PSYC 13015 CQU Sleep Schedule and Daytime Functioning Paper

PSYC 13015 CQU Sleep Schedule and Daytime Functioning Paper

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NEW FILE. DATASET NAME DataSet1 WINDOW=FRONT. SAVE OUTFILE=’\\rmit.internal\USRHome\sh6\s3503796\MiniReport.sav’ /COMPRESSED. DATASET ACTIVATE DataSet1. SAVE OUTFILE=’\\rmit.internal\USRHome\sh6\s3503796\MiniReport.sav’ /COMPRESSED. EXAMINE VARIABLES=ESS BY Sleep /PLOT BOXPLOT HISTOGRAM NPPLOT /COMPARE GROUPS /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. Explore Notes Output Created 07-OCT-2019 02:02:53 Comments Input Data \\rmit.internal\USRHome\sh6\ s3503796\MiniReport.sav Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data 35 File Missing Value Handling Definition of Missing User-defined missing values for dependent variables are treated as missing. Cases Used Statistics are based on cases with no missing values for any dependent variable or factor used. Syntax EXAMINE VARIABLES=ESS BY Sleep /PLOT BOXPLOT HISTOGRAM NPPLOT /COMPARE GROUPS /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. Resources Processor Time 00:00:04.94 Elapsed Time 00:00:02.47 [DataSet1] \\rmit.internal\USRHome\sh6\s3503796\MiniReport.sav Sleep Case Processing Summary Cases Valid Sleep ESS N Missing Percent N Total Percent N Percent 1 14 100.0% 0 0.0% 14 100.0% 2 11 100.0% 0 0.0% 11 100.0% 3 10 100.0% 0 0.0% 10 100.0% Descriptives Sleep ESS 1 Statistic Mean 5.6429 95% Confidence Interval for Lower Bound 4.3106 Mean Upper Bound 6.9751 5% Trimmed Mean 5.6032 Median 6.0000 Variance Std. Deviation Minimum 5.324 2.30742 2.00 Std. Error .61668 Maximum 10.00 Range 8.00 Interquartile Range 4.25 Skewness .072 .597 -.595 1.154 6.6364 .74190 Kurtosis 2 Mean 95% Confidence Interval for Lower Bound 4.9833 Mean Upper Bound 8.2894 5% Trimmed Mean 6.6515 Median 7.0000 Variance 6.055 Std. Deviation 2.46060 Minimum 3.00 Maximum 10.00 Range 7.00 Interquartile Range 5.00 Skewness .021 .661 -1.318 1.279 12.1000 .97125 Kurtosis 3 Mean 95% Confidence Interval for Lower Bound 9.9029 Mean Upper Bound 14.2971 5% Trimmed Mean 12.1111 Median 12.0000 Variance 9.433 Std. Deviation 3.07137 Minimum 7.00 Maximum 17.00 Range 10.00 Interquartile Range 5.25 Skewness -.031 .687 Kurtosis -.635 1.334 Tests of Normality Kolmogorov-Smirnova Sleep ESS Statistic df Shapiro-Wilk Sig. Statistic df Sig. 1 .160 14 .200* .955 14 .642 2 .131 11 .200* .939 11 .505 10 .200* .977 10 .948 3 .140 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction ESS Histograms Normal Q-Q Plots Detrended Normal Q-Q Plots GRAPH /ERRORBAR(CI 95)=ESS BY Sleep. Graph Notes Output Created 07-OCT-2019 02:11:30 Comments Input Data \\rmit.internal\USRHome\sh6\ s3503796\MiniReport.sav Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data 35 File Syntax GRAPH /ERRORBAR(CI 95)=ESS BY Sleep. Resources Processor Time 00:00:00.78 Elapsed Time 00:00:00.28 ONEWAY ESS BY Age /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Oneway Notes Output Created 07-OCT-2019 02:15:04 Comments Input Data \\rmit.internal\USRHome\sh6\ s3503796\MiniReport.sav Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data 35 File Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each analysis are based on cases with no missing data for any variable in the analysis. Syntax ONEWAY ESS BY Age /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Resources Processor Time 00:00:00.00 Elapsed Time 00:00:00.02 Warnings Post hoc tests are not performed for ESS because at least one group has fewer than two cases. ANOVA ESS Sum of Squares df Mean Square Between Groups 300.267 20 15.013 Within Groups 179.333 14 12.810 Total 479.600 34 F 1.172 Sig. .387 Sleep 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 Age ESS 31 39 42 21 18 32 23 34 41 33 41 42 27 42 33 39 18 36 23 30 27 31 35 25 19 31 20 29 33 53 55 32 18 16 25 3.00 3.00 2.00 3.00 5.00 6.00 6.00 7.00 5.00 6.00 8.00 7.00 8.00 10.00 4.00 6.00 3.00 4.00 5.00 7.00 8.00 10.00 7.00 9.00 10.00 7.00 9.00 10.00 11.00 13.00 15.00 11.00 13.00 17.00 15.00 Help File 4 (Week 10-Week 11) Practical 8-Week 10 Analysis of Variance (ANOVA) This help file contains instructions on (1) conducting a one-way between-subjects ANOVA + post-hoc tests using SPSS, (2) interpreting the relevant outputs, and (3) reporting the test results in APA format. The contents of this help file are as follows: • Tests of Normality and Homogeneity of Variance • One-Way Between-Subjects ANOVA + Post-Hoc Pairwise Comparisons The examples presented in this help file were developed using the data on a subset of the treatment groups in the SPSS data file, “Therapy”. Before testing for the differences between means (e.g., t test, analysis of variance), it is appropriate to obtain descriptive statistics for each group and to check if assumptions underlying the test of means are tenable (i.e., the improvement within each group come from normally distributed populations and population variances of the groups are equal). To get descriptive statistics (i.e., M, SD, skewness) and test for normality of distribution and homogeneity of variance, click on ANALYZE → DESCRIPTIVE-STATISTICS → EXPLORE. In the dialog box, highlight Improvement and move it to the DEPENDENT LIST box by clicking on the > button. Here, we need to ensure that the assumption of normality was met for students from both genders. Therefore, move the independent variable, Therapy, to the FACTOR LIST. Click on PLOTS. In the dialog box, ask for NORMALITY PLOTS WITH TESTS. Uncheck the box for stem-and-leaf, and ask instead for a HISTOGRAM. To check for the assumption of homogeneity of variance, click on POWER ESTIMATION. Click on CONTINUE/OK. (see help file 2 for more information) Practical 9-Week 11 To conduct a one-way ANOVA, click on ANALYZE – COMPARE-MEANS – ONE-WAY ANOVA. Transfer the variable Improvement to the DEPENDENT LIST box and Therapy to the FACTOR box. FACTOR is just another name for independent variable. To request for the results of the post-hoc analysis, click on POST-HOC and select Tukey. If the assumption of homogeneity of variance was violated, you can select the Games-Howell test to adjust for the non-equal variances. The resulting outputs will be similar to that of the usual ANOVA with Post-Hoc analysis. Below is the summary output of the One-Way ANOVA comparing the