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What is the best measure of daytime sleepiness in adults with heart failure?

Riegel, Barbara DNSc, RN, FAAN, FAHA1; Hanlon, Alexandra L. PhD1; Zhang, Xuemei MS2; Fleck, Desiree PhD, RN3; Sayers, Steven L. PhD4; Goldberg, Lee R. MD, MPH5; Weintraub, William S. MD6

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Journal of the American Association of Nurse Practitioners: May 2013 - Volume 25 - Issue 5 - p 272-279
doi: 10.1111/j.1745-7599.2012.00784.x
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Sleep-disordered breathing (SDB) is extremely common in adults with heart failure (HF). In a recent prevalence study, 61% of HF patients had central or obstructive sleep apnea (MacDonald, Fang, Pittman, White, & Malhotra, 2008). Yet, referral for polysomnography is not routine, even in specialty HF settings (Riegel, Moser, Powell, Rector, & Havranek, 2006). Identification and treatment of SDB is imperative because an untreated sleep disorder amplifies the strain on the heart with increased respiratory effort, hypoxia, and sympathetic stimulation (Valdivia-Arenas, Powers, & Khayat, 2009). Persons with SDB are at increased risk for myocardial infarction, stroke, arrhythmia, and probably early mortality (Selim, Won, & Yaggi, 2010). Treatment of SDB may improve ejection fraction and outcomes for patients with systolic HF and decrease pulmonary artery pressures for those with preserved ejection fraction (Oldenburg et al., 2009).

In the general population, people with SDB are typically sleepy during the day and a complaint of excessive daytime sleepiness is the symptom that stimulates a referral for testing. Yet, daytime sleepiness is rarely pronounced in HF patients with SDB (Javaheri et al., 1998; Kaneko et al., 2003; Sin et al., 1999). Therefore, a sensitive, accurate, and easy-to-administer screening method is needed to identify patients who may have SDB in order to more quickly and efficiently address this dangerous comorbidity. The purpose of this study was to test the sensitivity and specificity of three simple screening measures of daytime sleepiness in adults with HF.

Daytime sleepiness is defined as difficulty maintaining a desired level of wakefulness (Young, 2004). Individuals with excessive daytime sleepiness experience a feeling of being drowsy with a tendency to actually fall asleep or nap, known as sleep propensity (Laffont et al., 2002). There is general agreement that sleep propensity reflects the interaction of homeostatic mechanisms and circadian rhythm (Achermann, 2004; Achermann & Borbely, 1994). The homeostatic mechanisms regulate sleep intensity, while the circadian clock regulates the timing of sleep. Others have proposed that sleep propensity depends on the relative strength of the sleep and wake drives (Edgar, Dement, & Fuller, 1993). The sleep drive is the mechanism that tells us of the need for sleep while the wake drive reflects chronobiological and environmental factors such as physical activity, which stimulate arousal (Cluydts, De Valck, Verstraeten, & Theys, 2002). In adults with SDB, sleep intensity and continuity are interrupted by repeated episodes when breathing stops or is markedly reduced, which cause nighttime arousals and daytime sleep propensity (Banno & Kryger, 2007).

Measures of sleepiness

A variety of measures are available for the assessment of excessive daytime sleepiness. Cluydts et al. (2002) divided these measures into behavioral measures, subjective rating scales, and electrophysiological measures. Behavioral measures include performance tests such as the psychomotor vigilance task (PVT). The PVT is a sensitive measure of lapses in attention in response to daytime sleepiness (Dinges et al., 1997). Subjective rating scales reflect acute or global sleepiness. Two examples of subjective rating scales are the Stanford Sleepiness Scale (SSS) and the Epworth Sleepiness Scale (ESS; Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973). The SSS measures sleepiness at a particular moment, while the ESS measures global or typical sleepiness (Johns, 1992). Electrophysiological measures include tests such as polysomnography, pupillometry, and the Multiple Sleep Latency Test. When sensitivity and specificity of the Multiple Sleep Latency Test, the maintenance of wakefulness test, and the ESS were compared, the ESS was the most discriminating test (Johns, 2000). These measures vary greatly in cost, equipment, and training requirements.

