This study adhered to U.S. Department of Health and Human Services human experimentation guidelines and received Institutional Review Board approval from the CDC and collaborating institutions. All participants gave informed consent.
Between January and July 2003, we conducted a 2-day in-hospital study of adults identified with CFS from the general population of Wichita [19
]. The in-hospital study enrolled people who participated in the 1997 through 2000 Wichita Population-Based CFS Surveillance Study [20
]. The primary objective of the Surveillance Study was to estimate the baseline prevalence and 1-year incidence of CFS in Wichita, Kansas. Participants in the in-hospital study were fatigued adults with medically/psychiatrically unexplained chronic fatigue identified during the surveillance study. Fifty-eight participants had been diagnosed at least once with CFS and 59 had unexplained chronic fatigue that was not CFS. Controls were randomly selected from the cohort who participated throughout surveillance, who did not have medical or psychiatric exclusions, and who had not reported fatigue of at least 1-month duration; they were matched to CFS cases on sex, age, race/ethnicity, and body mass index. Upon admission to this study, subjects were re-evaluated for CFS symptoms and exclusionary medical and psychiatric conditions (discussed below). The 43 who, at the time of the in-hospital study, met 1994 criteria for CFS (discussed below) comprise the cases in this report. Control subjects were 43 individuals who had never reported fatigue during the surveillance study, who were not fatigued at the time of entry into this in-hospital study and who had no exclusionary medical or psychiatric condition identified at the time of the study (following section). Because current classification of CFS was not completely in accord with recruitment classification, strict matching was not maintained, though cases and controls were demographically comparable. Thirty-six (84%) of the 43 with CFS and 38 (88%) of the 43 controls were women; most (40 CFS and 42 controls) were white; their mean ages were 50.6 and 50.3 years, respectively; and body mass index was 29.4 and 29.3, respectively.
Assessment and classification of CFS
We classified participants as having CFS at the time of the study based on the empirical application [19
] of the 1994 CFS research case definition [1
]. We used the Multidimensional Fatigue Inventory (MFI) [21
] to evaluate fatigue status; we measured functional impairment with the Medical Outcomes Survey short form-36 (SF-36) [22
]; and, we used the CDC Symptom Inventory [23
] to assess frequency and severity of the 8 CFS defining symptoms. We defined severe fatigue as ≥ medians of the MFI general fatigue (≥ 13) or reduced activity (≥ 10) scales. We defined substantial functional impairment as scores lower than the 25th
percentile of published US population on the physical function (≤ 70), or role physical (≤ 50), or social function (≤ 75), or role emotional (≤ 66.7) subscales of the SF-36. Finally, subjects reporting ≥ 4 symptoms and scoring ≥ 25 on the Symptom Inventory Case Definition Subscale were considered to have substantial accompanying symptoms.
To assess whether medical conditions exclusionary for CFS (including untreated hypothyroidism, sleep apnea, or narcolepsy) had developed since the surveillance study, participants provided a standardized past medical history and a listing of current medications, underwent a standardized physical examination, and provided blood and urine for routine analysis. Medications that affect sleep were considered 'sleep medications' for the purpose of analysis and include: primary hypnotics (zolpidem, temazepam), narcotic analgesics (e.g., hydrocodone, oxycodone, propoxyphene), anti-depressants (e.g., citalopram, amitriptyline, imipramine, escitalopram, bupropion, venlafaxine, sertraline, paroxetine, fluoxetine), anti-anxiety (alprazolam), anti-histamines (e.g., diphenhydramine, chlorpheneramine, promethazine), decongestants (e.g., pseudoephedrine, guaifenesin), anti-convulsants (e.g., topiramate, clonazepam), anti-sleep phase disorder (melatonin), blood pressure controlling (e.g., clonidine, midodrine), anti-psychotics (e.g., quetiapine, ziprasidone), stimulants (e.g., methylphenidate, modafinil), peristaltic stimulants (metoclopramide), and muscle relaxants (cyclobenzaprine).
To identify psychiatric conditions exclusionary for CFS (current melancholic depression, current and lifetime bipolar disorder or psychosis, substance abuse within 2 years and eating disorders within 5 years), licensed and specifically trained psychiatric interviewers administered the Diagnostic Interview Schedule for Axis I psychiatric disorders.
We classified participants meeting the 3 criteria (MFI, SF-36, and Symptom Inventory) for CFS and in whom no exclusionary medical (including sleep) or psychiatric conditions were identified as having CFS. Participants whose scores were in the normal range on all of the above mentioned instruments and who had no exclusionary medical or psychiatric conditions identified were classified as non-fatigued. Persons with exclusionary medical or psychiatric conditions were not included in the analysis.
Objective measures of sleep alterations
Sleep studies were conducted in a 4-bed clinical research unit at Wesley Medical Center, Wichita, Kansas [12
]. These sleep studies consisted of polysomnography on night #1, Multiple Sleep Latency Tests (MSLT) during the following day, and repeat polysomnography on night #2. Patients were asked to arrive 3 hours before their typical bedtime on night #1 to allow adequate time for electrode application and standard bio-calibrations. "Lights out" and "Lights on" time were 22:00 and 7:00, respectively. MSLT began at 11:00 the following morning and consisted of three additional naps at 13:00, 15:00, and 17:00.