mean improvement across the CBT, Family Therapy and Wait-List conditions. (Read the lecture’s slides Week 9-Week 10) Post Multiple Comparisons (Read the lecture’s slides Week 9-Week 10) PSYC13015 Practical and Written Assessment (worth 40%) Background There is a clear relationship between insufficient sleep and poorer daytime functioning in the population. Short sleep duration is consistently linked, for example, with poorer cognitive performance, and deficits in mood and feelings of well-being. Sleep schedule variability refers to the consistency of bed time and wake time. Individuals who differ night to night in the time they go to bed, and the time they wake, have greater sleep schedule variability than individuals with consistent bed and wake times. Recent studies in children suggest that sleep schedule variability impacts on daytime functioning. Sleep schedule variability has primarily been considered in adult populations when a clinical problem (such as insomnia) is present. In these populations, sleep variability is associated with a variety of mental health variables (e.g. depression). However, few studies have specifically looked at sleep schedule variability in a representative, non-clinical sample of adults. This makes it difficult for us to know whether sleep schedule variability is related to poor daytime functioning in healthy, non-clinical adults. We also don’t have a clear idea of whether any demographic variables are associated with sleep schedule variability. With this in mind, our hypothetical project will attempt to answer the following questions: 1. Does sleep schedule variability affect daytime functioning in healthy adults? 2. Does the relationship between sleep schedule variability and daytime functioning stay the same, or change after adding age as a covariate? What do I need to do now? 1. Enter the attached data set into SPSS 2. Run appropriate screening and data cleaning so you know what your variables look like before analysing (don’t forget – you will need to mention what you did in your results section, and ensure that your data meets the requirements for the statistical analyses you are conducting). 3. If your data does not meet all the requirements of your chosen test then the advice is to still run the proposed analysis, but point out the assumption limitations in the report. 4. Provide (very briefly) all background information about your sample in the Results section. Note in a journal paper this might often go in the Methods section which you are not required to write. 5. Write a hypothesis for each research question. We would like to see these in your Results section (in a full report these would go in the Introduction section). You could base your hypotheses on theories and findings from sleep psychology research. It does not necessarily matter if the data support, or do not support your two hypotheses. 6. Conduct the analysis required to answer the first research question. 7. Conduct the analysis required to answer the second research question When you are working on the second research question we would like you to consider covariates. The reason for this is the existing literature tells us that in clinical cohorts, age may affect the outcome variable (daytime functioning). HINT: See the last page of Field’s textbook to help you work out the appropriate analyses for your assessment. HINT: We are not looking for a regression analysis for this assessment. 8. Prepare the necessary tables and figures (you may not need both) to complete your Results section to APA standards. Use writing style guidelines given in lectures, Zoom sessions, journal papers and remember to report effect sizes where applicable. 9. Write a discussion of your findings in a separate Discussion section, making sure you use at least three real journal articles from the research area to support your discussion 10. Unless stated otherwise, e.g. through extra guidance on Moodle forums adhere to APA formatting style (especially with respect to reporting statistical findings) wherever possible and include a Reference section. 11. You are required to screen the data for sources of bias and comment briefly on what you find. However, there is no requirement to actually amend the dataset, e.g. remove outliers, transform data etc. 12. Include pasted syntax (as per the portfolios and the first main assignment) for all analyses you have undertaken and used in your report. This includes assumption testing in this assessment. Put this at the bottom of your report. What do I need to know about the variables? 1. ‘Daytime Functioning’ has been measured using the Epworth Sleepiness Scale (ESS). This measure gives an idea of general level of sleepiness in an individual, with the general idea being that higher sleepiness (reflected by a higher score on the ESS) may represent poorer daytime functioning. I have provided a paper (Shahid, Shen & Shapiro, 2010) which gives you some background information about the ESS (you don’t need to worry about the other measures covered in this paper). 2. Participants were classified as having low, moderate, or high sleep schedule variability depending on how many minutes (on average) they varied their bed times and wake times. 3. You will need to familiarize yourself with the early chapters of the Field (2013, 2018) textbook to identify the types of variables (continuous, categorical, etc.) you have in your data set. This will directly affect the analyses you run for this assessment. There is a detailed Rubric for the assessment showing how the 100 marks will be distributed, but the broad weighting for each section of the assignment is as follows: • Appropriate selection and application of the relevant statistical procedures and techniques, including any assumption testing and identification of bias (15%) • Appropriate presentation and interpretation of the results, including the use of well-formatted graphs and tables as required (30%) • Appropriate critical discussion and explanation of the results in light of the research question (s), including an acknowledgement (if required) of any strengths and weaknesses of the analysis (45%) • Written expression and use of references (10%) Word count: 1300 +- 10%. Note: Whilst there are no set penalties for being excessively over or under the suggested range, assignments with less than 1000 words or greater than 1500 words will be looked at more closely and may lose some marks in the written expression section of the rubric. Data Set Sleep Variability Low Moderate Age 31 39 42 21 33 39 18 36 23 30 27 ESS Score 3 3 2 3 4 6 3 4 5 7 8 Sleep Variability Low Moderate Age 18 32 23 34 41 33 41 42 27 42 31 ESS Score 5 6 6 7 5 6 8 7 8 10 10 High 35 25 19 31 20 29 33 53 55 7 9 10 7 9 10 11 13 15 High 18 26 24 26 28 32 18 26 25 Note the above are values for one dataset, e.g. there are 14 participants in the Low condition, 16 in the Moderate and 10 in the High condition. 