As the goal of this study was to identify a measure of daytime sleepiness that is sensitive to daytime dysfunction in adults with HF, self-report measures that can be used to screen patients in an office setting were preferentially tested. We assessed the ability of three such measures of daytime sleepiness to identify sufficiently poor sleep quality to cause complaints of daytime dysfunction in adults with HF. The measure tested in addition to the ESS and the SSS was a simple Likert item assessing sleepiness. Poor sleep quality was measured with the daytime dysfunction subscale of the Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). A measure of daytime dysfunction because of poor sleep was chosen as others have shown that SDB can cause daytime dysfunction even in those individuals who do not perceive being sleepy (Verstraeten & Cluydts, 2004).


A sample of 280 adults with HF was enrolled from three sites in Philadelphia, Pennsylvania, and Newark, Delaware, as part of a larger study of the impact of sleepiness on HF self-care. Institutional review board approval was obtained from each institution and every subject provided informed consent. Data for the current descriptive study were collected by research assistants at enrollment, which took place during home visits. Further details of this study have been published previously (Riegel et al., 2011).

Study population

All participants had chronic (systolic or diastolic) symptomatic HF confirmed based on echocardiographic and clinical evidence and were as follows: (1) able to perform tests (e.g., sufficient visual and hearing acuity, able to speak and read English); and (2) living in a community (noninstitutional) setting. We excluded those with major depression to avoid issues of sleepiness caused by depression. Anyone noted in the medical record to have a major depressive illness were excluded. We also screened potential participants with the 9-item Patient Health Questionnaire (PHQ-9; Kroenke, Spitzer, & Williams, 2001); anyone reporting five or more of the nine symptoms more than half the days in the past 2 weeks (one of the symptoms had to be depressed mood or anhedonia) was excluded. Anyone with a positive response to the item asking about suicidal ideation or with evidence of major depression was strongly encouraged to seek care and the provider was notified. We also excluded individuals with significant cognitive impairment by history or on testing with the Telephone Interview for Cognition Screening (TICS); anyone with a score <24 (significant cognitive impairment) was excluded (Brandt & Folstein, 2003). Individuals with an imminently terminal illness, plans to move out of the area, a history of drug or alcohol abuse within the prior year, night shift workers, and those on renal dialysis were excluded. In total, 333 eligible individuals were identified and 280 were enrolled.


Sleep dysfunction was assessed with the daytime dysfunction subscale of the PSQI, a subjective rating scale (Buysse et al., 1989). The subscale items ask about staying awake and maintaining enough enthusiasm to get things done during the day. The two items are scored 0 (not in the past month) to 3 (three or more times a week), summed, and then compressed into a subscale score ranging from 0 to 3; a higher score indicates more daytime dysfunction. When the full PSQI score is used, a score >5 has a sensitivity of 89.6% and a specificity of 86.5% in distinguishing good and poor sleepers. In this study, the score on PSQI daytime dysfunction subscale was dichotomized at a cut-point of 2 (0/1 vs. 2/3).


Respondents rated the likelihood of falling asleep in eight soporific situations using a 4-point scale ranging from never dozing (0) to high chance of dozing (3; Johns, 1993). Test–retest reliability (r = .82) and internal consistency (α = .88) have been established in addition to its single factor structure (Johns, 2000). Scores are summed, with higher scores indicating greater sleepiness, or categorized as sleepy (≥11), or not sleepy (<11). At this cut-point of 11, the ESS had a sensitivity of 93.5% and a specificity of 100% for distinguishing pathological from normal sleepiness in a sample drawn from sleep-disorder centers (Maislin et al., 1995). This cut-point on the ESS, however, may not adequately detect excessive daytime sleepiness in adults with HF (Javaheri et al., 1998; Kaneko et al., 2003; Sin et al., 1999). So, in addition to a cut-point of 11 (sleepy ≥ 11 or not sleepy < 11), we tested a cut-point of 6 (sleepy ≥ 6 or not sleepy < 6) on the advice of the instrument author (Murray Johns, personal communication, 2006). This lower cut-point allowed us to have a higher index of suspicion for HF patients who are known to have less sleepiness than other patient groups. Because the instrument itself was not changed, this different cut-point did not affect the reliability and validity of the measure.


The SSS is one of the oldest subjective sleepiness scales still in use today (Hoddes et al., 1973). Subjects evaluate their current degree of sleepiness from 1 to 7 with 1 equivalent to feeling vital, alert, or wide awake and 7 equivalent to feeling that sleep onset is soon. The SSS is said to be sensitive to both sleep deprivation and time of day (Babkoff, Caspy, & Mikulincer, 1991; Johnson, Freeman, Spinweber, & Gomez, 1991). The SSS was shown to be sensitive to deficits in alertness following partial sleep deprivation, although it did not predict individual performance on vigilance tests (Herscovitch & Broughton, 1981). In this study, we tested a cut-point of 4 on the SSS (sleepy ≥ 4 or not sleepy < 4), which corresponds to “a little foggy; not at peak; let down.”

