized by EDS, DIMS, or an RDI [5 have poorer quality of life in one or more domains than the average population. Clinicians however are well aware that sleep problems affect patients and their partners. Research evidence that supports these clinical observations is beginning to accumulate but has been limited by lack of use of standardized measures and the common assumption of improvement in quality of life with treatment rather than incorporating items which get at the potential for negative impact. Kiely and McNicholas used a 9 item questionnaire with bedpartners of patients who had been on CPAP treatment for 2 to 12 months [38]. They found that bedpartners reported improved quality of life for themselves and even greater improvement in quality of life for the affected partner. The investigators acknowledged the potential for a negative impact from the obstrusiveness and noise of CPAP but did not ask about any negative consequences to treatment. In a large Swedish case-control study women aged 30 to 64 years living with a heavy snorer (as defined by them) were significantly more likely to report symptoms of insomnia, daytime sleepiness, fatigue and morning headache than women living with nonsnorers [39]. More work is needed in this area with more careful controls on the characteristics, relationships and home environments of the partners. A Sleep Apnea Partners Quality of Life Index is under development by the second author. Preliminary results suggest that partners do not get as much of an improvement in their quality of life on average as patients. Interestingly there seems to be little correlation between improvements in the patient’s quality of life and the partner’s quality of life. As found in other studies there is a low correlation between RDI and patient’s quality of life as measured by either the SAQLI or SF-36.

Sleep apnea The studies discussed in this section are those in which a specific diagnosis of sleep apnea had been

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made. In the preceding section most of the subjects with EDS and/or RDI [5 probably met the diagnostic criteria for sleep apnoea but that was not specified [40]. Impaired quality of life has been identified by the American Academy of Sleep Medicine Task Force on Sleep-Related Breathing Disorders in Adults as one of the associated features of obstructive sleep apnoea and sleep hypoventilation syndrome [41]. The Task Force went on to recommend the SF-36, noting that the domains of vitality, role-emotional, mental health and social functioning are consistently rated lower by sleep apnea patients and are responsive to CPAP treatment. However Yang and others were unable to fully replicate those results in a prospective study involving 37 primary care patients with mild (AHI 5–15) to severe sleep apnoea compared to 46 primary care patients without OSA [14]. They found significant differences in Physical Functioning and Role-Physical only and this was when patients with mild OSA were included and age, gender, body mass index, and number of co-morbid conditions were controlled. Interestingly Vitality was not significantly associated although a trend was evident. They addressed one of the most serious problems with the SF-36 in relatively healthy populations: ceiling effects. Over 40% of the sample scored at the top on the scale (i.e. good quality of life) on four of eight domains, making it insensitive to minor differences or improvement in these areas. As noted previously, Role Emotional could not be used in the Sleep Heart Health Study for similar reasons. They also raised the issue of symptom status and its potential impact on comparative studies. The OSA patients were described as symptomatic but there was no measure of daytime sleepiness or other symptoms. Results on the responsiveness of the SF-36 to treatment of OSA vary. In a prospective case series of 29 patients with an average RDI of 77 (range 15–200), patients scored lower than norms for their age and gender in all domains [42]. After eight weeks of CPAP therapy significant improvement was shown in Vitality, Social Functioning and Mental Health when compared to individual baseline scores. Of note they found that the “magnitude of improvement was related to the degree of quality of life impairment prior to treatment, rather than to the severity of disease as measured by the RDI and arousal indices” [42]. While the NHP has also been used in a number

