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Nursing Interventions Focusing on Self-efficacy for Patients With Multiple Sclerosis in Rehabilitation: A Systematic Review

Cognitive impairment (CI) is common in multiple sclerosis (MS). Processing speed (PS) is often affected, making it an ideal target for monitoring CI. This study aims to evaluate the association between disease-modifying therapy (DMT) use and intensity and longitudinal changes in Processing Speed Test (PST) scores for individuals with MS.

A retrospective analysis of individual PST scores at a single MS center was conducted. Individuals with 2 or more PST assessments were included. Scores on the PST were compared longitudinally between those who had been on a DMT for 2 or more years and those who had been off a DMT for 2 or more years and between those on high-efficacy DMTs and those on low-/moderate-efficacy DMTs. A linear regression model was approximated to evaluate the rate of cognitive change over time. A propensity score adjustment was conducted using a multivariable logistic regression.

The cohort was 642 individuals, 539 on DMT and 103 off DMT. Median age and disease duration was 49.7 (IQR 42.4-57.9) and 16.6 years (IQR 9.3-23.0) in the DMT group, and 58.9 (IQR 52.2-65.3) and 20.0 years (IQR 14.1-31.4) in the non-DMT group. Both cohorts were predominantly female (75% DMT, 79.6% non-DMT), with a mean of 4 assessments (IQR 3-5), and an average monitoring duration of 1.9 years (1.2-2.4) in the DMT group, and 1.8 years (1.4-2.4) in the non-DMT group. After adjusting for multiple factors, DMT status and intensity were not found to be significant predictors of longitudinal PST change.

Neither DMT status nor intensity was a significant predictor of cognitive processing speed over a period of approximately 2 years. Future prospective studies are needed to further support these findings.

From the Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH,USA (AA, JB); Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA (MA, GM, DO); Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA (KN); Department of Neurology, Hotel Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon (GM). 

Daniel Ontaneda MD, PhD, 9500 Euclid Ave U10, Mellen Center, Cleveland, OH 44116; 

 Dr Amin received a Novartis fellowship award (NGC44741). Dr Macaron has served on advisory boards for Biologix, Genentech-Roche, Merck, and Novartis; received speaker fees from Biologix, Merck, and Novartis; and participated in educational activities for NeurologyLive

 and John Hopkins's e-Literature Review. Dr Ontaneda receives research support from Genentech, Genzyme, National Institutes of Health, National Multiple Sclerosis Society, Novartis Patient Centered Outcomes Research Institute, and Race to Erase MS Foundation. He has received consulting fees from Biogen Idec, Genentech/Roche, Genzyme, Janssen, Merck, and Novartis. The other authors have no conflicts of interest.

 Presented as a poster at the European Committee for Treatment and Research in Multiple Sclerosis; October 27, 2022; Amsterdam, The Netherlands.

Multiple sclerosis (MS) is among the most common disabling neurologic diseases in young adults.

 Global disease prevalence continues to rise, with estimates from 2020 approximating 35.9 per 100,000 people.

 Approximately 40% to 70% of individuals with MS demonstrate varying degrees of cognitive impairment (CI).

 Importantly, all forms of MS have been associated with CI, including cognitive deficits in individuals with clinically isolated syndrome (CIS) and radiologically isolated syndrome.

 CI can be disabling even in the early stages of the disease and arises independent of physical disability.

Because it affects social functioning and future employment and often results in increased social isolation, CI predicts quality of life in MS.

 Individuals with mild disease and early cognitive deficits have an increased likelihood of worsening disability within 5 years, as well as evolution from benign MS at 12 years.

The most common cognitive domains affected by MS include information processing speed (IPS), verbal and visual memory, visuospatial processing, and attention.

 Less commonly, executive functioning, language, and long-term memory are implicated.

 IPS is the ability to manipulate information over a short period of time.

 It is a useful cognitive marker in MS, as it is affected early in the disease course and is associated with worse vocational outcomes independent of age and self-reported physical disability.

