Abstract Objective Cognitive fatigue (CF) can be defined as decreased performance with sustained cognitive effort. The present study examined the interrelatedness of disease severity, fatigue, depression, and sleep quality in order to evaluate their predictive roles of CF in MS. Four theoretical models examining these variables were assessed. Methods Fifty-eight individuals with a diagnosis of MS were recruited. CF was measured by examining last third versus first third performance on the Paced Auditory Serial Addition Test (PASAT). The PASAT and self-report measures of fatigue, depression, and sleep quality were administered. Path analysis was used to evaluate each of the models. Results CF was correlated only with depression (r = .362, p = .006) and sleep quality (r = .433, p = .001). Sleep quality was the greatest significant independent predictor of CF (β = .433, t(1,55) = 3.53, p < .001), accounting for 17.3% of the total variance. The best fitting model showed sleep quality as the largest contributor to CF; however, depression played a smaller predictive role. Furthermore, depression emerged as the strongest predictor of sleep quality and fatigue. Disease severity weakly predicted depression. Conclusions Sleep quality is the most significant predictor of CF in MS. As such, sleep quality may be a treatable cause of CF. Sleep quality itself, however, accounted for only 17.3% of the variance in CF suggesting that other variables which were not formally assessed in this sample (e.g., anxiety, etc.) may also play a predictive role. Follow-up studies should evaluate how results may differ with a larger sample size. Cognitive fatigue, Multiple sclerosis, Neuropsychology, Fatigue, Sleep quality Introduction Multiple sclerosis (MS) is a demyelinating disease of the central nervous system with a wide variety of neurological symptoms typically involving the visual, motor, and autonomic systems. Beyond these primary symptoms, individuals with MS often report comorbidities associated with the disease, such as fatigue, depression, and sleep disturbances. These secondary symptoms are often highly prevalent and can negatively affect an individual’s quality of life (Amato et al., 2001; Lobentanz et al., 2004). Furthermore, individuals also frequently report a lack of mental energy or mental fatigue, hereafter referred to as cognitive fatigue (CF). While currently there is no universally accepted definition of CF, it can be defined as a decrease in, or inability to sustain, task performance throughout the duration of a sustained attention task (Bryant, Chiaravalloti, & DeLuca, 2004; Schwid, Covington, Segal, & Goodman, 2002). It should be noted that this may not be the only way to operationalize CF as it is likely a reflection of several underlying deficits (i.e., slowed processing speed, sustained attention deficits, etc.). Nonetheless, we chose to remain consistent with past work by defining CF in the context of decreased performance over time. Typically, the assessment of cognitive fatigue relies on self-report measures but these can present with limitations (Cohen et al., 2000; Schwid et al., 2003). An alternative is to assess cognitive fatigue during the performance of a sustained attention task, objectively quantifying CF as a decline in performance from the beginning to the end the task (Krupp & Elkins, 2000; Morrow, Rosehart, & Johnson, 2015; Schwid et al., 2003; Walker, Berard, Berrigan, Rees, & Freedman, 2012). The Paced Auditory Serial Addition Test (PASAT) has been shown to be a sensitive and valid measure to objectively quantify cognitive fatigue in MS (Morrow et al., 2015; Walker et al., 2012). As research has only just recently begun focusing on evaluating CF in MS, there is a need for a more comprehensive understanding of how other secondary symptoms of the disease interact with CF. Currently, there is a lack of theoretical groundwork that evaluates the relationships between these secondary symptoms and whether or not they can predict an individual’s susceptibility to cognitive fatigue. While the study of cognitive fatigue is becoming more predominant in the MS literature, no research to date has examined the interrelatedness of an objective measure of CF and other associated characteristics of the disease, or has established a theoretical model explaining these contributing factors. The goal of the present study, therefore, was to examine the interrelatedness of fatigue, depression, sleep disturbances, and cognitive fatigue in MS. In addition, the impact of physical disease severity was also examined. The current study attempted to replicate and expand upon the methodology employed by Strober and Arnett (2005). Whereas they evaluated predictors of the construct of “fatigue” in general (i.e., a construct based on self-report), the current study will evaluate which combination of variables best predict objectively measured cognitive fatigue. Past research has examined the association between self-reported fatigue and depression; however, results have been mixed. A significant association between fatigue and depression has been reported in some studies (Bakshi et al., 2000; Fisk, Pontefract, Ritvo, Archibald, & Murray, 1994; Ford, Trigwell, & Johnson, 1998), but not in others (Iriarte, Carreno, & de Castro, 1996; Vercoulen et al., 1996). In addition, sleep disturbances have been found to be significantly related to depression (Clark et al., 1992) as well as fatigue (Attarian, Brown, Duntley, Carter, & Cross, 2004). Though there is some evidence that self-reported fatigue, depression, and sleep disturbances may