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of sleep apnea studies it is difficult to make any generalizations because of differences in scoring and components used. Meslier and others did a mailout survey of 3225 OSA patients in France who had used CPAP for at least 6 months, using the French version of the NHP, Part 1 [43]. They found that energy was the quality of life domain rated poorest, followed by physical mobility and then sleep. Female respondents rated quality of life consistently poorer across all domains. In spite of self-report of general improvement in symptoms, average quality of life scores in all domains for both females and males remained poorer than for the general French population. In a much smaller German study 39 patients were tested before CPAP treatment and nine months later using the NHP Part 1 and a visual analogue quality of life scale [2]. Only energy and emotional reactions showed significant improvement although all domains except social isolation showed positive trends. The visual analogue scale was insensitive to change. Jokic and others, using the NHP Part 1 in a comparison between positional treatment and CPAP in 13 patients with positional OSA, found higher energy levels with CPAP but caution that the finding was not corroborated by other measures [44]. Fornas and others used the NHP in an untreated OSA population, also finding energy and sleep to be among the worst perceived domains but because of differences in the way the scores were calculated the findings from these studies cannot be compared [45]. Engelman and others used the NHP Part 2 in a randomized trial comparing CPAP with an oral tablet placebo in 32 consecutive OSA patients with AHI averaging 28 per hour of sleep (range 7 to 129) [46]. They found significant improvement in ratings for social life, sex life, and looking after the home in the CPAP group compared to the placebo group. In a subsequent trial limited to 34 mild OSA patients (AHI 5–15) it is noteworthy that they added the SF-36 [47]. No difference between the CPAP and placebo group was detected by the NHP Part 2 but improvement was statistically significant in five SF-36 domains: Role-physical, Bodily Pain, Social Function, Vitality and Health Transition with moderate effect sizes, ranging from 0.44 to 0.67. The work of Engelmans’ group [46, 47] stands out for its design strength, being based on randomized trials. Much of the other intervention research to date for OSA has depended on case

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series, pre-test post-test designs and/or comparisons with general population norms. The potential for placebo effect has received insufficient attention in such studies [48]. Other quality of life measures have been less frequently used in sleep apnoea studies. Jenkinson, Stradling and Petersen compared the SF-36, a British version of the SIP known as the Functional Limitations Profile (FLP), and the EQ-5D in 108 male OSA patients before and 5 weeks after beginning treatment [49]. The responsiveness of the SF-36 and FLP were broadly similar with significant improvement shown in most dimensions. Effect sizes for the statistically significant SF-36 subscales were in the 0.48 (Role Emotional) to 0.98 (Vitality) range with the Mental and Physical Health summary scores at 0.76 and 0.57 respectively. Effect sizes for the statistically significant FLP subscales ranged from 0.47 (Alertness) to 0.88 (Sleep and Rest) with the Psychosocial domain score at 0.64 and the total score at 0.68. The EQ-5D detected little decrement in quality of life for OSA patients before treatment and hence had little range for responsiveness. Thus it was not recommended for use with OSA patients. Kribbs and others found the SIP to be responsive in detecting change with two to three months of CPAP treatment [50]. Most often the SIP has been used in validation studies for disease or condition specific measures such as the FOSQ and SAQLI. The SAQLI was used in a Chinese study of 108 OSA patients to study the effects of education and support on compliance with CPAP [51]. Patients who received enhanced education and support (i.e. videotape, telephone support, check-in at weeks 1 and 2) had greater improvement in quality of life at 4 (P=0.008) and 12 weeks (P=0.047) after initiation of treatment than did those with usual education and support (i.e. brochures and check-in at weeks 4 and 12). No significant between-group differences were detected in compliance, sleepiness or cognitive function. However, in a British trial comparing intensive education and nursing support, significant differences were found at six months in compliance, symptoms, mood, and cognitive functioning but not quality of life as measured by the SF-36 and NHP Part 2 [52]. An Austrian group used the Munich life-quality dimension list in a three group cross-sectional study in which they found that subjects on CPAP treatment for >3 months had comparable quality of life

to a control group of hospital visitors, both of whom had significantly better quality of life on 6 of 19 dimensions in this generic measure [53]. Generally it can be concluded that untreated OSA impacts most domains of health related quality of life and that treatment improves quality of life with some domains being more responsive than others. However the somewhat inconsistent domain by domain results across studies, even when the same measure was used, illustrate one of the more fundamental problems with quality of life research. It is very difficult to measure a construct that is so broad and subjective. The most effective strategies for dealing with this issue appear to be: 1. Use of consistent measures across studies, ideally one generic such as the SF-36 or its derivatives and one condition-specific such as the FOSQ or SAQLI. 2. Inclusion of some means whereby subjects can weigh satisfaction by importance and/or controlling for not only the usual demographic factors but also types of employment, primary relationships, and preferred leisure activities.