 IPS can be reliably measured with simple tests such as the Symbol Digit Modalities Test (SDMT),

 a concise test with excellent psychometric properties that is used in most cognitive batteries,

 or the Processing Speed Test (PST), an electronic adaptation of the SDMT.

 The PST was found to have excellent retest reliability as compared with the SDMT and has been integrated into routine care at our center

 and several other centers in Europe and North America.

With broadened therapeutic options for MS treatment in the past decade, whether an association exists between disease-modifying therapy (DMT) and the progression of CI has become an increasingly pertinent question. A recent meta-analysis evaluating 44 studies with 55 distinct relapsing MS populations revealed a modest protective effect of DMT on longitudinal cognitive function in the form of processing speed,

 but escalation DMTs did not provide a greater benefit in cognitive performance than platform therapies.

 Furthermore, many highly effective therapies (eg, ocrelizumab, cladribine, alemtuzumab) were either poorly represented or not included in the study.

The aims of the current study are to determine whether an association exists between DMT and cognitive processing speed within a large-scale clinical cohort, to evaluate whether significant differences in PST scores exist among individuals on high versus low potency therapies, and to investigate associations between DMT status and MRI correlates of MS-related CI.

This study was approved by the Cleveland Clinic institutional review board. We conducted a single-center observational study using a clinical practice database incorporating the Multiple Sclerosis Performance Test (MSPT)—a digital battery of tests routinely performed at each visit by patients at Cleveland Clinic—from December 2015 through April 2020.

All encounters with individuals with a diagnosis of MS, aged 18 years or older and with 2 or more PST evaluations, were included in this study. Exclusion criteria included unavailable PST scores, missing basic demographic data (ie, age, sex, level of education, disease type, and disease duration), and any switch from high-efficacy DMT to low-/moderate-efficacy DMT. Patients were divided into 2 cohorts: the DMT cohort with individuals who had spent at least 2 years on any DMT, and the non-DMT cohort with individuals who had been off of a DMT for at least 2 years and did not start one during the follow-up period. All PSTs performed outside of this 2-year DMT window (on or off) were included in our analysis. The DMT cohort was further subdivided into 2 cohorts: those on high-efficacy DMT, and those on low-/moderate-efficacy DMT. Individuals who switched from high-efficacy DMT to low-/moderate-efficacy DMT or vice versa during the follow-up period were excluded. High-efficacy treatments included ocrelizumab, alemtuzumab, natalizumab, and rituximab. Low-/moderate-efficacy treatments included dimethyl fumarate, fingolimod, teriflunomide, interferon beta-1a, interferon beta-1b, and glatiramer acetate. Efficacy was classified based on the consensus definition from an ongoing randomized clinical trial (DELIVER-MS; NCT03535298).

Study variables were collected from individuals with MS via the MSPT iPad application during Cleveland Clinic Enterprise clinical visits. Data obtained from the MSPT included clinical MS history, patient reported outcomes, and MSPT performance measures. These data were supplemented by data from the electronic medical record (EMR),as well as standardized imaging sequences and analysis conducted within our institution. All MS types were included in our study. Relevant demographics assessed included patient age, sex, education level, initial age at MS diagnosis, and MS disease duration (defined as time from initial MS diagnosis to PST performance). The Patient Determined Disease Steps (PDDS) scale was used as a measure of self-reported physical disability. The Patient Health Questionnaire-9 (PHQ-9) was used as a measure of depression severity.

Z-scores were calculated using the raw PST (zPST) scores of the individuals with MS compared with controls as a function of age, sex, and education level. The duration of time evaluated from initial PST score to last follow-up, and the number of overall PST scores were included. Several metrics thought to be associated with cognitive function in MS were derived from a subset of the study cohort (as previously described

) via MRI volumetric analysis, including whole brain fraction (WBF; defined as the total brain volume including gray and white matter, normalized to the outer contour volume), T2 lesion volume (T2LV; defined as native white matter lesions visualized on T2-weighted fluid attenuated inversion recovery [FLAIR]), gray matter fraction (GMF; total volume of gray matter) and thalamic fraction (ThalF; total thalamic volume in relation to outer contour volume).