14 11 14 15 19 11 13 17 15 Poor Results Choose the right statistical test (5 MARKS) Chose the wrong statistical test (0) Results Assumptions and bias (15 MARKS) Did not discuss assumptions, or did so poorly (0-6) Results Interpretation (20 MARKS) Results APA style (5 MARKS) Fair Good Very good Chose a test that is not the one desired, but could plausibly be used (3) Chose the correct statistical test (5) Discussed some assumptions but not others or made an error (9-11) Discussed all assumptions, but made an error (12-14). Some suggestions made for correcting bias if present. Discussed all assumptions without error. Excellent suggestions made for correcting bias if present. (15) Interpreted output The output was interpreted incorrectly (0-8) correctly except for some key elements (10-12) The output was interpreted correctly, but there was ambiguity in some parts (1315) Output was generally interpreted correctly with just minor clarity issues (16-18) Output was interpreted correctly and this was very clearly conveyed (19-20) APA style was absent (0-1) APA style was poor (2) APA style was generally good APA style was good with only 1 error (4) with 2 or more errors (3) APA style was excellent (5) Results were restated with error (5) Some results were restated Results were restated but but not all (6) with some ambiguity in expression (7-8) Results were clearly and accurately restated (9-10) Discussion Summarise Findings were not summarised (0-4) main findings (10 MARKS) Only discussed one or two assumptions, or made an error (7-8) Excellent Discussion Interpret results in line with the literature (20 MARKS) Results were discussed in line with the literature but little or no interpretation offered (10-12 MARKS) Results were discussed in line with the literature and interpretation offered but this was not critical (13-15 MARKS) Results were somewhat critically discussed and interpretation offered. Discussion was good to very good (16-17 MARKS) Results were critically discussed in line with the literature. Discussion was outstanding (18-20 MARKS) Discussion Implications (5 Implications of the results was not MARKS) discussed (0) Discussion of implications was poor (1-2) Discussion of implications was satisfactory (3) Discussion of implications was good (4) Discussion of implications was outstanding (5) Discussion Limitations/Future research (5 MARKS) Limitations and future research were not discussed 0) Only limitations or future research were discussed (12) Both limitations and future Discussion of both research were discussed and limitations and future this was satisfactory (3) research was good (4) Discussion of limitations and future research was outstanding (5) Discussion Conclusion There was little or The main findings were no conclusion (0-1) summarised but not implications (or vice versa) (2) The conclusion was generally The conclusion was good satisfactory (3) (4) The conclusion was outstanding (5) There was no reference list (0) All references were included but there were some major violations of APA style (3) All references were included and were in excellent APA style (5) (5 MARKS) Other References (5 MARKS) There was little or no interpretation of the results offered (0-9 MARKS) Not all references were included in the reference list (1-2) All references were included but there were some minor violations of APA style (4) Other Written expression (5 MARKS) The written expression was poor (0-1) The written expression was satisfactory however there were many grammatical/spelling errors (2) The written expression was satisfactory with some grammatical/spelling errors (3) PSYC13015 – Marking Rubric Mini-Report (2019) [Live] The written expression was clear but there were some minor grammatical/clarity issues (4) The written expression was outstanding (5) Journal of Psychosomatic Research 69 (2010) 81 – 89 Measurements of sleepiness and fatigue Azmeh Shahid a,b,⁎, Jianhua Shen a,c , Colin M. Shapiro a,b,c a Department of Psychiatry, University Health Network, University of Toronto, Toronto, Canada b Youthdale Treatment Center, University of Toronto, Toronto, Canada c Sleep Research laboratory, Toronto, Canada Received 17 July 2009; received in revised form 9 April 2010; accepted 9 April 2010 Abstract Sleepiness and fatigue are terms commonly used in clinical practice and research. At times sleepiness and fatigue are used interchangeably; however, each of them has distinct implications for diagnosis and treatment. The objective of this article is to review the psychometric properties of the measurements of sleepiness and fatigue. Although there are objective and subject measures to evaluate sleepiness, only rating scales are available to assess fatigue. Further research should be directed toward exploring the potential mechanisms underlying the measurements of sleepiness and fatigue. Establishing objective assessing instruments to evaluate fatigue and clarifying the relationship between objective and subjective assessments of sleepiness are crucially needed. © 2010 Elsevier Inc. All rights reserved. Keywords: Fatigue; Measurement; Rating scale, Sleepiness Introduction Sleepiness and fatigue are terms commonly used in both clinical practice and research literature. Both sleepiness and fatigue are ubiquitous phenomena. Sleepiness and fatigue negatively effect daily functioning, and patients who have these feelings are distressed. Although sleepiness and fatigue are two different and distinct entities, many patients and unfortunately many medical practitioners are unaware of the complexity and heterogeneity of these symptoms. This may be because that some patients use the terms tired, sleepy and fatigued interchangeably and it is difficult to tease apart whether the primary issue is fatigue or sleepiness. The two complaints have distinct implications for clinical diagnosis and ⁎ Corresponding author. Toronto Western Hospital Bathurst St 7th floor Main Pavilion Rm 427 Toronto ON, Canada M5T 2S8. Tel.: +1 416 603 5273; fax: +1 416 603 5292. E-mail addresses: azmeh.shahid@uhn.on.ca, azmehs@hotmail.com (A. Shahid). 