Daytime sleepiness was measured with the MSLT, which has demonstrated objective sensitivity to the effects of sleep deprivation, sleep fragmentation, sleep restriction, insufficient sleep hypersomnia, and in disease states such as sleep apnea and narcolepsy [24
]. Multiple sleep latency tests were performed and scored according to standard guidelines [26
] with the exception that four naps were recorded. The mean sleep latency on the MSLT was defined as the mean time from lights out to the first 30-second epoch scored as sleep. A sleep onset REM was defined as one or more epochs of REM sleep occurring within 15 minutes of the first epoch scored as sleep. We considered a mean sleep latency <5 min as pathological sleepiness, scores between 5–10 min as a degree of daytime sleepiness (borderline abnormal), and scores of 10–20 min as normal and a lack of daytime sleepiness. Because mean values on the MSLT may adversely be affected by a spurious sleep latency on a single nap opportunity [28
] possibly due to what might be perceived as stressful inter-nap activities [29
], median values were also computed for each subject.
Measures of sleep architecture and diagnoses of primary sleep disorders were based upon data from MSLT and the second nocturnal polysomnography (to allow for sleep-lab habituation). Clinical outcomes of polysomnographic assessment and MSLT included obstructive sleep apnea, periodic limb movements, narcolepsy, insufficient sleep syndrome, primary/secondary insomnia, delayed sleep phase syndrome, bruxism, central sleep apnea, and upper airway resistance syndrome.
The polysomnographic outcome variables used in our analyses included: total sleep time
(TST) (in min), sleep efficiency
(% of time spent in bed asleep), the percentage of TST spent in non-REM (NREM) and REM sleep, latency to sleep onset
(in min) to three consecutive epochs of sleep, and REM latency
, defined as the time between the first epoch of any stage of sleep and the first epoch of REM-sleep. Brief arousals
were scored following criteria set forth by the American Academy of Sleep Medicine, and the number of arousals
expressed as a rate per hour of sleep adjusted for TST. Periodic leg movements
both with and without accompanying arousals, were scored according to conventional criteria [30
], and expressed as an index of the rate of leg movements per hour of sleep, and a separately derived index of those accompanied by an American Academy of Sleep Medicine -defined arousal [31
]. We further recorded alpha intrusion
, which was noted in review of 30-second segments.
Polysomnography data were scored by an Emory University registered polysomnology technologist and interpreted by an Emory University Department of Neurology American Board of Sleep Medicine certified physician [12
Assessment of subjective sleep quality and sleepiness
During the afternoon of their arrival at the hospital, subjects completed a self-administered questionnaire that explored themes and beliefs regarding sleep. The first two sleep specific questions, taken from the CDC Symptom Inventory [23
], queried frequency and intensity of unrefreshing sleep and problems sleeping during the past month. A score of 0 reflected no difficulty with unrefreshing sleep or no problems sleeping and the maximum score of 16 indicated the problem had occurred all the time and was severe [see [23
]]. The remaining 24 items of this questionnaire came from the Epworth Sleepiness Scale [32
], which evaluates levels of excessive daytime sleepiness, and from the Toronto Sleep Assessment Questionnaire (SAQ©
], which measures self-reported sleep quality.
Subjects completed four questionnaires (the Nap Booklets) after each nap on day 1, which assessed latency to fall asleep during each nap. Subjects also completed two questionnaires (the Sleep Booklets) the morning after each overnight study, which evaluated 1) perceived sleep quality the night before on a visual analogue scale from 'Best possible sleep' (equals 0) to 'Worst possible sleep' (equals 140); 2) latency to fall asleep (in min); and 3) total sleep time (in min).
Differences in categorical demographic data between CFS cases and non-fatigued controls were evaluated by Chi-Square or Fisher's exact test and continuous variables were compared by the t-test. Chi-Square test was also used for comparison CFS cases and non-fatigued controls in sleep study alterations. We used standard logistic regression analysis to regress CDC Symptom Inventory scores (unrefreshing sleep, problems sleeping) as well as Sleep Booklet scores (latency to fall asleep, total sleep time, sleep quality) and sleep medication use (yes/no) on case status (CFS/non-fatigued). Data from all participants was evaluated by logistic regression; in addition the subgroup of subjects with no alterations noted in sleep studies (normal sleep) were evaluated separately.
A two factor analysis of variance (ANOVA) using a general linear model was employed to measure the association between cases status and medication use (yes/no) with polysomnographic variables. Log transformed values of polysomnographic variables were used when necessary to satisfy the assumption of normally distributed outcomes. Mean values for each polysomnographic variable were adjusted for medication use by utilizing the least squares method.
Paired samples t
-tests were used to compare 1) mean sleep latency, as measured by the MSLT, and mean sleep latency, as evaluated by the Nap Booklets and 2) latency to fall asleep and total sleep time as measured by nocturnal polysomnography with latency to fall asleep and total sleep time as measured by the Sleep Booklets. Comparisons were done separately for the group of subjects with CFS and for the non-fatigued controls. P-values for the paired t
-tests were adjusted for multiple comparisons using both a Bonferroni correction and by computing a false discovery rate [34
Sleep questionnaire data from the SAQ©
and the Epworth sleepiness scale were z-transformed for multivariate analyses. We used Principal Component Analysis (PCA) [35
] with varimax rotation to evaluate which constellation of sleep symptoms represented the majority of the variance in sleep symptoms. Two-factor ANOVA was applied for comparison of factorial scores of sleep questionnaire items between CFS and non-fatigued groups, controlling for sleep medication use (yes/no). Comparison of factorial scores was done for all participants as well as for the subgroup of subjects with normal
Statistical significance for all tests was set at the 5% level. All statistics were computed using SPSS 12.0 (SPSS Inc, Chicago, IL).