Baseline demographics among study groups (DMT vs non-DMT, and high- vs low-/moderate-efficacy treatments) were tested using the Mann-Whitney 

 test. For each study participant, a linear regression model was fitted with z-PST as the dependent variable and age of assessment as the independent variable. The coefficient of this model, the cognitive slope, was used to approximate the rate of change in cognitive function over time. The mean of all available quantitative MRI metrics during the time range used in the analysis above were also calculated for each participant. Disease course was dichotomized into relapsing-remitting MS (RRMS) vs progressive MS. 

 values less than or equal to .05 were used for statistical significance testing. Statistical analysis was conducted using R. Slopes of cognitive dysfunction among the different groups were tested within the linear model.

The association between DMT use and PST score was determined using propensity score (PS) adjustment and PS was calculated using a multivariable logistic regression model in which the dependent variable was a binary indicator of receiving DMT, while the covariates included baseline age, sex, level of education, disease duration, disease subtype, and baseline z-PST score. Matching using the full method with a caliper width of 0.25 SD was conducted using MatchIt library in R. Standardized mean biases were evaluated to ensure balance after propensity score matching between the DMT and non-DMT cohorts. A multivariable linear regression model was then fitted with the cognitive slope as the dependent variable, and the binary DMT status as the independent variable. Covariates included baseline age, sex, education level, disease duration, disease subtype, and baseline z-PST score. A similar process was done for the high-efficacy and low-/moderate-efficacy DMT cohorts.

A total of 749 individuals were identified, of which 710 had at least 2 PST scores. After exclusion criteria were applied, 642 were included in the analysis (

; all supplemental material is available in a PDF at the end of the online article). The average follow-up was 1.9 years (SD: 1.2-2.4) in the DMT group and 1.8 years (SD: 1.4-2.4) in the non-DMT group. The average number of PST scores was 4.0 (SD: 3-5).

Cognitive Slope Comparison Between DMT vs Non-DMT Groups  

Of the 642 study participants, 539 were on DMT (DMT group) and 103 were not on a DMT (non-DMT group). The cohort's clinical characteristics are summarized in 

. On average, the DMT group was significantly younger (

 < .001), with a younger age at diagnosis (

 ≤ .015), shorter overall disease duration (

 ≤ .011), and a higher predominance of relapsing disease (

 ≤ .001). After matching, 537 individuals on DMT and 102 individuals not on DMT were included in the matched group. The summary of the linear regression model fitted in the matched cohort for prediction of cognitive slope is shown in 

. Cognitive slope was defined as the change in PST over time. Cognitive slope was determined to be positive in both the DMT and non-DMT groups. There was no significant difference in cognitive slope across the 2 groups (

). The only variable with a statistically significant relationship was the initial zPST score, which correlated negatively with the cognitive slope. Older age, male sex, and increased disease duration showed negative point estimates for PST slope, although these did not reach statistical significance. The estimate for being on or off DMT was not found to be statistically significant.

Cognitive Slope Comparison Between High-Efficacy DMT vs Low-/Moderate-Efficacy DMT

In the DMT cohort, 396 individuals who had available data and did not switch DMT class during the evaluated period were included. Of these, 96 (24.2%) were on high-efficacy DMT (high-efficacy group) and 300 (75.8%) were on low- to medium-efficacy DMT (low-/moderate-efficacy group). The clinical characteristics of the DMT cohort are summarized in 

. After matching, 95 high-efficacy DMT and 298 low-/moderate-efficacy DMT patients were included in the matched cohort. The summary of the linear regression model fitted to the matched cohort for prediction of cognitive slope is shown in 

. None of the variables except education, including being on high-efficacy DMT compared to low-/moderate-efficacy DMT, were found to have statistically significant estimates in predicting the cognitive slope (

When available, the average MRI metrics for the duration of monitoring were compared across the DMT vs non-DMT cohorts, as well as high-efficacy vs low-/moderate-efficacy cohorts as shown in 

. Patients in the DMT cohort had higher GMF, higher T2LV, and lower thalamic volumes when compared with the non-DMT cohort. No other statistically significant differences were found.