0022-3999/10/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2010.04.001 treatment. These two symptoms are particularly common in the psychosomatic field. It is beyond the scope of this primarily methodological review to discuss the implications of the distinctions but the interested reader is referred to the following references [1–3]. Currently, there are objective methods measuring sleepiness. A number of rating scales are being used to subjectively assess sleepiness and fatigue. The objective of this review is to assess the psychometric properties of the objective and subjective measurements available on the topic of sleepiness and fatigue. A broader compilation of scales relevant to sleep medicine will be published in the book titled “One Hundred Sleep Related Scales” [4]. Sleepiness Generally, sleepiness means an increased propensity to doze off or fall asleep; it may be related to a low arousal level [5]. Sleepiness is also defined as a tendency to fall asleep. Sleepiness may be affected by different conditions, such as medical problems, psychiatric diseases and primary sleep disorders. When sleepiness occurs at an inappropriate time or 82 A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 an atypical situation, it becomes pathological. For example, excessive daytime sleepiness (EDS) is an important feature of narcolepsy. Sleepiness may be influenced by mood, motivation, autonomic and physiological changes, and fatigue and sleep requirement of the individual [6]. Because of this, it is difficult to obtain an accurate prevalence of EDS. The reported range of the prevalence of EDS is between 3% and 22.6% in different studies [7]. Sleepiness is a multifactorial phenomenon, and it may possess ‘Trait’ and ‘State’ features [8]. Trait features are those could be influenced by individual personality and person specific features; they are usually long term and stable; while state features are short term. Therefore, authors have recommended that EDS should be measured using various rating techniques and it should be assessed on a sound empirical basis. Because sleepiness increases the risk of the occupational injuries and automobile accidents, it is important to assess and treat daytime sleepiness [9,10]. There are objective and subjective measures to assess excessive daytime sleepiness. Objective measurements of sleepiness Multiple Sleep Latency Test The Multiple Sleep Latency Test (MSLT) was first described by Carskadon and Dement [11]. The underlying background of establishing the MSLT is that sleepiness is a physiological need for sleep, while increasing tendency to fall asleep indicates greater sleepiness. During the test, an individual is required to lie down in a dark room to fall asleep. An MSLT includes four or five sessions; each session lasting 20–30 min and performed at two hourly intervals. Sleep latency is measured by standard electrophysiological means and is defined as the time elapsed from lights out to the first epoch of any stage of sleep. The two important outcome variables are the mean sleep onset latency and the number of rapid eye movement sleep episodes that occur. The validity and reliability of the MSLT have been documented in several clinical and experimental situations [12]. The inter-rater reliability and test–retest reliability are acceptable. One study found that over a time span of 4 and 14 months, the test–retest reliability of MSLT was good to excellent (r values were between 0.65 and 0.97) [13]. MSLT is a useful tool to assess sleepiness induced by various conditions, including acute and partial sleep deprivation, circadian rhythm disorders, disrupted sleep, sleep apnea, and narcolepsy, use of hypnotics and alcohol usage and idiopathic hypersomnolence. A mean sleep latency of 5 min or shorter on the MSLT represents severe pathological sleepiness [14–17] (please see Table 1). The measurement cutoff points of the MSLT affect its sensitivity and specificity to assess EDS. The sensitivity and specificity were 80.9% and 89.8%, respectively, when the cutoff of 5 min was used; 94.5% and 73.3%, respectively, when the cutoff was raised to 8 min; and 52% and 98.3%, respectively, when the cutoff was 3 min or shorter. Clinically it is useful to think of severe, moderate and mild daytime sleepiness, but little effort has been made to validate these distinctions. Although MSLT is a reliable, valid and accurate test, it may fail to measure some aspects of sleepiness [8]. For instance, MSLT may not separate an individual’s ability to fall asleep (sleep propensity) from his need to sleep. This may be because the MSLT ignores the effects of an “arousal factor” on sleepiness. An “arousal factor” is generated internally and it is independent of the environment [18]. Maintenance of Wakefulness Test The major function of the Maintenance of Wakefulness Test (MWT) is to measure the strength of the arousal system. While some may view MWT as a useful tool in estimating daytime sleepiness we would not share this view. Although there are some correlations between sleepiness and decreased the level of alertness, sleepiness is not the reverse of alertness. Clinically, many patients who have significant sleepiness, have little impairment of alertness. During the process of doing the MWT, subjects are instructed to sit in a dimly lit room for 30 min and attempt to stay awake. The duration may be set at 20, 30 or 40 min. We have preferred 30 min as a compromise between a ceiling effect (with 20 min) and the onerous 40 min version. It has also facilitated performance tasks at the midpoint of successive tests. Sleep onset is defined as the first appeared three consecutive 30 second epochs of stage 1 sleep or the first epoch of any other sage of sleep [19]. Based on studies we and others have completed [19], using the 30 min protocol, a mean sleep latency of the MWT is determined by the mean value of the sleep onset latency on the