be related in MS, Strober & Arnett (2005) noted that the existing literature lacks any research which examines these three factors and their combined or interactive effects. At the time of their writing, research had only yet evaluated associations between two variables at a time, thus any higher level interactions between the variables were not being considered. In their own study, they examined four models designed to predict fatigue that included these three variables concurrently. Their four competing models were formulated based upon the presence of certain relationships among these constructs in MS samples and from findings in other studies that attempted to predict fatigue in similar disorders (e.g., systemic lupus erythematosus) (Huyser et al., 1998). The present study attempted to build upon the work of Strober and Arnett (2005) by incorporating the concept of cognitive fatigue into the statistical model. Whereas the focus of their work was on self-reported fatigue as the outcome variable, in the current study cognitive fatigue was the outcome variable of interest, and thus self-reported fatigue was considered as a possible predictor. One limitation of their study, which was addressed in the current work, concerns their measure of sleep disturbance. In their study a composite measure of sleep disturbances was derived from various measures as opposed to one comprehensive, and psychometrically sound, measure. In the current study, a previously validated measure of sleep disturbance (Pittsburgh Sleep Quality Index) was used in order to facilitate replicability in the future. The current study was exploratory in nature given the current lack of theoretical background in cognitive fatigue research to date. As such, there are several possible casual pathways involving the predictor variables. Four models were proposed based on suspected relationships between the variables and were tested with a focus on cognitive fatigue as the outcome variable. Methods Participants A total of 58 individuals with a confirmed diagnosis of clinically definite MS based on 2010 McDonald criteria (Polman et al., 2011) were recruited through the MS Clinic at the Ottawa Hospital. Those with probable MS or those with symptoms suggestive of MS without a clinically definite diagnosis were excluded. All individuals were fluent in English and presented with no other neurological, medical, or psychiatric condition which may have impaired cognition (besides MS and depression). Individuals with prior head trauma, learning disabilities, history of seizures or unexplained syncope, or who are currently using drugs (either legal or illegal) that may have an effect on cognitive function were excluded. In addition, those individuals who were currently experiencing an MS exacerbation were considered ineligible. Procedures The study was approved by the Ottawa Hospital Research Ethics Board. After undergoing informed consent procedures, participants completed a demographic interview. Individuals completed the PASAT as part of a comprehensive neuropsychological battery evaluating cognitive domains such as information processing speed, executive functions, and working memory, among others. In addition, self-report measures of depression, fatigue, and sleep disturbances were completed. The battery was fixed and as such the PASAT was administered at the same time for all participants. Measures Cognitive fatigue Paced Auditory Serial Addition Test (PASAT) – The PASAT is a measure of information processing speed and working memory in which participants are instructed to listen to a time-spaced series of single digit numbers (from 1 to 9) and add each number to the previous number presented. The individual must provide his/her response orally prior to the presentation of the next digit for the response to be considered correct. The speed at which the participant must process information during this task can be manipulated by presenting the series of digits at different rates, i.e., with varying inter-stimulus intervals (ISI). The PASAT has been shown to be a sensitive and valid measure to objectively quantify cognitive fatigue in MS (Morrow et al., 2015; Walker et al., 2012). Fatigue Modified Fatigue Impact Scale (m-FIS) – The m-FIS is a self-report measure of fatigue whereby individuals rate the extent to which fatigue has been a problem for them within the past month, including the day of testing (Learmonth et al., 2013; Téllez, Rio, Tintoré, Nos, & Galán, 2005). The m-FIS consists of 21-items and is composed of three subscales that describe how fatigue impacts upon cognitive (10 items), physical (9 items), and psychosocial functioning (2 items). Each of these items is given a score from 0 (no problem) to 4 (extreme problem) with resulting m-FIS total scores ranging from a minimum of 0 to a maximum of 84, with higher scores indicating more severe levels of fatigue. The m-FIS has been validated and has shown good psychometric properties in an MS patient population (Learmonth et al., 2013). Depression Patient Health Questionnaire – 9 (PHQ-9) – The PHQ-9 is a 10-item self-report questionnaire of depression. Individuals are asked to indicate how often over the last two-week interval they have been bothered by each of the first 9-items (e.g., Poor appetite or overeating; Feeling down, depressed, or hopeless). Each item is scored on a scale of 0 to 3, with 0 being “not at all” and 3 being “nearly every day”. From these nine items, a total score is calculated. Scores of 5, 10, 15, and 20 represent cut-off points for “mild”, “moderate”, “moderately severe”, and “severe” depression, respectively (Kroenke, Spitzer, Williams, & Lowe, 2010). The 10th item is a follow-up item that assigns weight to the degree to which any depressive problems noted have affected the patient’s level of functioning (Patten et al., 2015). Sleep quality Pittsburgh Sleep Quality Index (PSQI) – The PSQI is a self-rated questionnaire that assesses sleep quality and disturbances over a one-month time interval. Nineteen individual items generate seven “component” scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global total score. Disease severity Expanded Disability Status Scale (EDSS) – The EDSS is a method of quantifying disability in MS and monitoring change in an individual’s level of disability over time. This score is largely reflective of an individual’s physical level of disability. Scores range from 0 (no disability) to 10 (death) in 0.5 unit increments, with higher values representing higher levels of disability. Analyses The relationship between each of the independent variables was established first by examining Pearson correlation coefficients. Next, multiple regression analyses were conducted to examine the collective role that these variables played in predicting cognitive fatigue. A hierarchical regression entering the variables in the order of the magnitude of their zero-order correlations with cognitive fatigue was performed to determine whether each predictor accounted for significant independent variance in cognitive fatigue. Finally, path analysis was employed to test each of the various models. To examine each model, fit indices were chosen which take into consideration the relatively small sample size. In particular, the chi-square to degrees of freedom ratio (CMIN/df) was chosen over the chi-square. A ratio less than 2 suggested that the model was acceptable (Ullman, 1996). Additionally, the Comparative Fit Index (CFI) and the Incremental Fit Index (IFI), which address the issues of parsimony and sample size while taking the degrees of freedom into account, were used. Both these indices range from 0 to 1.0, with a value greater than .90 representing good fit (Byrne, 2001). Results All statistical analyses were conducted using SPSS Version 23 in conjunction with SPSS AMOS 23. Correlations Pearson correlation coefficients showed that depression was significantly correlated with both fatigue (r = .749, p = <.0005) and sleep quality (r = .694, p = < .0005). Sleep quality and fatigue were also correlated (r = .476, p = < .0005). With regards to cognitive fatigue, CF was related to depression (r = .362, p = .006) and sleep quality (r = .433, p = .001), but not to self-reported fatigue or disease severity. Regression Hierarchical regression entering the variables in the order of the magnitude of their zero-order correlations with cognitive fatigue showed that sleep quality was the greatest and only significant independent predictor of CF (β = .433, t(1,55) = 3.53, p < .001), accounting for 17.3% of the total variance. Path Analyses The four models tested are outlined in Fig. 1. Their corresponding fit indices can be found in Table 1. Fig. 1. View largeDownload slide Four models predicting cognitive fatigue. Fig. 1. View largeDownload slide Four models predicting cognitive fatigue. Table 1. Fit indices of the four competing models Model CMIN/df CFI IFI Model fit Model 1 5.54 0.661 0.695 Poor fit Model 2 5.36 0.675 0.707 Poor fit Model 3 0.234 1.00 1.00 Acceptable fit Model 4 0.176 1.00 1.00 Best fit Model CMIN/df CFI IFI Model fit Model 1 5.54 0.661 0.695 Poor fit Model 2 5.36 0.675 0.707 Poor fit Model 3 0.234 1.00 1.00 Acceptable fit Model 4 0.176 1.00 1.00 Best fit Note: CMIN/df = chi-square to degrees of freedom ratio; CFI = Comparative Fit Index; IFI = Incremental Fit Index. View Large Table 1. Fit indices of the four competing models Model CMIN/df CFI IFI Model fit Model 1 5.54 0.661 0.695 Poor fit Model 2 5.36 0.675 0.707 Poor fit Model 3 0.234 1.00 1.00 Acceptable fit Model 4 0.176 1.00 1.00 Best fit Model CMIN/df CFI IFI Model fit Model 1 5.54 0.661 0.695 Poor fit Model 2 5.36 0.675 0.707 Poor fit Model 3 0.234 1.00 1.00 Acceptable fit Model 4 0.176 1.00 1.00 Best fit Note: CMIN/df = chi-square to degrees of freedom ratio; CFI = Comparative Fit Index; IFI = Incremental Fit Index. View Large Model 1 theorized that sleep disturbances were caused by both disease severity and depression independently and that sleep disturbances in turn caused both self-reported fatigue and cognitive fatigue. It was thought that those who experienced greater physical disease severity (i.e., tremors, restlessness, etc.) and higher levels of depression would report lower sleep quality during the night. In turn, this lower quality of sleep would result in increased cognitive fatigue and greater levels of self-reported general fatigue. This model was a poor fit to the data. Model 2 differed from Model 1 in that it suggested that depression was caused exclusively by disease severity (i.e., those with greater physical MS symptomatology would have higher levels of depression) and that, similar to Model 1, depression, in turn, impacted sleep quality and subsequent fatigue and cognitive fatigue. This model was also a poor fit to the data. Model 3 was similar to Model 2 though instead suggested that self-reported fatigue was a result of depression (rather than sleep quality). This model theorized that depression had a greater impact on self-reported fatigue than did sleep quality (consistent with the correlations noted) and thus those with higher levels of depression would report more fatigue. This