We conducted a large-scale, single-center, retrospective analysis investigating the association between DMT and cognitive function in a cohort of individuals with MS. After controlling for age, level of education, MS phenotype, disease severity, physical disability, and baseline PST score, our study did not find a significant difference in PST scores between treated and untreated patients with MS. Importantly, study participants were matched based on their initial PST score after a 2-year period either on or off DMT, and no significant longitudinal difference in average PST score was revealed. Similarly, when individuals on DMT were divided into high-efficacy DMT and low-/moderate-efficacy DMT cohorts, no significant difference in average PST score slope was found, a finding similarly described in prior literature.

 As expected, older age, male sex, and increased disease duration, each of which is considered a positive predictor of increased disease severity, were all negatively associated with PST score. Higher initial PST score was negatively associated with PST slope. While the reason for this is not entirely clear, it may suggest that patients at a higher cognitive baseline have more opportunity for future decline or may represent a regression to the mean effect.

The cognitive slopes calculated for both cohorts were positive, suggesting that PST scores improved during the 2-year period observed. This is likely due to confounding by the practice effect, in which individual testing inevitably leads to performance enhancement on future tests.

 The limited length of time observed and number of PSTs recorded are possible contributors to this finding. Nonetheless, DMT status did not appear to be associated with the absolute value of the cognitive slope.

We interrogated MRI measures in relation to DMT status, as these measures may be associated with cognitive function in MS. Our analysis showed no significant difference in WBF between the DMT and non-DMT cohorts across an average of 5 years, suggesting the cohorts were well matched. In the DMT cohort, GMF and T2LV were both significantly higher, suggesting that these values are not necessarily negatively correlated, and ThalF was significantly lower and is likely related to greater T2LV.

As previously mentioned, a meta-analysis conducted by Landmeyer et al in 2020 that evaluated the effects of DMT on cognitive test performance concluded that there is a small to moderate effect size of both platform and escalation therapies on processing speed.

 Notably, there was no difference between escalation and platform therapies.

 There are several reasons why our results may have differed. Many of the studies included in the meta-analysis were uncontrolled observational studies that evaluated cognition as a secondary or tertiary outcome.

 Additionally, the sample sizes evaluated in several of these studies were small; only 1 study reached a cohort of 1000, and the majority included less than 100 participants.

 Underreporting of negative results in studies with smaller samples was also noted, which may have led to publication bias.

 Furthermore, the included studies used the SDMT as well as the Paced Auditory Serial Addition Test, which may have led to increased variability. In our study, approximately 24% of individuals in the DMT cohort were on high-efficacy DMT, which included ocrelizumab, alemtuzumab, natalizumab, and rituximab. In the prior meta-analyses, 1 sample each of alemtuzumab (21 individuals) and rituximab (75 individuals) were included, and neither ocrelizumab nor natalizumab was featured. Similar to the prior meta-analysis, our study further emphasizes a lack of a robust response to DMT despite the use of escalation therapies with high potency, which suggests that there may be a secondary pathophysiological mechanism in MS-related cognitive decline.

Recent literature implicating the role of gray matter pathology and neurodegeneration in MS-related cognitive decline may provide further insight into our findings. Gray matter involvement in MS has been well documented, with demyelination occurring in the cerebral cortex and deep gray structures.

 While gray matter destruction and associated cerebral atrophy are more commonly seen in primary progressive MS, these findings are variably present in relapsing and secondary progressive disease as well.