Likert item.

Rating scales are commonly used to measure attitudes and perceptions. The sensitivity of such items is determined by the number of response choices available. In this study, a single item with a 10-point scale was used. Respondents were instructed to indicate their sleepiness at that moment by choosing a number between 1 (not sleepy at all) and 10 (very sleepy). Others have found that using a single item to measure sleep quality produces reproducible and valid data in other patient groups (Cappelleri et al., 2009). No prior testing of sensitivity and specificity of this approach was located. In this study, a cut-point of 4 (sleepy ≥ 4 or not sleepy < 4) was tested for sensitivity and specificity.


Demographic and clinical characteristics are described using mean values and standard deviations for continuous variables, and frequencies and percents for categorical variables. Characteristics of the sample were compared according to dichotomized PSQI daytime dysfunction subscale score (<2 vs. ≥2) using two-sample t-tests and chi-square statistics for continuous and categorical variables, respectively. Linear regression models and Pearson correlation coefficients were used to quantify the direction and strength of linear association between PSQI daytime dysfunction subscale score and self-rated measures of daytime sleepiness (ESS, SSS, and Likert item).

For each of the three self-rated daytime sleepiness measures, receiver operating characteristic (ROC) curves were generated by plotting the sensitivity against 1-specificity. The area under the ROC curve (AUC) was calculated to assess the accuracy of each measure against the dichotomized PSQI daytime dysfunction subscale score. AUC measures the ability of a screening tool to correctly classify individuals as having a specific condition or not, in this case excessive daytime sleepiness. Scores can range from 0.5 to 1.0, where 0.5 indicates an uninformative screen, and 1.0 indicates a perfect screen. The predictive capacity of the three subjective rating scales to predict the PSQI daytime dysfunction subscale score was further described by estimating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio (+LR), and negative likelihood ratio (−LR), along with their corresponding 95% confidence intervals. Likelihood ratios can be used to determine the odds of a specific outcome in a particular patient.


Descriptive statistics were used to identify the predominant characteristics of the sample. The sample of 280 was predominantly male (64%), Caucasian (63%), married (81%), older adults (62 ± 12.5 years; Table 1). Most had systolic (69%) HF of ischemic origin (37%) and were severely limited in their functional abilities (77% New York Heart Association [NYHA] class III or IV). However, comorbidity measured with the Charlson index (Charlson, Pompei, Ales, & MacKenzie, 1987) was low for most (53%), indicating that overall the subjects had few other major illnesses.

Table 1
Table 1:
Demographic, clinical, and treatment characteristics of the sample of adults with heart failure, with comparisons according to the presence or absence of sleep dysfunction
Table 1
Table 1:

When scores on the PSQI daytime dysfunction subscale were dichotomized at ≥2, only 16% (n = 45) of the sample reported significant daytime dysfunction because of poor sleep quality (Table 1). Using t-tests and chi-square statistics, we found that those reporting daytime dysfunction were likely to be younger (p < .001), to be unmarried (p = .002), to have NYHA functional class IV HF (p = .015), and to report low income (p = .006) and fewer hours of sleep (p = .015).

The correlation between the three measures of daytime sleepiness was generally low (.24–.41), with the exception of the relationship between the SSS and the Likert item, which was moderately large with r = .64 (Table 2). None of the three measures was moderately or highly correlated with daytime dysfunction.

Table 2
Table 2:
Correlation coefficients among PSQI sleep dysfunction score and measures of sleepiness

Using ROC curves (Figure 1), the measure of daytime sleepiness that was most sensitive to daytime dysfunction was the Likert item (Table 3). Although sensitivity of the ESS was higher using a cut-point of ≥6, specificity at this cut-point was low. At the cut-point of ≥11, the ESS was moderately specific but inadequate in sensitivity. A similar picture was seen with the SSS. Only the Likert item demonstrated adequate sensitivity and specificity.

Table 3
Table 3:
Sensitivity and specificity for sleepiness measures as a predictor of sleep dysfunction ≥2
Figure 1
Figure 1:
ROC curve for predicting sleep dysfunction (score >2).