model had an acceptable level of fit, though was not the best model fit for the data. Model 4 was the best fitting of the competing models. This model builds upon Model 3 and suggests that cognitive fatigue is a result of not only sleep disturbances but that depression also plays a role as well, both in terms of how it relates to sleep disturbance and how it relates to CF more directly. The inclusion of depression as a direct predictor of cognitive fatigue was theorized by the assumption that those with higher levels of depression would be less motivated to perform the task (i.e., our measure of cognitive fatigue) and thus would show more evidence of cognitive fatigue as reflected by poorer task performance. Discussion The goal of the present study was to examine the interrelatedness of disease severity, fatigue, depression, sleep disturbances, and cognitive fatigue in an MS sample. Four competing models were developed and evaluated to examine how these disease characteristics might predict cognitive fatigue in MS. The relationships between the individual constructs within the models were developed based on previous evidence in the literature along with our own theoretical suppositions of how these variables might interact. Regression analyses revealed that sleep quality was the only significant predictor of CF and as such this relationship was designated in each of the models tested. Consistent with the literature, sleep quality and measures of self-reported fatigue were correlated in the current sample. It seems intuitive that those who would report more disturbances in their sleep would report higher levels of subjective fatigue. This relationship was evaluated in both Model 1 and Model 2. In addition, depression was correlated with sleep quality in the current sample and as such both Models 1 and 2 proposed that depression impacted sleep quality which further impacted cognitive fatigue. These models, however, both showed poor levels of fit. The association between sleep quality and self-reported fatigue was only moderate (factor loading of .47) and as such this association was not as strong in the current sample as was anticipated. This suggests that other factors may impact an individuals’ level of reported fatigue besides sleep quality (i.e., depression). The relationship between depression and sleep quality showed a high association (factor loading of .70) consistent with their correlation in the current sample and as such this relationship was maintained in Model 3 and Model 4. As with Model 1 and Model 2, Model 3 indicated that depression impacted sleep quality and subsequently cognitive fatigue while having no unique contribution to predicting cognitive fatigue directly. This model assumed no unique contribution of depression consistent with the regression analysis. In addition, this model evaluated whether depression had a significant role on an individual’s level of self-reported fatigue (previously attributed to sleep quality in Model 1 and Model 2). The association between these variables was high (factor loading of .74). This model showed an acceptable level of fit, though was not the best fitting model that was tested. The best fitting model was Model 4 which suggested that sleep quality and depression both played a role in predicting cognitive fatigue despite depression showing no significant unique contribution to CF in the regression analysis. Nonetheless, this relationship was included in the model given the significant correlation observed between these variables (r = .362, p = .006). The lack of significance and small factor loading (.12) in the model however suggests that the role that depression plays in directly impacting an individual’s level of CF is small. Given the small sample in the current study, future studies should evaluate whether this predictive relationship between depression and cognitive fatigue is maintained, improved, or lost when evaluating larger samples. This model maintains the high associations observed between depression and sleep quality as well as between depression and self-reported fatigue; consistent with the correlations noted within our sample. The role of disease severity, in particular physical disease severity, and how it relates to and interacts with the other variables in each model remains unclear. In this sample, disease severity did not correlate with any of the other variables and as such it is difficult to determine if and where in the models disease severity may have had an impactful role. This indeterminate relationship is evident in the path analysis of our models as factor loadings were small when we suggested it may impact sleep quality (Model 1; factor loading .01) or depression (Models 2–4; factor loading .14) in this sample. The lack of correlation between disease severity and cognitive fatigue (and subsequent non-inclusion in the regression analyses) suggests that disease severity does not play a direct role impacting cognitive fatigue and so its most appropriate place in the model remains ambiguous. While Strober and Arnett (2005) found that disease severity was an independent predictor of self-reported fatigue (Strober & Arnett, 2005), we did not test this relationship directly as the outcome variable of interest in our case was cognitive fatigue; thus we tested the affect of disease severity on both sleep quality and depression given those were the two variables most correlated with CF. Lastly, whether or not cognitive impairment relates to and/or predicts an individual’s level of CF remains unclear. While this relationship was not