four naps. A mean sleep latency between 19 and 24 min indicates mild impairment of alertness; between 13 and 18.9 min indicates moderate impairment of alertness; and b12.9 min indicates severe impairment (Table 1). When the cutoff of the MWT was set at 12 min, the sensitivity was 84.3%, and the specificity was 98.4%. The MWT has been used to evaluate the response of pharmacological treatment in narcolepsy [20,21]. Subjective measures of sleepiness Subjective measures of sleepiness mainly rely on rating scales. The majority of the rating scales are self-evaluated instruments. Using a rating scale to measure sleepiness is inexpensive, simple and less time-consuming [8]. Selfreported scales have some drawbacks. These include unintended bias and purposeful falsification. Sleepiness rating scales broadly include two categories. The first category is used to assess “State” sleepiness, such as the Stanford Sleepiness Scale (SSS), Karolinska Sleepiness Scale (KSS), and Visual Analogue Scales. These scales are used to measure short term changes in sleepiness. Other tests measure a global level of sleepiness, most like a “Trait” A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 83 Table 1 Features of the instruments for measuring sleepiness Scale Name MSLT MWT KSS Reference What is assessed (construct) 12, 13,16–18 How quickly one falls asleep when asked to do so PSG recording Investigator administered Subjects with daytime sleepiness 19–21 Measures strength of the arousal system PSG recording Investigator administered Subjects with impaired alertness Item number Scale type Target population Internal consistency (α) Test–retest 0.65–0.97 reliability (r) Concurrent Gold standard validity (r) Cut off point 10–15 min, mild sleepiness or normal; 5–10 min moderate sleepiness; b5 min severe sleepiness ESS SSS SWAI DSS 32–34 18, 35–37 Situational General sleepiness level of sleepiness 23–25,29,31 Sleepiness in patients with sleep complaints 39–42 Multidimensional measurement of sleepiness 38,43,44 Originally developed as a part of the SWAI 9 items 7 levels Likert 59 items 9 semi-continuous scale Daytime sleepiness 8 items 4 points ordinal scale: Daytime sleepiness 0.89 (for EDS component) 0.71 8 items 4 points (0–3) Patients with sleep complaints 0.88 – – – 0.822 Tested with EEG variable 19–24 min, mild; 13–19 min, moderate;≤2.9 min severe impairment of alertness Total score is between 0–24; 10 or over indicates abnormal or pathological sleepiness aspect of sleepiness. The Epworth Sleepiness Scale (ESS) and Sleep Wake Activity Inventory (SWAI) belong in this category. Stanford Sleepiness Scale The SSS was developed by Hoddes et al. [22]. It is one of the most widely used subjective sleepiness measuring instrument. This self-rating scale is used to quantify sleepiness levels in patients with sleep complaints (Table 1) The SSS is a Likert-type scale with seven vigilance levels. Subjects are asked to indicate which level best describes their current state. Studies have suggested that the SSS is sensitive to reveal sleepiness induced by sleep deprivation [23,24]; however, it is not so sensitive with patients suffering from sleep disorders and tending to deny sleepiness [25,26]. This may partially explain why the SSS is not sensitive to distinguish between sleep apnea subjects and normal sleepers, even though for patients with narcolepsy it has been a useful subjective sleepiness measurement scale [27]. John [28] had indicated that the SSS is not a valid measure to assess sleep propensity. He investigated the concurrent validity of the SSS with performance on mental tasks and evaluated whether the SSS shows any changes in sleepiness with sleep loss. The results indicated that the mean SSS ratings positively correlated (r=0.68) with those of performance on the Wilkinson test, which was developed by Wilkinson (1968), and it is used to measure performance following a reduction of sleep time by two hours. The SSS – Tested with MSLT Tested with MSLT 1 indicates that the behavior was always present, 9 meant never present and 5 meant sometimes present. Total score is from 0 to 24; scores 0–10: normal (no significant skew); ≥11: significant skewdness. ratings had a lower correlation with the performance on a memory test (r=0.47). Following sleep deprivation, the mean SSS ratings were significantly lower than the corresponding ratings with no deprivation. This may be because that sleepiness is a multi-dimensional phenomenon [29]. Additionally, the SSS may significantly correlate with fatigue. This makes the usage of the scale complicated [30]. Karolinska Sleepiness Scale The KSS was developed by Akerstedt and Gillberg [31]. This nine-point scale measures the subjective level of sleepiness at a particular time during the day. On this scale, subjects indicate which level best reflects the psychophysical sate experienced in the last 10 min. The KSS is a measure of situational sleepiness. It is sensitive to fluctuations. Scores on the KSS increase with increased periods of wakefulness and strongly correlate with the time of the day [32] (Table 1). Kaida et al. have investigated the validity of the KSS and found that the KSS was highly correlated to electroencephalography (EEG) and behavioral variables. These results indicate that the validity of the KSS is high [33]. However, because the scores of the KSS vary according to earlier sleep, time of day and other parameters, it is difficult to deduce its test–retest reliability. The KSS is useful in assessing the changes in response to environmental factors, circadian rhythm, and effects of drugs. Because the KSS is not a measure of Trait sleepiness, it has not been widely used for clinical purposes. 