 Furthermore, both gray matter inflammation and atrophy may be present early in MS disease activity and also can occur in CIS.

The relationship between gray matter disease and acute white matter lesions in MS remains unclear. Cortical atrophy is not always a reliable predictor of white matter lesion burden on MRI.

 This is evidenced by large autopsy studies evaluating MS pathology, which have not shown a clear association between white matter and gray matter disease involvement.

 Furthermore, similar levels of CI have been measured across CIS and early RRMS, despite a significantly higher level of MRI T2 lesion burden in RRMS,

 and the frequency and severity of CI may be similarly debilitating across both populations.

 These findings suggest that cognitive deficits in MS may be at least partially independent of acute MS relapse and focal white matter lesion development.

 Finally, cognitive deficits can be present in patients with minimal physical disability, resulting in a weak correlation between the Expanded Disability Status Scale (EDSS) score and CI.

 This may help to explain why DMT administration, which is highly efficacious in preventing acute relapse activity and preserving EDSS, may result in a weaker association with cognitive preservation.

Our study has several limitations, many of which are common to observational studies. Our study was performed at a single center, which limits its generalizability to other populations, especially outside of the United States. As with any retrospective analysis, the possibility of confounders may have affected the outcome, and although efforts were made to control for major confounders as previously detailed, some residual confounders persist. Indication bias in the study may have removed treatment effects from our results. While all patients seen at our center take the PST prior to their appointment, those with more active disease likely take more PSTs, which could result in a detection bias. Excluding individuals with less than 2 PSTs may have also led to a degree of selection bias.

Our dichotomization of 2 years on treatment and 2 years off treatment was intended to maximize the ability to demonstrate an effect while maintaining statistical power based on sample size, but this decision may have influenced the results. It is possible that 2 years may not be a long enough time period to identify a significant difference in processing speed across the 2 cohorts. Furthermore, the timing of DMT also may have differential effects at different stages of the disease course. Studying a cohort of patients who are stable on a particular DMT for the duration of 2 years suggests overall disease stability and excludes patients with breakthrough disease who might be more cognitively affected. This is an inherent challenge in evaluating the effect of DMT on progressive symptoms in MS in a real-world observational study. Furthermore, despite attempts to match patients based on disease severity and duration, our cohort had extensive variability, with a subset of individuals who likely had a high level of disease burden at the time of evaluation. There were also differences in sample size between our DMT and non-DMT cohorts, with our high-efficacy and low-/ moderate-efficacy subgroups limited in size, especially given that those who switched treatment groups or were exposed to high-efficacy therapy prior to being on low-/moderate-efficacy therapy were excluded from our analysis.

As noted earlier, due to limitations in length of monitoring and number of PSTs completed, our study could not avoid practice effect confounding. This is a common problem with the use of the SDMT.

 The suitability of SDMT or PST to monitor long-term treatment effects in an observational cohort is still to be determined, but it may require even larger sample sizes and more prolonged follow-up. Previous studies have demonstrated that several different cognitive phenotypes exist in MS, and processing speed may only be impaired in a minority of patients early in the disease state.

 Over time, the number of cognitive domains affected by MS increases.

 Damasceno et al demonstrated that 47.5% of individuals with MS showed multiple cognitive domains affected at 2 years, with that number rising to 62.2% after 6 years. Thus, the use of a brief monodomain cognitive screen, rather than comprehensive neuropsychological testing or brief neuropsychological batteries, may not be sufficient on its own to truly identify CI, particularly over a brief period of time. This may have contributed to the absence of observed DMT effect in our study. Taken together, our data may suggest that evaluating processing speed in isolation may not be a sensitive treatment outcome biomarker in clinical practice.