formally being evaluated a priori, post hoc analyses were conducted to determine whether cognitive impairment may have played a significant role in the current sample. Despite the fact that 57.9% of individuals were classified as cognitively impaired on one or more cognitive tests (i.e., ≤1.5 SD below the mean), adding cognitive impairment to the path analyses made no significant unique contribution to the models. These results are specific to the current sample, however, and may differ with larger sample sizes. There were limitations to the current study. Given the small sample size, the findings from the various models may be specific to only the current study and could be quite different in larger samples. While fit indices were chosen which attempted to account for the small sample size, future studies should evaluate whether these findings can be replicated in larger samples as the results may differ. In addition, most of the variables assessed were done so as self-reported measures. In particular, only a self-report measure of sleep disturbance was used. Given that sleep quality was the largest predictor of an individual’s level of CF, an objective measurement of sleep quality would have been ideal (e.g., EEG) as one would expect that those with poorer objective measures of sleep (as evidenced by slower sleep onset or less time spent in slow wave/REM sleep as detected by EEG, for example) would show higher levels of CF. Unfortunately, this was not assessed in the current study and should be considered as a future direction for research. A third limitation of the present study concerns the subjective nature of fatigue itself. Past research has shown that self-reported measures of fatigue consistently show no correlation with objective measures given its debated etiology (primary vs. secondary symptom of the disease) and overall subjective nature. The current study did not directly explore this relationship as, consistent with past research, the two were uncorrelated in this study’s sample. Rather, all of the models tested proposed that fatigue was a result of either depression or sleep quality. As such, the direct relationship between subjective fatigue and objective cognitive fatigue remains unclear. This lack of a seemingly intuitive relationship (i.e., those with higher levels of self-reported fatigue would be expected to show objectively more performance decline (i.e., CF)) continues to remain elusive. Future studies should address this relationship more directly in other MS samples and the impact other secondary disease characteristics might have on this relationship. Despite the limitations noted, this study has important implications. To the best of our knowledge, this was the first study to examine and develop a theoretical framework for how secondary MS disease characteristics may predict an individual’s objective level of CF. While the models proposed do not represent the entire breadth of possible relationships between the variables evaluated (nor do they represent all possible symptoms which may contribute to CF), given the good model fit for Model 4 we propose that this model represents a valuable starting point for future studies attempting to examine how disease symptomatology may impact CF. Variables which were not directly evaluated in the current study (anxiety, etc.) may also play a role in predicting an individual’s level of CF and as such, future research should consider including these to examine their possible predictive roles. As stated, the major limitation of the current study is its relatively small sample size; thus, whether findings remain consistent with larger sample sizes remains an important issue for future research. Secondly, these findings have important implications for treatment and symptom management. Results suggest that sleep disturbances seem to have the greatest impact on an individual’s level of CF and as such improvements in an individual’s overall sleep quality may also improve an individual’s experience with CF. Future research should consider exploring how levels of objective CF differ between those with higher sleep quality (e.g., those with little difficulty falling/staying asleep, those who are getting an appropriate amount of sleep each night, etc.) from those with poorer sleep quality. The hope is that sleep quality may prove to be a treatable cause of cognitive fatigue in MS which may, in turn, improve an individual’s’ overall quality of life. Funding This work was supported by the University of Ottawa Brain and Mind Research Institute (uOBMRI). Conflict of Interest None declared. Acknowledgements The authors would like to gratefully acknowledge the time and effort put forth by all the study participants. Their contributions are much appreciated. The authors have no conflicts of interest to disclose. References Amato , M. P. , Ponziani , G. , Rossi , F. , Liedl , C. L. , Stefanile , C. , & Rossi , L. ( 2001 ). Quality of life in multiple sclerosis: the impact of depression, fatigue, and disability . Multiple Sclerosis , 7 , 340 – 344 . Google Scholar CrossRef Search ADS PubMed Attarian , H. P. , Brown , K. , Duntley , S. P. , Carter , J. D. , & Cross , A. H. ( 2004 ). The relationship of sleep disturbances and fatigue in multiple sclerosis . Archives of Neurology , 61 , 525 – 528 . Google Scholar CrossRef Search ADS PubMed Bakshi , R. , Shaikh , Z. A. , Miletich , R. 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Archives of Clinical Neuropsychology – Oxford University Press
Published: Feb 17, 2018
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