The results of this study suggest that use of a single-scaled question about sleepiness is an effective method of detecting sleep problems that may require further screening in a clinical setting. These findings are important because abnormal sleep patterns are associated with increased risks of morbidity, poor quality of life, and mortality (Zisapel, 2007). Daytime sleepiness is an indicator of the need for further screening for SDB, as well as other chronic conditions such as nondipping hypertension (Erden et al., 2010), chronic kidney disease (Kumar et al., 2010), depression (Koutsourelakis et al., 2009), and rheumatic disease (Goodchild, Treharne, Booth, & Bowman, 2010). Adding this single scaled question to the routine history may improve the detection of chronic comorbid conditions in adults with HF.

This study is the first to definitively show that neither the SSS nor the ESS—regardless of the cut-point used—is sufficiently sensitive and specific for use in clinical practice. In research, a single item measure of daytime sleepiness is not adequate, but in clinical practice it may be the most practical and sensitive method for screening (Schumacher, Gleason, Holloman, & McLeod, 2010).

The SSS has fallen out of favor in recent years, probably because it was suspected of poor sensitivity (Herscovitch & Broughton, 1981). The ESS, however, is considered the gold standard for the assessment of daytime sleepiness by many. In spite of this, the cut-point of ≥11 has been questioned by clinicians. Even the instrument author recommends that the cut-point be modified for patients with HF.

In this population of adults with HF, we found that only 16% reported daytime dysfunction because of poor sleep quality. This was surprising, as sleep complaints are highly prevalent in other populations. In a survey of 1935 patients from family practice offices in North Carolina, more than half the patients reported daytime sleepiness (Alattar, Harrington, Mitchell, & Sloane, 2007). In that survey, one group likely to report poor sleep was those with limited activity, similar to our sample of NYHA functional class III patients. The reason that so few of our patients reported daytime dysfunction may be related to the known change in sensitivity to impaired sleep that occurs with age (Bixler, Vgontzas, Ten Have, Tyson, & Kales, 1998). Or, it could be related to the gray and white matter losses in the brain that occur in HF (Woo, Kumar, Macey, Fonarow, & Harper, 2009).

These results support those of prior investigators who have demonstrated that excessive daytime sleepiness is not easily detected in adults with HF, even when they have SDB (Arzt et al., 2006; Javaheri et al., 1998; Johansson et al., 2010; Kaneko et al., 2003; Rao et al., 2006). In our study, daytime sleepiness did not differ in subjects with and without SDB. The mean scores on the ESS in our sample were basically identical to those found by Arzt et al. (2006) in their study of daytime sleepiness in patients with HF. Comparing sleepiness in patients with and without HF, Arzt et al. also concluded that HF patients have less subjective daytime sleepiness than would be expected given the prevalence of SDB. Clearly, a high level of suspicion needs to be used when considering which patients to refer for further testing.

The sociodemographic factors characterizing patients with daytime dysfunction because of poor sleep were age, marital status, and income. Sleep complaints are usually attributed to the elderly (Stores, 2007), although others have found sleep problems to be more common in younger individuals, as we found (Kumar et al., 2008). The finding that single individuals had more daytime dysfunction supports that of Hawkley and colleagues, who found that loneliness in socially isolated individuals predicted daytime dysfunction, independent of sleep duration and after adjusting for depression (Hawkley, Preacher, & Cacioppo, 2010). Our finding that individuals with lower income were more likely to report more daytime dysfunction supports results from the 2004 to 2007 National Health Interview Survey. Lower income was found to be associated with poor sleep in that nationally representative survey of 110,441 noninstitutionalized U.S. adults (Krueger & Friedman, 2009).

One limitation of this study is that the sample of adults with HF may not mirror that of the general population of HF patients, who are generally older. These patients were younger probably because everyone with HF was included, regardless of the type or etiology of HF (systolic or diastolic). This decision influenced the mean age of the sample but may also have made the results more generalizable to HF patients overall.

Implications for practice

The likelihood ratios in Table 3 can be used to identify the odds of daytime dysfunction associated with daytime sleepiness in a particular patient. The positive likelihood ratio of 2.37 for the Likert item indicates that the likelihood of having daytime dysfunction for a patient scoring 4 or more on that single item has increased by approximately 2.4-fold over someone with a score less than 4. These results suggest that a higher index of suspicion is warranted in HF patients who are relatively young, unmarried, functionally compromised, those with low income and an inadequate amount of sleep. Those reporting sleepiness should be considered for further screening.


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    Heart failure; screening; sleep disorders; outcomes

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