Introduction
Multiple sclerosis (MS) is an inflammatory and neurodegenerative demyelinating disease of the central nervous system, affecting both gray and white matter. Reference Morrow, Drake, Zivadinov, Munschauer, Weinstock-Guttman and Benedict1–Reference Ghasemi, Razavi and Nikzad4 People with MS (PwMS) can experience a multitude of different neurological deficits, including cognitive impairment (CI). Reference Morrow, Drake, Zivadinov, Munschauer, Weinstock-Guttman and Benedict1 CI impacts 40–70% of PwMS and correlates highly with gray matter lesions and atrophy, causing difficulties such as slowed information processing speed (IPS) and impaired working memory. Reference Amato, Prestipino and Bellinvia2,Reference Nasios, Bakirtzis and Messinis3
Fatigue affects about 90% of PwMS. Reference Walker, Lindsay-Brown and Berard5–Reference Minden, Frankel, Hadden, Perloffp, Srinath and Hoaglin7 There are varying definitions of fatigue throughout the literature including a state of reduced capacity for work following a period of mental or physical activity, and a feeling of physical tiredness and lack of energy that is distinct from sadness or weakness. Reference Schwid, Covington, Segal and Goodman8,Reference Krupp, Alvarez, LaRocca and Scheinberg9 In most studies, fatigue is a subjectively measured concept and is largely evaluated using self-report tools. Reference Krupp, LaRocca, Muir-Nash and Steinberg10–Reference Mills, Young, Pallant and Tennant12 In contrast, cognitive fatigability (CF) is less well understood and relies on objective quantification. Reference Walker, Lindsay-Brown and Berard5,Reference Berard, Fang and Walker13 A unified taxonomy has been proposed to differentiate subjectively measured fatigue from objectively measured CF in clinical and research settings. Reference Kluger, Krupp and Enoka14 Although there are varying definitions of CF throughout the literature, it is presently defined as an inability to maintain optimal task performance throughout the duration of a sustained cognitive task. Reference Walker, Lindsay-Brown and Berard5,Reference Schwid, Covington, Segal and Goodman8,Reference Bryant, Chiaravalloti and DeLuca15,Reference Walker, Berard, Berrigan, Rees and Freedman16 PwMS demonstrate a greater susceptibility to CF compared to healthy controls during sustained cognitive tasks, often reflected by a breakdown or decline in performance throughout the task. Reference Bryant, Chiaravalloti and DeLuca15 Those individuals who have a greater susceptibility to CF have greater difficulty maintaining the cognitive effort required to achieve desired task outcomes over a lengthy period of time. Reference Berard, Smith and Walker17
Although self-assessments are commonly used to evaluate CF in PwMS, they strictly assess subjective fatigue and are consequently subject to psychological error, thereby reducing their accuracy. Reference Linnhoff, Fiene, Heinze and Zaehle18 Objective tests are therefore used to compliment the subjective assessments in order to better quantify CF. Some studies have measured CF using reaction time tasks, such as the Psychomotor Vigilance Task. Reference Berard, Fang and Walker13 CF indicators, such as cognitive processing speed and working memory changes, have been more commonly utilized to evaluate CF and can be measured using neuropsychological tests such as the Paced Auditory Serial Addition Test (PASAT). Reference Tombaugh19–Reference Morrow, Rosehart and Johnson21
Physical and CIs associated with MS have a critical effect on daily functioning for PwMS. Approximately half of PwMS are unemployed, resulting in a significant burden on their quality of life. Reference Clemens and Langdon22,Reference Kobelt, Thompson, Berg, Gannedahl and Eriksson23 A United Kingdom study found that unemployment rates of PwMS were approximately 75% within 10 years of diagnosis. Reference Coyne, Boscoe, Currie, Landrian and Wandstrat24,Reference Ford, Jones and Middleton25 The correlation between physical disability in MS and employment status has been widely acknowledged in the literature, with studies showing that the rate of unemployment is proportional to the Expanded Disability Status Scale (EDSS) scores. Reference Clemens and Langdon22,Reference Kavaliunas, Wiberg and Tinghog26,Reference Krause, Kern, Horntrich and Ziemssen27 Although there is a strong correlation between a high EDSS score and unemployment, 54% of PwMS with an EDSS score ≤ 3 can be unemployed, suggesting that physical disability is perhaps not the sole factor contributing to the increased rates of unemployment. Reference Kobelt, Thompson, Berg, Gannedahl and Eriksson23