Large-scale multicenter prospective studies are needed to further evaluate the effect of DMT on long-term cognitive outcomes in MS. Ideally, future studies should account for acute relapses across DMT and non-DMT cohorts and compare MRI volumetric data longitudinally, evaluating whether cognitive decline occurs at similar rates despite significant differences in acute relapse rate. Future prospective studies should also focus on evaluating the association of DMT and CI in individuals with early RRMS, who often start DMT near the time of their MS diagnosis. A recent study investigating the effect of a highly effective DMT (ie, ocrelizumab) compared with platform therapies (ie, beta-1a interferon) highlighted the significance of steady progression independent of relapse activity (PIRA) on disability accumulation and revealed that highly effective therapies may be significantly more effective in preventing PIRA.

 Although CI was not evaluated, investigating the effects of various DMT on PIRA in relation to CI may prove to be important.

This single-center retrospective analysis examined the association of DMT and processing speed and did not find a significant difference between individuals with MS on a DMT vs those not on a DMT for at least 2 years, after controlling for age, level of education, MS phenotype, disease severity, physical disability, and baseline PST score. Additional multicenter longitudinal prospective studies are needed to further support these findings. Future studies should evaluate the rate and magnitude of cognitive decline across multiple cognitive domains in individuals with MS deemed to have clinically stable disease, as this may allow clinicians a sensitive and effective tool in measuring progression independent of relapse activity.

Association Between Disease-Modifying Therapy and Information Processing Speed in Multiple Sclerosis

Fatigue is a common and debilitating symptom of multiple sclerosis (MS). Prior work suggests that the prevalence of fatigue is higher in progressive MS (PMS) than relapsing MS (RRMS). No patient-reported outcomes of fatigue have been validated specifically for individuals with PMS, despite evidence that they characterize fatigue differently than individuals with RRMS. Therefore, the objective of this study was to characterize fatigue in both the RRMS and PMS subtypes and determine the convergent validity of the Fatigue Symptoms and Impacts Questionnaire (FSIQ) for individuals with PMS.

A nationwide survey yielded 806 (637 RRMS, 169 PMS) complete responses. The survey collected demographic information and self-reported disease severity, as well as measures of fatigue, health-related quality of life, and self-reported functioning.

People with PMS reported significantly more severe fatigue than those with RRMS (

 < .001). The FSIQ subdomains of physical, cognitive/emotional, and coping demonstrated moderate (

More severe fatigue in people with PMS as compared to those with RRMS underscores the importance of using validated tools to capture fatigue in persons with PMS. The FSIQ is a valid and freely available tool to capture the physical, mental, and emotional fatigue of individuals with PMS.

From the Neuroimaging and Neurorehabilitation Laboratory (JF, ZA-R, NF), Department of Health Care Sciences (NF), and Department of Neurology (NF), Wayne State University, Detroit, MI; Department of Physical Therapy, MGH Institute for Health Professions, Boston MA (PP). 

 Nora Fritz, PhD, PT, DPT, NCS, 259 Mack Avenue, #2324, Detroit MI 48201; 

We would like to thank the National Multiple Sclerosis Society for their assistance with survey distribution.

 Prudence Plummer, PhD, PT, BPhysio(Hons), has research funding from the National Multiple Sclerosis Society and has active contracts with Helius Medical Technologies, Inc, and the Shepherd Center. Nora Fritz, PhD, PT, DPT, NCS, has research funding from the National Institutes of Health, the National Multiple Sclerosis Society, and Teva Pharmaceutical Industries that is unrelated to the submitted work. Fritz serves as member at large for the Consortium of Multiple Sclerosis Centers and receives travel funds to support attendance at its annual meeting.

One of the most common symptoms in multiple sclerosis (MS) is fatigue, with up to 92% of individuals reporting fatigue

 that manifests as lack of energy, exhaustion, or worsening of MS symptoms and ultimately contributes to increasing disability.

 Fatigue is common in both the relapsing and progressive phenotypes of MS. Due to the higher prevalence of relapsing-remitting MS (RRMS), the majority of fatigue research has focused on this phenotype. However, it has been recently suggested that fatigue may be more prevalent in progressive forms of the disease (PMS).