Narcolepsy While the impact of narcolepsy on psychosocial functioning has been long recognized there has been surprisingly little specific health related quality of life research. Unlike the situation for other sleep disorders, several excellent reviews are available that address impact on broader issues of quality of life like learning and social adjustment in the school context; work, career, and socioeconomic implications; marital stress and sexual dysfunction; stigma; and safety in home, work, and driving situations [54, 55]. A detailed 1981 survey comparing life effects of narcolepsy in 180 subjects matched with local controls and drawn from centres in Canada, Japan, and Europe is a classic in this area [56]. For the Modafinil in Narcolepsy Multicenter Study the SF-36 was chosen as the primary quality of life measure along with supplemental scales on symptoms, attention/concentration, productivity, self-esteem, overall health perceptions, driving limitations, and social support [57]. Results show most severe effects in Vitality, Role Limitations Physical and Emotional and Social Function. The magnitude was comparable or worse to scores from subjects

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with Parkinsons Disease or epilepsy. The SF-36 was also used in a postal survey completed by 313 members of the United Kingdom Association for Narcolepsy [58]. No significant difference was found on any of the sub-scales between respondents with or without cateplexy but, as in previous studies, average scores on functional and emotional status were poorer than age matched normative data. Unfortunately generic measures such as the SF-36 miss much of the profound life-changing aspects of severe narcolepsy.

Snoring Little work has been done that differentiates quality of life impact of snoring from OSA but two studies are of import here, besides the one previously discussed with respect to bed-partners of snorers, many of whom had confirmed OSA. In a Swedish study in which responses of 42 males with a mean RDI of 8.6 (range 1–47] and mean BMI of 26 (range 20–39) were compared to a population-based sample of 786 men of comparable age (mean 45 years, range 28–62) both parts of the NHP were used [59]. BMI was correlated with the total score for Part I and Mobility in particular whereas the RDI was not correlated with either the total score or Energy. For Part 1 significant differences between snorers and controls were found for Energy (P<0.001), Emotions (P= 0.02) and total score (P= 0.001) with a trend re Sleep (P=0.07). Total scores were comparable to those found for other chronic conditions such as hypertension and chronic obstructive respiratory disease. For Part 2 there were significant differences in all seven areas: paid employment, house work, social life, family life, sex life, hobbies, and holidays. The investigators concluded that Part 2 of the NHP is more sensitive to impact of snoring than Part 1 and emphasized the observation that snoring is not the minor social problem it is often viewed to be. In validating the SAQLI, we included 50 snorers as well as 113 subjects with mild (n=48), moderate (n=31), and severe (n=21) sleep apnea [29]. We found that the magnitude of frequency times importance products for each of the test items varied by severity of OSA with the lesser weighting among snorers as expected, but, of importance, was the finding that the rank ordering was very similar. The etiology for the impact of snoring on quality of life goes beyond the effects of sound to the increasing

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evidence of a related syndrome of upper airway resistance [31]. However, even in OSA quality of life research it would be useful to control for snoring as the social disturbance of the sound itself clearly impacts family, social, and even some work relationships (e.g. among crews, firemen and others who sleep at a work site).

Restless legs and periodic limb movement In a recent Academy of Sleep Medicine Review reference is made to the “striking omission” of quality of life research with respect to restless legs and periodic limb movement disorder (PLMD) [60]. Most trials for treatment of restless legs and PLMD included symptom and sleep quality measures but not quality of life. In two German drug trials a modified version of the Hamburg visual analog scales were used [61, 62]. This measure is described as having two domains: life satisfaction (e.g. with cognitive performance, activities of daily living, leisure, and efficacy at work) and burden of symptoms (e.g. depression, fatigue, physical symptoms). In a study comparing levodopa with benserazide to placebo the biggest improvements in quality of life for the 32 subjects were noted in activities of daily living, leisure, less severe fatigue and depressive feelings [61]. In a study evaluating the tolerability and efficacy of the longer acting dopamine agonist D1/D2 cabergoline with 9 subjects, significant improvement was found after 12 weeks of therapy in activities of daily living, mental function and general satisfaction with life, with reduced fatigue, and depressive feelings [62]. Given the small sample size it is also noteworthy that the change in leisure was P=0.051.