There is a growing body of literature demonstrating that CI can also negatively impact employment status and consequently quality of life for PwMS. Reference Morrow, Drake, Zivadinov, Munschauer, Weinstock-Guttman and Benedict1 A systematic review of studies investigating the relationship between employment status and cognition found that PwMS who were unemployed scored lower on objective cognitive tests than PwMS who remained employed, although statistical significance varied throughout the studies. The most consistent finding was the difference in IPS between the groups. Reference Clemens and Langdon22 Previous studies have found that the most robust predictor of employment status in PwMS was the Multiple Sclerosis Functional Composite (MSFC). Reference Honarmand, Akbar, Kou and Feinstein28 The MSFC is a measure of MS-related disability that includes measures of upper and lower extremity function and cognition (i.e., the sole cognitive measure is the PASAT). The combined measures in the MSFC explained 49.8% of the variance in employment status, surpassing the predictive value of the EDSS. Reference Honarmand, Akbar, Kou and Feinstein28 Progressive disease course, longer disease duration, and female sex were also associated with unemployment, as has poorer performance on the Symbol Digit Modalities Test (SDMT) and Word List Generation task. Reference Honarmand, Akbar, Kou and Feinstein28 Strober et al. also evaluated the utility of the MSFC in predicting employment status in PwMS, finding the SDMT, rather than the PASAT, to be the sole significant predictor of employment status. Reference Strober, Chiaravalloti, Moore and DeLuca29
To date, no study has yet evaluated whether CF specifically influences employment status. It is this gap in the literature that the current study seeks to address. The current aim is to investigate whether PASAT performance and/or CF scores are associated with employment status in PwMS and, if so, to what degree (i.e., how much CF is associated with employment status).
Material and Methods
Participants
Participant data were aggregated from three separate, and now completed studies, all which had evaluated cognition in MS. All studies were approved by either the Ottawa Health Science Network Research Ethics Board (two studies), or by the Sunnybrook Health Sciences Centre Research Ethics Board (one study). A total of 186 individuals with a confirmed diagnosis of MS by their treating neurologist (McDonald criteria) were recruited from the Ottawa Hospital MS Clinic and Sunnybrook Health Sciences Centre. Inclusion criteria for all participants included being between 18 and 65 years of age, fluency in English, and a confirmed diagnosis of MS. Exclusion criteria included any neurological, medical, or psychiatric condition (besides MS and depression) that might impede cognition, use of legal or illegal drugs that might impact cognition, prior head trauma, learning disability, attention-deficit disorder, dementia, formally diagnosed mild CI, or substance abuse (alcohol, cannabis, tobacco, etc.). After all participants were given the opportunity to ask questions, informed consent was obtained.
Procedures and Measures
Participants completed the study protocol at the time of day that was most convenient for them. All participants completed a demographic interview with a research assistant on site that was trained by a licensed Clinical Neuropsychologist. As part of the interview, participants were asked about their employment status including whether they worked full-time or part-time, and how many hours they worked weekly.
