The study instruments included a demographic questionnaire, the KPS scale,29
the Pittsburgh Sleep Quality Index (PSQI),19
the General Sleep Disturbance Scale (GSDS),30
the Lee Fatigue Scale (LFS),31
and the Attentional Function Index (AFI).32
Objective data on sleep-wake circadian activity rhythms were obtained by continuous noninvasive monitoring of activity over 48 hours using a wrist motion sensor (Mini Motionlogger Actigraph, Ambulatory Monitoring Inc., Ardsley, NY).33–35
A minimum of 36 hours of continuous data are necessary to have sufficient data to calculate circadian rhythm parameters for a 24-hour period.15
The demographic questionnaire obtained information on age, gender, marital status, education, ethnicity, employment status, living arrangements, and the presence of a number of co-morbid conditions.
Subjective ratings of sleep disturbance were evaluated using the PSQI and the GSDS. The PSQI consists of 19 items designed to assess the quality of sleep in the past month
. The global PSQI score is the sum of the seven component scores (i.e., subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, daytime dysfunction). Each component score ranges from 0 to 3 and the global PSQI score ranges from 0 to 21. Higher global and component scores indicate more severe complaints and a higher level of sleep disturbance. A global PSQI score of >5 indicates a significant level of sleep disturbance.19
A cutoff score of 8 was found to discriminate poor sleep quality in oncology patients.36
The PSQI has established internal consistency, test-retest reliability, and construct validity.19, 36, 37
In this study, the Cronbach’s alpha for the global PSQI score was 0.72.
The GSDS consists of 21-items designed to assess the quality of sleep in the past week
. Each item was rated on a 0 (never
) to 7 (everyday
) numeric rating scale (NRS). The GSDS total score is the sum of the seven subscale scores (i.e., quality of sleep, quantity of sleep, sleep onset latency, mid-sleep awakenings, early awakenings, medications for sleep, excessive daytime sleepiness) that can range from 0 (no disturbance
) to 147 (extreme sleep disturbance
). Each mean subscale score can range from 0 to 7. Because the GSDS items are rated on a scale of 0 (never) to 7 (every day), the subscale scores provide an estimation of the number of days per week that a patient experiences a particular problem. Higher total and subscale scores indicate higher levels of sleep disturbance. Mean subscale scores of ≥ 3 and a GSDS total score of ≥ 43 indicate a significant level of sleep disturbance.38
The GSDS has well-established validity and reliability in shift workers, pregnant women, and patients with cancer and HIV.13, 30, 39
In the current study, the Cronbach’s alpha for the GSDS total score was .84.
Subjective ratings of physical
fatigue and energy were evaluated using the LFS.31
The LFS consists of 18 items that are each rated on a 0 to 10 NRS. Total fatigue and energy scores were calculated as the mean of the 13 fatigue items and the 5 energy items, with higher scores indicating greater fatigue severity and higher levels of energy. Respondents were asked to rate each item based on how they felt “right now,” within 30 minutes of awakening (morning fatigue, morning energy), and prior to going to bed (evening fatigue, evening energy). The LFS has been used with healthy individuals31, 40
and in patients with cancer and HIV.7, 13, 41, 42
Cutoff scores of ≥ 3.2 and ≥ 5.6 indicated high levels of morning and evening fatigue, respectively.38
Cutoff scores of ≤ 6.0 and ≤ 3.5 indicate low levels of morning and evening energy, respectively. The LFS was chosen for this study because it is relatively short, easy to administer, and has well established validity and reliability. In this study, Cronbach alphas for evening and morning fatigue at baseline were .96 and .95, respectively. Cronbach alphas for evening and morning energy were .95 and .95, respectively.
Subjective ratings of attentional
fatigue were evaluated using the AFI. The AFI consists of 16 items that are each rated on a 0 to 10 NRS. A mean AFI score was calculated, with higher scores indicating greater capacity to direct attention and, therefore, lower levels of attentional fatigue.32
Based on a previously conducted analysis of the frequency distributions of AFI scores, attentional fatigue can be grouped into categories of functional status (i.e., patients who score < 5.0 functioning poorly and experiencing high levels of attentional fatigue, patients who score 5.0 to 7.5 functioning moderately well and experiencing moderate levels of attentional fatigue, patients who score >7.5 functioning well and experiencing low levels of attentional fatigue.43
The AFI has established reliability and validity.32, 44
In the current study, Cronbach’s alpha for the AFI was 0.95.
Objective data on sleep-wake circadian activity rhythms were obtained by continuous noninvasive monitoring of activity over 48 hours using wrist actigraphy. Seven nocturnal sleep/rest, four daytime wake/activity, and six circadian activity rhythm parameters were selected from those identified by a National Cancer Institute sponsored conference,45
an expert panel that recommended a standard set of research assessments in insomnia,46
and recently published studies.18, 47
The definitions and values for each of these parameters are listed in .
Definitions of and values for sleep/wake, activity/rest, and circadian rhythm parameters obtained with actigraphya
Wrist actigraphy has been validated with EEG measures of sleep and awakenings in men and women with both healthy and disturbed sleep patterns.34, 35, 46
It provides continuous motion data using a battery-operated wristwatch-size microprocessor that senses motion with a piezo-electric beam and detects movement in all three axes. The accompanying Action 4® software (Ambulatory Monitoring Inc., Ardsley, NY) allows analysis of activity and nonactivity as well as automatic scoring of sleep and wake episodes in minutes. Actigraphy scores, calculated using specific algorithms, correlate with polysomnography in adults at greater than 90%.35
Patients were asked to use the event marker on the wrist actigraph to indicate “lights out” and “lights on” time. Patients reported no difficulties wearing the wrist actigraph. Since the actual time is important in the calculation of the amount of sleep obtained in the amount of time designated for sleep, having an additional source of information about nap times, bed times, and wake times is important. This information was recorded by patients in a two day diary. Upon awakening, the patients used the diary to indicate the number of awakenings during the night.
Data were analyzed using SPSS Version 15.48
Descriptive statistics and frequency distributions were generated for the sample characteristics and symptom data. Spearman rank correlations were calculated between variables.
Actigraphy files programmed in zero-crossing mode with 30 second intervals were analyzed using the Cole-Kripke algorithm in the Action 4® software (Ambulatory Monitoring Inc., Ardsley, NY) by two of the researchers (KL and CW). First, the file was scanned for missing data. If more than four hours of day data or two hours of night data were missing, that day’s or night’s data were not used in the analyses. Time limits were set for the 48-hour period. The file was reviewed and intervals were individually set for each day and night period using, in order of priority as decision guides, the event marker, diary data, channel data, and cascading movement data. Cosinor analysis fit a cosine and sine wave to the wrist actigraphy data using a least-squares regression model. The mesor (24-hour adjusted mean value or y-intercept), amplitude, and acrophase (time of day for peak activity) were the circadian activity rhythm parameters obtained from the regression model.49
The autocorrelation coefficient for a 24-hour rhythm was obtained from the Action 4® software program.
All calculations used actual values. Adjustments were not made for missing data. Therefore, the cohort for each analysis was dependent on the largest set of available data across groups. A p-value of <0.05 was considered statistically significant.