84 A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 Epworth Sleepiness Scale The ESS was developed by Johns [34]. The background of establishing the ESS was driven from the observations of the nature and occurrence of daytime sleepiness. This simple self-administered scale is used to measure general level of daytime sleepiness. The ESS has a set of eight situations commonly encountered in daily life. Some of them are very soporific, others less so. On a scale of 0–3, subjects rate how quickly they would fall asleep or doze off in each of the eight situations. The total score is between 0 and 24. A score of 10 or higher indicates abnormal or pathological sleepiness. The ESS tries to deal with the fact that people have different daily routines; some of these routines promote sleep while others are more activating (Table 1). According to Johns [18], the ESS is the most discriminative test, so far, on average sleep propensity. One study by Johns showed that the ESS was the more discriminative test compared with MSLT and MWT. The ESS is sensitive to the changes produced by treatments for sleep apnea [34–36]. Although using the ESS we may discriminate normal from pathological subjects, we need to develop good objective tests to measure sleep propensity in different situations in which the subjects posture and activity inter alia is also taken into consideration [18]. Johns has investigated the reliability and internal consistency of the ESS. Over a period of 5 months, the Pearson correlation between the scores of two time points was 0.822, indicating that the test–retest reliability of the ESS was high. The ESS has a high level of internal consistency with a Cronbach’s alpha of 0.88 (Table 1). normal levels of sleepiness. The scores of the SWAI are generally improved following normalization of sleep disordered breathing. Further studies are needed to establish the reliability of the EDS factor in the absence of an intervening variable over time. Sleep Wake Activity Inventory The SWAI was developed by Rosenthal [37]. The purpose of developing the SWAI was to establish a measure to assess multidimensional components of sleepiness. The items on the SWAI were derived from clinical experience and an appraisal of earlier self-report scales [38–41]. The final version of the SWAI consists of 59 items. A Likert-type scale is used for each item, which includes a 1–9 semicontinuous scale. While performing the assessment, subjects are asked to circle the number that best describes them: 1 indicates that the behavior is always present, 9 means never present and 5 means sometimes present. Subjects are asked for their response over the last 7 days (Table 1). A high Cronbach’s alpha (0.89) is obtained for the factor of Excessive Daytime Sleepiness (EDS). Cronbach’s alpha values of other factors are moderate: 0.72 for Psychic Distress, 0.76 for Social Desirability, 0.71 for Energy Level, 0.69 for Ability to Relax, and 0.69 for Sleep. The SWAI is the first self-report instrument to assess sleepiness that has been shown to be sensitive to various sleepiness levels. The MSLT was used for validating the SWAI, and the EDS factor of the SWAI showed a high predictive value of mean MSLT scores. The EDS scores on the SWAI are helpful to differentiate pathological sleepiness from diagnostic gray zone and FACES The FACES is a 50-item adjectival checklist which is useful in assessing both sleepiness and fatigue at the same time. It is discussed in more detail in the section on scales for fatigue in this article [44]. Daytime Sleepiness Scale and Nocturnal Sleep Onset Scale The Daytime Sleepiness Scale (DSS) and Nocturnal Sleep Onset Scale (NSOS) are self-report measures originally part of the SWAI [37]. In the study performed by Johnson et al., the psychometric evaluation of the DSS and NSOS was assessed in a representative community sample [42]. A DSS has eight items. Items are rated on the base of the two week period preceding the interview. Each item has a four-point ordinal scale: never, rarely, sometimes and often. The score on the DSS is from 0 to 24. Scores 0–10 indicate “normal” (no significant skew), while scores of 11 or higher indicate significant positive skew (Table 1). The revised eight-item DSS has a good internal consistency with a Cronbach’s alpha of 0.71. The construct validity of the DSS is acceptable. There is a similarity on the thresholds of the MSLT and the DSS. It is generally agreed that the threshold for pathological sleepiness for MSLT b5 min describes 10–15% of normal healthy volunteers, while the proposed threshold in DSS identifies 17% of the population as potentially having abnormal levels of daytime sleepiness [42,43]. Revised NSOS includes two items. Rating of the items is based on the two week period preceding the interview. As in the DSS, each item of the NSOS has a four-point ordinal scale: never, rarely, sometimes and often. The NSOS has a reasonable internal consistency with a Cronbach’s alpha of 0.58. Toronto Sleepiness and Fatigue Scale The Toronto Sleepiness and Fatigue Scale (TSFS) was developed by Shen et al. [45]. The objective of this study was to develop a questionnaire to measure sleepiness and fatigue concurrently. This 10-item questionnaire includes sleepiness (TSFS-S) and fatigue assessments (TSFS-F) related to a common stem. It was showed that the Pearson correlation coefficients (r) between scores on the TSFS and those of the validated scales measuring sleepiness and fatigue, including the Epworth Sleepiness Scale and Fatigue Severity scale were between 0.54 and 0.69 on Day 0 and between 0.46 and 0.71 on Day 7. Cronbach’s alphas were between 0.84 and 0.87 for the TSFS-S and TSFS-F. Intraclass correlation coefficients were 0.87 for the TSFS-S and 0.80 for the TSFSF. The TSFS has been shown to be a useful instrument to measure sleepiness and fatigue simultaneously and may be particularly useful in trying to discern if a treatment influences fatigue or sleepiness to a greater or lesser extent. A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 The problem hitherto is that these were separate scales but changes in a sleepiness scale would not be accurately compared to changes in a fatigue scale. Fatigue Fatigue is common in physical and psychiatric disorders [46]. Symptoms of fatigue are commonly reported in patients with depression, chronic fatigue syndrome, HIV, cancer. Fatigue may be a side effect of a number of medication treatments [47]. Generally, fatigue is not specific in its presentation and symptomatology. Fatigue may be induced by physical, physiological and psychological causes; it often presents as a feeling of tiredness and exhaustion [48]. Fatigue is the most common symptom reported to the physicians. The prevalence rate of fatigue in primary care ranges between 7% and 45% [49]. This large difference in the prevalence rate may be because of a lack of a working definition for fatigue and