In all three contributing studies, the PASAT was administered as part of a larger neuropsychological battery. In order to diminish the impact that the time of administration could have on participants performance on the task, the test was administered as either the third or fourth task in the battery. The PASAT measures IPS and working memory in the auditory modality by evaluating the participant’s ability to add one number to a previously presented number at a 3-second interstimulus interval (i.e., 3” PASAT). Although there are drawbacks to the PASAT, Reference Tombaugh19 it is acknowledged as a reliable evaluator of IPS Reference Berard, Smith and Walker17,Reference Tombaugh19 and has been used extensively to evaluate CF in PwMS. Reference Berard, Smith and Walker17,Reference Morrow, Rosehart and Johnson21 Participant responses were recorded, and the total number of correct responses, dyad score, and percent dyad scores were calculated. Traditionally, PASAT performance is assessed by counting the number of correct responses. However, the test’s sensitivity may be affected if individuals use a “chunking method” which decreases the difficulty of the test. As such, the results achieved using this strategy are no longer a reflection of the participants true IPS and cognitive abilities. Reference Berard, Smith and Walker17 Dyad scoring increases the sensitivity given that, when counting dyads, a response is considered correct only if the previous response was also correct. This ensures the working memory demands of the task are being met. Previous work has identified the percent dyad scoring method, a measure of the proportion of time a person is meeting the working memory demands of the task, as most sensitive to CF. Percent dyad scores were calculated using the following formula: (Dyad Score/Total Correct Score) × 100%. These three scores reflect various aspects of an individual’s level of performance on the task (for further details about these scoring methodologies please, see Walker et al. Reference Walker, Berard, Berrigan, Rees and Freedman16 ). In addition to these performance scores, CF scores can also be calculated using the PASAT by subtracting performance scores on the first half of the task from performance scores on the second half. This CF score was calculated for each of the three scoring methodologies. A negative CF score (total correct example: −2.25 indicates a decrease in the total number correct over time as the task progresses). This difference between the halves reflects how an individual’s performance changes across the task (i.e., their susceptibility to CF).
Analyses
Analyses were first conducted to determine if there were any demographic differences between the employed and unemployed groups based on sex, age, and number of years of education. A chi-square test for independence was used to evaluate possible differences in the proportion of males and females present in the group. A one-way analysis of variance (ANOVA) compared the age and number of years of education between the groups. ANOVAs also examined whether differences in PASAT performance or CF measures existed between the two employment groups. Linear regressions, with employment status as the dependent variable, were performed to determine if PASAT performance scores or CF scores were associated with employment status.
Results
There were no significant differences in PASAT performance or CF measures between the three contributing studies. As such, the data were aggregated across all three studies for all analyses.
Demographics
The mean age of the sample was 43.1 years (SD 9.7, range 21–65). There were 145 (78%) women and 41 (22%) men. Average education of participants was 15.1 years (SD 2.2, range 11 years–21 years). Average disease duration was 7.6 years (SD 6.6, range 0 years–30 years). The breakdown in disease course was as follows: 84.4% relapsing–remitting MS, 11.8% secondary progressive MS, and 3.8% primary progressive MS. The mean EDSS score was 2.63 (SD = 1.88, range 0–7.0).
Group Differences
Comparison between the employed (either full or part-time) and unemployed groups on demographic and disease-related variables are found in Table 1.
Table 1: Comparison of employed and unemployed groups on demographic and neurological variables
Of the 186 participants, 54 (29%) were employed whereas 132 (71%) were unemployed. Unemployed participants tended to be older and have a longer disease duration and a higher EDSS score, indicating greater MS-related disability. Given these differences, age, EDSS, and disease duration were controlled for in subsequent analyses. Group differences were found on the 3-second PASAT for all three scoring methods (total correct, total dyad, and percent dyad), with the employed group scoring significantly higher than the unemployed group. There were no significant group differences for any of the CF measures (see Table 2).
Table 2: Comparison of PASAT performance and cognitive fatigability score between employed and unemployed groups
Association with Employment Status
A binary logistic regression analysis, with employment status as the dependent variable, was conducted to identify potential associations with employment status. When controlling for age, EDSS score, and disease duration, 3-second PASAT performance scores for total correct (β = 0.008, t(1,111) = 2.454, p = 0.02), total dyad (β = 0.006, t(1,111) = 2.440, p = 0.02), and percent dyad scores (β = 0.004, t(1,111) = 1.952, p = 0.05) emerged as having significant associations with employment status. The amount of variance accounted for was 54.8% for both total correct and total dyad, and 53.5% for percent dyad scoring. Further analysis revealed that CF scores were not significantly associated with employment status when using the total correct raw score (β = 0.002, t(1, 111) = 0.188, p = 0.85), total dyad (β = 0.003, t(1, 111) = 0.355, p = 0.72), or percent dyad raw score (β = 0.004, t(1, 111) = 1.441, p = 0.15).