Insomnia The relationship between insomnia and quality of life has been repeatedly studied but the lack of clear definitions of both constructs and the use of unstandardized measures has weakened the overall conclusions that can be drawn. Zammit and others found poorer quality of life in all domains of the SF-36 for volunteers with selfreported insomnia three or more nights a week for at least 1 month when compared to health controls [63]. However the SF-36 did not detect

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differences between treated and untreated subjects. It was not clear whether this was a problem of sensitivity of the measure or lack of precision as to what constituted treatment. It is of note that these investigators added a self-developed measure, the Quality of Life Inventory, for which no psychometric information is provided but which did include domains not well addressed on the SF-36 such as leisure activity. They also added a Work Daily Activities Questionnaire and comment in the discussion that a disease/condition specific measure such as the FOSQ would be useful in supplementing the SF-36 for domains such as intimacy and sexual relationships. Two recent documents on nonpharmacologic treatment of chronic insomnia echo the call for increased research on the clinical impact of insomnia treatment on quality of life [64, 65]. In spite of several attempts to develop condition specific quality of life measures for insomnia none has been well validated and widely used. The co-existence of insomnia with mood disorders and numerous other health and environmental conditions adds to the challenge of developing a quality of life measure for insomnia.

FURTHER CONSIDERATIONS The impact of sleep disorders on quality of life was documented in popular literature long before the advent of specific measures. Disorders of excessive sleepiness (e.g. sleep apnoea and narcolepsy) and circadian rhythms (e.g. delayed sleep phase) have been shown to interfere with educational achievement, interpersonal relationships, employment, and safety [66, 67]. These parameters extend far beyond the standard domains of most generic health related quality of life measures. The need for increased attention to the measurement of quality of life is high. A review of the published abstracts for the 2001 Associated Professional Sleep Societies conference indicated that about 1% incorporated quality of life indicators [68]. Most of the work to date has related to the disorders of excessive sleepiness using generic health related measures. The challenge goes out to sleep researchers and clinicians to broaden their focus (a) to the impact of all sleep disorders on quality of life and (b) to thinking of quality of life in terms of the dimensions that are most important to individuals. To do so requires recognition of

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other indicators such as economic status, intimate relationships, leisure, safety, learning, and career progression as well as the more common indicators of physical, mental and social function, burden of symptoms (including treatment side-effects), and overall sense of well-being.

Practice Points 1. Quality of life is essentially what individuals perceive as their overall sense of well-being based on functional ability, health, and satisfaction with the important dimensions of their lives. Thus it is best determined in interaction with the individual. 2. The findings reported here suggest at least some quality of life domains are almost always affected when patients experience chronic sleep disturbance. 3. Sleep quality and quantity should be routinely assessed in primary care because of its association with quality of life, including symptoms of fatigue, energy levels, daytime sleepiness, mental and physical functioning, family relationships, and even bodily pain. 4. A single question about quality of life may be somewhat helpful but use of a standard measure that can be completed while the patient is in the waiting room can be much more useful in eliciting the extent to quality of life in different domains is being affected and the subsequent response to treatment.

Research Agenda Further research is needed to: 1. Identify etiological factors associated with DIMS and quality of life in specific patient groups. 2. Compare the quality of life impact of sleep disorders to the impact of other chronic conditions (using generic measures and as a basis for determining QALYs). 3. Investigate association of demographic and cultural variables, co-morbidities, etc.using sleep disorder specific measures such as the FOSQ and SAQLI. 4. Explore the relationships between gender and quality of life in different sleep disorders.

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5. Identify baseline factors that would predict which sleep disorders patients are likely to gain the most benefit to quality of life from various treatments.

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14.

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GLOSSARY AHI=Apnea-hypopnea index; CPAP=Continuous positive airway pressure; DIMS=Disorders of initiating or maintaining sleep; EDS=Excessive daytime sleepiness; ESS=Epworth Sleepiness Scale; FOSQ=Functional Outcomes of Sleepiness Questionnaire; HUI=Health Utilities Index; MSLT=Multiple Sleep Latency Test; NHP=Nottingham Health Profile; PLMD=Periodic limb movement disorder; QALY=Quality adjusted life year; RDI=Respiratory disturbance index; SAQLI=Sleep Apnea Quality of Life Index; SDB=Sleep Disordered Breathing; SF-36=Medical Outcomes Study 36Item Short Form Health Survey; SIP=Sickness Impact Profile.