differences in measurement techniques. Fatigue is a feeling of strain or exhaustion; it includes physiological fatigue and pathological fatigue. Physiological fatigue, or “normal fatigue,” is induced by daily activities it lasts a short period and is usually relived by rest [50]. Pathological fatigue is usually caused by a medical or emotional disorder or an intervention for a disease, and is more chronic in nature [51]. Due to lack of objective measures and assessment tools, fatigue is still poorly defined and measured [52]. Research is needed to develop “gold standard” to objectively measure fatigue. This will help to quantify and strategize the treatment of the patients who have fatigue. There are many subjective rating scales which have been developed to measure fatigue. The following eleven scales are commonly used to evaluate fatigue. These scales provide a wide range of tools which are of interest both in the mental health field and in the somatic domain. Brief Fatigue Scale The Brief Fatigue Scale (BFI) was developed by Mendoza and colleagues [53] to assess the severity of fatigue and the impact of fatigue on daily functioning in patients with cancer and during cancer treatment. The BFI assesses the severity of fatigue and the impact of fatigue on daily functioning in past 24 h. It has nine items; each item is rated on an 11-point Likert scale type. A global fatigue score can be obtained by averaging all the items. The BFI has no cutoff score. The authors have stated that the scale is easily understood and translated. Reported Cronbach’s alpha of the BFI ranges from 0.82 to 0.96, indicating that the internal consistency of the scale is high. The concurrent validity was established by comparing the scores of the BFI and those of the Profile of Mood Status (POMS)-Fatigue Subscale (POMS-F) and Functional Assessment of Cancer Therapy (FACT)-Fatigue Subscale 85 (FACT-F). The results showed that correlation between the scores of the BFI and the POMS-F and those between the BFI and the FACT-F are highly significant (Table 2). Although the BFI has been accepted as a reliable instrument to assess fatigue levels in cancer patients, it has not been validated in non-cancer patients. The test–retest reliability of the BFI has not been established. Functional Assessment of Cancer Therapy FACT was developed by Yellen et al. [54]. It includes a set of comprehensive questions used to measure health related quality of life in patients with chronic diseases, especially cancer. The general version of the FACT (FACT-G) has 34 items [54]. The FACT-F has 13 items, which has been validated and is used alone. The construct of the FACT-F is similar to those of the POMS-F, the POMS-Vigor Subscale and the Piper Fatigue Scale. The FACT-F is mainly used in cancer patients and has not been validated in other populations. The scale has high internal consistency with the Cronbach’ alpha of 0.93 and has excellent test–retest reliability with a correlation coefficient of 0.90 (Table 2). Fatigue severity scale The Fatigue Severity Scale (FSS) is a nine-item selfreport questionnaire. While scoring the items, respondents have to mark from one to seven, where “1” indicates no fatigue and “7” indicates severe fatigue. High scores indicate more fatigue. The FSS measures the impact of fatigue on functioning and behavioral aspects, rather than the intensity of symptoms related to fatigue [55]. The FSS is one of the most widely used fatigue evaluating scales. It has high internal consistency with Cronbach’s alpha between 0.81 and 0.89 [46]. It also has good test–retest reliability with a correlation coefficient of 0.84 and is sensitive to changes with time and treatment. The FSS has shown good concurrent validity (with Visual Analogue Scale, r=0.68) [55] and is able to distinguish patients with different diagnoses, such as systemic lupus erythematosus, multiple sclerosis and chronic fatigue syndrome, shift workers, and depression [51,55]. The scale may predict changes in fatigue over time. It is capable of showing clinical improvement with treatment. It is applicable in both clinical and research settings. Checklist Individual Strength The Checklist Individual Strength (CIS) is a 24-item questionnaire designed to measure several aspects of fatigue. It was developed by Vercoulen [56] for hospital studies. Each item on the scale is scored on a seven-point Likert scale. The CIS has four subscales: Subjective Experience, Concentration, Motivation and Physical Activity. It has 86 Scale Name BFI FACT-F FSS CIS FAI FIS CFS FSI MFI Reference What is assessed (construct) 54 Fatigue severity and impact on daily functioning in cancer patients and cancer treatment 47,52,53,56 Impact of fatigue on functioning 57 Fatigue in patients with chronic fatigue syndrome in hospital studies 58 Qualitative and quantitative aspects in patients with medical problems 59 Impact of fatigue on the quality of life 60 Severity of fatigue in patients with chronic fatigue syndrome 61, 62 Severity, impact and duration of fatigue 63,64 51 45 Multidimensional In cancer Characterize features of fatigue patient in different research aspects of sleepiness states Item number Scale type 9 Self-report 11–point Likert Cancer patients 55 Health related quality of life in patients with cancer and chronic diseases 13 Rater administered 9 Self-report 24 – 29 Self-report Likert 40 – 14 – 20 Responses on a Multidimensional Self-report continuum Cancer patients Fatigued Subjects 0.93 0.90 0.81–0.89 0.84 Chronic fatigue syndrome 0.96 – Medical disorder patients 0.70–0.92 0.29–0.69 – Extended – version of FSS Target population Internal consistency 0.82–0.96 Test–retest – reliability Concurrent validity Tested with POMS-F, FACT-F POMS-Fatigue and Vigor Subscales and PFS 0.87 – PFS FACES 41 Visual analogue scale Cancer research 50 Self-report Good – Chronic fatigue Women with syndrome breast cancer Cancer patients Fatigued patients 0.88–0.90 – 0.94 Weak 0.84 High – – – Compared within and between groups Moderate – A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 Table 2 Psychometric features of fatigue measurement scales A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 been validated for patients with chronic fatigue syndrome (CFS). The CIS has a good internal consistency with a Cronbach’s alpha of 