Discussion
General Information and Study Results
Unemployment rates in PwMS have been reported to be as high as 80%, a stark contrast to reports that rates of unemployment before diagnosis are approximately 4–10%. Reference Cutter, Baier and Rudick30–Reference Scheinberg, Holland, Larocca, Laitin, Bennett and Hall32 The current investigation examined the associations between PASAT performance, CF scores, and employment status in PwMS. The 3” PASAT performance scores for the total correct, total dyad, and total percent dyad emerged as having significant associations with employment status. Contrarily, CF scores were not significantly associated with employment status. No other study has yet evaluated whether CF specifically, as defined by a decline in performance over the duration of a sustained cognitive task, influences employment status.
Demographic Results
Unemployed individuals were found to be significantly older, have longer disease duration, and higher EDSS scores; all of which are factors characteristic of greater MS-related disability. These findings are consistent with the literature. Work participation is reduced as a result of increasing age, functional changes, and disease severity. Notably, disease severity and associated impairments are reported as one of the most significant reasons for unemployment in PwMS. Reference Pompeii, Moon and McCrory33 Cadden & Arnett have also reported EDSS score as a measure that is significantly associated with unemployment status. Reference LaRocca, Kalb, Kendall and Scheinberg34 As well, a longer disease duration and higher EDSS score are indicative of a greater level of disability, which consequently make it harder for PwMS to manage work-related demands.
PASAT Performance Scoring
PASAT performance scores were revealed to have the only significant association with employment status in PwMS. The unemployed group scored significantly lower than the employed group, suggesting that those who are unemployed are more likely to have difficulty with tasks requiring quick thinking and mental manipulation of information (i.e., processing speed and working memory). Given the lack of association with CF, results suggest that the ability to maintain employment is related more to working memory and processing speed than it is to an objective decline in cognitive performance over the duration of a single cognitive task. Silvana et al. reviewed 157 articles that assessed IPS in PwMS and found that IPS deficits have negative impacts on aspects of cognition such as working memory. IPS deficits were also identified as predictors of employment status in PwMS. Reference Vijayasingham and Mairami35 IPS is said to be a primary cognitive function, important for higher cognitive processes with influences on learning, memory, word retrieval, and executive function. These are all factors that are important in job performance and maintenance. Reference Cadden and Arnett36 Honarmand and colleagues evaluated the utility of the MSFC as a predictor of employment status in PwMS, and, in accordance with our findings, found that the PASAT was able to significantly differentiate employed and unemployed participants. Reference Honarmand, Akbar, Kou and Feinstein28 Nonetheless, there are discrepancies in the literature regarding the predictive ability of the PASAT, with other studies finding no difference in PASAT performance between employment groups. Reference Strober, Chiaravalloti, Moore and DeLuca29 This discrepancy may be due to differences in methodology or participant characteristics (i.e., a smaller proportion of RRMS, longer disease duration, etc.).
PASAT CF Scoring
As opposed to the 3” PASAT performance scores, CF scores were not associated with employment status. Studies evaluating the impact of fatigue on the employment status in other neurological conditions have demonstrated higher rates of unemployment in those subjectively reporting significant mental fatigue. Palm et al. evaluated the impact of mental fatigue on the employment status of people with traumatic brain injuries, and a correlation was found such that higher scores on the Mental Fatigue Scale resulted in less working time. Reference Costa, Genova, DeLuca and Chiaravalloti37 Similarly, Smith & Arnett evaluated factors that significantly contributed to a change in employment status in PwMS. The “no work” participants scored significantly higher on the Fatigue Impact Scale than the “work” group. Reference Eizaguirre, Vanotti and Merino38 Just as the literature demonstrates no consistent correlation between subjective cognitive fatigue and objective CF, it seems that there are differences in the association of these concepts to employment status as well.
