Brief and distinct empirical sleepiness and fatigue scales

Bailes, Sally; Libman, Eva; Baltzan, Marc; Amsel, Rhonda; Schondorf, Ron; Fichten, Catherine S.

doi:10.1016/j.jpsychores.2005.08.015

Cited by 60 publications

(58 citation statements)

References 33 publications

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“…Although classical psychometric assessment tools like the Epworth Sleepiness (ESS) and the Fatigue Severity Scales (FSS) are available for the assessment of subjective fatigue and sleepiness complaints, the definition and semiological distinction of both concepts often remain difficult for both patients and clinicians [5,11] . Moreover, although both fatigue and sleepiness can be associated with complaints of NRS [1] , their relationships to each other and to sleep remain insufficiently understood.…”

mentioning

confidence: 99%

Do ‘Sleepy’ and ‘Tired’ Go Together? Rasch Analysis of the Relationships between Sleepiness, Fatigue and Nonrestorative Sleep Complaints in a Nonclinical Population Sample

et al. 2010

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Objective: The lack of distinction in the clinical use of terms like fatigue and sleepiness is an important issue. While both fatigue and sleepiness can potentially be associated with nonrestorative sleep (NRS) complaints, their relationships are still poorly described. We propose to use Rasch analysis-based methods to study the interrelations of fatigue, sleepiness and NRS. Methods: 150 subjects (mean age = 39.3 years, range = 18–65) from a community sample underwent a structured computer-assisted web interview. We assessed demographic data, sleep habits, and subjective fatigue with the Fatigue Severity Scale (FSS), global and situational sleepiness with the Epworth Sleepiness (ESS) and the Stanford Sleepiness Scales, respectively, and affective symptoms with the Hospital Anxiety and Depression Scale. Dimensionality, measurement invariance and common person equating were investigated to study the FSS, ESS and their relations to NRS. Results: NRS was linked to shorter habitual sleep duration and to higher scores on psychometric scales. Both sleepiness and daytime fatigue were positively correlated to each other and to the intensity of affective symptoms. Rasch analyses showed both the ESS and FSS to measure unidimensional concepts of sleepiness and fatigue, respectively. In contrast to the FSS, the ESS only showed partial invariance to an NRS complaint. Common person equating suggests that, despite similar Rasch-derived agreeability scores, fatigue and sleepiness (as measured by the FSS and ESS) nevertheless designate distinct constructs. Conclusion: NRS complaints can simultaneously present with higher daytime fatigue and sleepiness levels but the associative relationships between fatigue and sleepiness remain relatively unaffected by NRS. Although participants might not present adequate differentiation, fatigue and sleepiness seem to relate to different underlying concepts.

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confidence: 99%

Do ‘Sleepy’ and ‘Tired’ Go Together? Rasch Analysis of the Relationships between Sleepiness, Fatigue and Nonrestorative Sleep Complaints in a Nonclinical Population Sample

et al. 2010

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“…The dissociation between fatigue and sleepiness ratings has been highlighted previously, with the former more frequently reported in insomnia. 71,72 Controlling for OSA severity, overweight/obese participants were more likely to belong to the IRD than the MI symptom cluster profile. Our findings report on cross-sectional data, and thus preclude inference about causal relationships between insomnia-related distress and obesity; however, these results might suggest avenues for further investigation.…”

Section: "Insomnia-related Distress" Symptom Cluster Profilementioning

confidence: 99%

Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

Crawford

Chirinos

Iurcotta

et al. 2017

Journal of Clinical Sleep Medicine

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Study Objectives: This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Methods: Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. Results:The best-fitting model had three symptom cluster profiles: "High Subjective Wakefulness" (HSW), "Mild Insomnia" (MI) and "Insomnia-Related Distress" (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. Conclusions:The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care. Keywords: insomnia disorder, latent profile analysis, symptom profile, symptom clusters Citation: Crawford MR, Chirinos DA, Iurcotta T, Edinger JD, Wyatt JK, Manber R, Ong JC. Characterization of patients who present with insomnia: is there room for a symptom cluster-based approach? J Clin Sleep Med. 2017;13(7):911-921. I NTRO DUCTI O NInsomnia is the experience of the difficulty falling asleep, difficulty staying asleep, or early morning awakenings. About one-third to one-half of adults complains of these symptoms. Frequently, other complaints, such as sleepiness, fatigue, and hyperarousal, will occur with nocturnal sleep disturbance. An insomnia disorder is defined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 2 as the combination of the nocturnal sleep disturbance with one of these waking complaints at least 3 nights a week for at least 3 months. Approximately 8% to 10% of the adult population meets these criteria. 3,4Insomnia disorder is a heterogeneous condition.5 This can pose a chall...

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“…Healthy control sample (Bailes, et al, 2006). Fifty-four control group subjects were individuals with no diagnosed medical or psychiatric condition.…”

Section: Study 1: Development Of the Classification Systemmentioning

confidence: 99%

Classifying Medication Use in Clinical Research

Rizzo

Creti

Bailes

et al. 2010

J Prim Care Community Health

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Background: Medication use data are usually collected in clinical research. Yet no standardized method for categorizing these exists, either for sample description or for the study of medication use as a variable. Objective: The present investigation was designed to develop a simple, empirically based classification scheme for medication use categorization. Method: The authors used factor analysis to reduce the number of possible medication groupings. This permitted a pattern of medication usage to emerge that appeared to characterize specific clinical constellations. To illustrate the technique’s potential, the authors applied this classification system to samples where sleep disorders are prominent: chronic fatigue syndrome and sleep apnea. Results: The authors’ classification approach resulted in 5 factors that appear to cohere in a logical fashion. These were labeled Cardiovascular or Metabolic Syndrome Medication, Symptom Relief Medication, Psychotropic Medication, Preventative Medication, and Hormonal Medication. Conclusions: The findings show that medication profile varies according to clinical sample. The medication profile for participants with sleep apnea reflects known comorbid conditions; the medication profile associated with chronic fatigue syndrome appears to reflect the common perception of this condition as a psychogenic disorder.

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Brief and distinct empirical sleepiness and fatigue scales

Cited by 60 publications

References 33 publications

Do ‘Sleepy’ and ‘Tired’ Go Together? Rasch Analysis of the Relationships between Sleepiness, Fatigue and Nonrestorative Sleep Complaints in a Nonclinical Population Sample

Do ‘Sleepy’ and ‘Tired’ Go Together? Rasch Analysis of the Relationships between Sleepiness, Fatigue and Nonrestorative Sleep Complaints in a Nonclinical Population Sample

Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

Classifying Medication Use in Clinical Research

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