0.96. It is able to discriminate fatigue levels in patients with multiple sclerosis, CFS and healthy controls. The test–retest reliability of the CIS has not been demonstrated (Table 2). Fatigue Assessment Inventory The Fatigue Assessment Inventory (FAI) has 29 items. It was developed by Schwartz [57] to assess both qualitative and quantitative aspects of fatigue in medical patients in whom fatigue is the major symptom (Table 2). The FAI has four subscales namely: Fatigue Severity, Situation Specificity, Psychological Consequences and Response to Rest/Sleep. In general, the FAI has good psychometric properties. It shares some items with those of the FSS and has good convergent validity. The FAI has a high correlation with the FSS (r=0.98). The test–retest reliability is moderate with correlation coefficients between 0.29 and 0.69. The internal consistency is reasonably high with Cronbach’s alpha between 0.70 and 0.92. The FAI has been validated in out-patients in neurological and rheumatologic clinics. It is able to distinguish healthy subjects from patients. Fatigue Impact Scale The Fatigue Impact Scale (FIS) was developed by Fisk [58] to assess the impact of fatigue on quality of life and to improve understanding of the effects of fatigue. The scale has 40 items and is intended to assess the impact of fatigue on cognitive, physical and psyschosocial functions. The FIS has a high internal consistency with a Cronbach’s alpha of 0.87. It moderately correlates with the Sickness Impact Profile (r=0.51). The FIS has been validated in patients with hypertension and multiple sclerosis (Table 2). Chalder Fatigue Scale The Chadler Fatigue Scale was developed to measure severity of fatigue in patients with chronic fatigue syndrome [59]. This 14-item scale has questions related to physical and mental fatigue. The Chadler Fatigue Scale can be scored by using a Likert scale; each item has four options: better than usual, no more than usual, and worse than usual and much worse than usual. The Chadler Fatigue Scale has been found to be reliable. The reported Cronbach’s alpha values of the scale are between 0.88 and 0.90. The Chadler Fatigue Scale is a valid estimator of change and is used to assess the symptom severity (Table 2). The scale is useful in detecting fatigue in epidemiological studies. However, it is recommended that, clinically, the scale should not be used alone, but as an adjunct measuring instrument. 87 Fatigue Symptom Inventory The Fatigue Symptom Inventory (FSI) was developed by Hann et al. [60]. This multidimensional scale is used to assess the severity, impact and duration of fatigue. The initial standardization of the inventory was performed in women who were diagnosed with breast cancer, and who were undergoing treatment for cancer, as well as those who did not have cancer. The FSI has a good internal consistency with a Cronbach’s alpha of 0.94. It is widely used in both male and female cancer patients, although its test–retest reliability is reported to be weak [61] (Table 2). Multidimensional Fatigue Inventory The Multidimensional Fatigue Inventory (MFI) was developed by Smets et al. [62]. This 20-item self-report instrument has five subscales: General Fatigue, Physical Fatigue, Mental Fatigue, Reduced Motivation and Reduced Activity. The MFI is one of the more comprehensive measures of fatigue used in cancer patients and it needs further development in order to be used in clinical settings [63]. The MFI has shown good internal consistency with a Cronbach’s alpha of 0.84 (Table 2). It has been tested for psychometric properties in cancer patients receiving radiotherapy, patients with CFS, psychological and medical students, army recruits and junior physicians. The convergent validity was established by comparison of the MFI with a Visual Analogue Scale measuring fatigue. The correlations of between and within group comparisons are generally acceptable (0.22brb0.78). Piper Fatigue Scale The Piper Fatigue Scale (PFS) was developed by Piper [50] and it was originally used in cancer patients for the purpose of research. The PFS is a 41-item Visual Analogue Scale representing the temporal, intensity, affective, and sensory dimensions of fatigue. The validation test was performed in 42 patients. The concurrent validity of the scale was moderate and the internal consistency was high (Table 2). However, the validity of dimensional structures has not been tested. Visual Analogue Scale for Fatigue The Visual Analogue Scale for Fatigue (VAS-F) is an 18item scale to measure fatigue and energy levels. The energy subscale has 5 items and the Fatigue subscale has 13 items. The VAS-F is easily understood and administered. Performing the VAS-F requires little in the way of reading skills and time. The VAS-F has good psychometric properties. The internal consistency of the scale and subscales are good with the values of Cronbach’s alpha between 0.91 and 0.96 [64]. The limitations are that subjects should have good motor and visual abilities and some 88 A. Shahid et al. / Journal of Psychosomatic Research 69 (2010) 81–89 subjects feel hesitant in using the extreme ends of the 100mm lines. In general our view would be that VAS are useful for longitudinal but not for cross-sectional purposes [65]. FACES The FACES is a 50-item adjectival checklist designed to characterize different aspects of sleep and fatigue states [44]. The abbreviation of FACES represents its five subscales: Fatigue, Energy, Consciousness, Energized and Sleepiness. The fatigue subscale has 15 items and the sleepiness subscale includes 10 items. It has good convergent and discriminate validities, indicating that the FACES is a promising self-report instrument for the measurement of sleepiness, fatigue and related subjective experiences at the same time (Table 2). Conclusion Sleepiness and fatigue are commonly seen in clinical settings, as well as in the general population. Accurately assessing sleepiness and fatigue is crucial for clinical understanding of patients and for research. There are objective and subjective instruments to measure sleepiness. However, only rating scales are available for measuring fatigue. In the listed 11 scales measuring fatigue, the top three commonly used are the FSS, CFS and FIS. 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