Limitations and Future Directions
One limitation of the current study is the lack of information about employment type. Whether there is a difference in PASAT performance, CF scores, and their association with employment status depending on the nature of the work being done is unclear. One can speculate that CF may have more of an influence on one’s ability to perform jobs where there are greater cognitive demands than one’s ability to perform jobs with fewer such demands (and perhaps more physically fatiguing components). In addition, the current study did not distinguish between those who worked full-time, part-time, or were retired/on disability (who were excluded in the current study). While data were collected on full- vs. part-time employment as part of the demographic interview, the small sample size of those that were employed overall (54/186 individuals) limited the potential to subdivide the sample further for more fine-grained comparisons. Furthermore, the generalizability of our findings may also be limited by the relatively early disease course in our sample (mean disease duration of 7.6 years). The current sample may not have exhibited the greater severity of CF that one would anticipate that a sample with a longer disease course would, and thus the restricted range of disease duration may have been a factor in why no association between CF and employment staus was observed. The current sample also had a relatively low mean EDSS score of 2.63. which may have also had a similar influence on outcomes. In addition to the above, given that recruitment into the studies were on a voluntary basis, there is the potential for a selection bias to occur, whereby only those who had an interest in participating in research, or those who were less likely to be impaired, would be more likely to have participated and have consented to being contacted to take part. One further confounding variable may be the time of day when the outcome measures were completed. Individuals were scheduled to take part in the study at a time of day of their choosing. As such, there is the potential that changes in daily fatigue levels may have impacted their cognitive performance (i.e., those who came in after working all day may have been more fatigued, resulting in poorer PASAT performance). Furthermore, whether individuals were taking medications that may have influenced their cognitive processing or levels of fatigue was also not considered at present. There is the potential that some medications, or some disease modifying therapies, may have resulted in improved processing speed on the PASAT for some individuals in each group, particularly those with relapsing–remitting MS. Reference Landmeyer, Burkner, Wiendl, Ruck and Hartune39 A recent systematic review evaluating the cognitive efficacy of pharmacologic treatments for MS, however, suggests there is insufficient evidence for cognitive improvement at present given the contradictory findings in the literature. Reference Chen, Goverover, Genova and DeLuca40 Similarly, medications used to treat primary MS symptoms have the potential to also influence levels of fatigue. While some studies report no relationship between the use of immunosuppressive or immunomodulatory drugs and levels of fatigue, Reference Putzki, Katsarava, Vago, Diener and Limmroth41 others have reported an increase in fatigue levels, particularly with interferon – β, with improvements in fatigue being noted upon switching medications. Reference Hadjimichael, Vollmer and Oleen-Burkey42,Reference Iriarte, Subira and Castro43 Given the potential influence of medications on both cognition and fatigue, and the inconsistent findings in the literature, a more thorough consideration of its impact on the present study’s findings warrants future study, particularly given that little is known about the influence of medications on susceptibility to CF specifically.
Future studies should examine the associations between PASAT performance and CF scores in the context of a more inclusive statistical model accounting for other potential contributing factors to employment. For example, the current study did not exclude PwMS with depression, given the high rates of co-morbidity. Nonetheless, depression may have directly, or indirectly, impacted their cognition, their levels of CF, or their employment status. A more complex model could account for a greater proportion of the variance in employment status reported in the current investigation. Such a study could also allow for the examination of whether the relationship between cognition and employment status is bidirectional (i.e., does employment status also influence cognition?). In addition, future studies should also examine whether employment type has an impact on the association with employment status in PwMS given that certain jobs require a higher level IPS and working memory, factors that were found to be significantly associated with employment status. Accounting for employment type in future studies could help better understand the association between unemployment and PASAT performance scores. Future investigations should also evaluate if employment status is better associated with CF when CF is measured across time rather than within-task. Clinically, many individuals report that they do not necessarily experience the same level of CF when performing within-task CF tests, such as the PASAT, as they do following a full day of work. Having a more fined-grained analysis that takes into account different ways to measure and define CF may help to better our understanding of its association with employment status in PwMS.
Acknowledgements
The authors would like to thank the PwMS who participated in this study for their time and effort. The authors would also like to thank Dr Anthony Feinstein (Sunnybrook Hospital) who, although was not directly involved in this study, provided data from a previous study.
Funding
This work was supported by the University of Ottawa Brain and Mind Research Institute and the Multiple Sclerosis Society of Canada. The funding sources had no involvement in the writing of this work.
Disclosures
The authors have no conflicts of interest to disclose related to this work.
Statement of Authorship
EM contributed to the design of the study, conducted analyses, and wrote/edited the manuscript. JB conceived and designed the study, conducted analyses, and edited the manuscript. LW conceived and designed the study as well as edited the article. All authors contributed and approved the submitted version.