 Currently, there are very few measures of fatigue for patients with PMS. A recent systematic review

 recommended the following fatigue measures for individuals with MS based on available psychometrics: Fatigue Impact Scale, Fatigue Severity Scale (FSS), Modified Fatigue Impact Scale (MFIS), Neurological Fatigue Index-MS, and Unidimensional Fatigue Impact Scale. However, none of the studies included in the review exclusively examined the psychometrics in PMS; all had mixed samples with more individuals with relapsing than progressive phenotypes. Thus, the psychometric properties of the recommended and most widely used fatigue measurement scales for people with PMS are largely unknown.

Recent work has developed and examined psychometrics of a fatigue scale specifically for persons with RRMS, the Fatigue Symptoms and Impacts Questionnaire (FSIQ).

 FSIQ development included interviews with individuals with MS to determine fatigue-related symptoms and impacts and cognitive interviews with additional people with RRMS using a draft questionnaire. Next, content confirmation interviews with people with PMS were conducted, followed by item reduction, which resulted in 7 symptom items and 13 impact items. Critically, this comprehensive measure includes physical, mental, and emotional subscales, while many existing measures fail to capture all 3 domains. In a recent qualitative study, individuals with PMS identified physical and mental concepts of fatigue separately and characterized fatigue as occurring daily with negative impacts on quality of life.

 Concept mapping to existing patient-reported outcome measures supported the FSIQ as the most suitable exiting option for assessing fatigue in persons with PMS.

 The validity and minimal detectable change of this measure and its physical, mental, and emotional subscales in individuals with progressive disease are not known. Further, convergent validity of the FSIQ with other commonly used measures of fatigue (eg, FSS or MFIS) or with common clinical symptoms (ie, depression, anxiety) has not been established for individuals with either RRMS or PMS. Valid measures of fatigue for people with RRMS and people with PMS are critically needed to monitor change over time and with intervention.

Therefore, the objectives of this study were to characterize fatigue in both the RRMS and PMS subtypes and to determine the convergent validity of the FSIQ for people with PMS compared with those with RRMS. Furthermore, we examined the relations between fatigue and other self-reported symptoms (eg, depression, sleep, anxiety, pain, stress, physical activity, cognition, and quality of life) in people with RRMS and PMS.

All data were collected and managed using an online survey designed in Research Electronic Data Capture (REDCap). REDCap is a secure, Health Insurance Portability and Accountability Act–compliant, web-based software platform designed to support data capture for research studies hosted at Wayne State University

 and available via a link distributed through the National Multiple Sclerosis Society electronic newsletter. Participants anonymously responded to the survey and did not report identifying or personal health information. Participants were included if they endorsed a diagnosis of RRMS or PMS, were age 18 years or older, and were able to read and write in English. Participants were excluded if they were unable to complete or consent to the survey. All study procedures were approved by the Wayne State University Institutional Review Board. The survey collected demographic information and self-reported disease severity with the Patient-Determined Disease Steps (PDDS) scale, as well as measures of fatigue, health-related quality of life, and self-reported functioning.

Fatigue was assessed with a battery of self-report questionnaires; in all cases, higher scores indicate greater fatigue.

The FSIQ assesses fatigue-related symptoms, as well as fatigue-related impacts, in physical, cognitive, emotional, and coping domains. The FSIQ has established validity for RRMS.

The FSS is a 9-item scale measuring severity of physical fatigue and its impact on activity and lifestyle. The FSS has moderate test-retest reliability and excellent convergent validity with self-reported walking function on the MS Walking Scale-12 (MSWS-12).

The MFIS is a 21-item scale with physical, cognitive, and psychosocial subscores.

 The MFIS has excellent test-retest reliability

 and excellent concurrent validity with the FSS.

The Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue-MS is an 8-question scale examining fatigue frequency and interference with physical functioning in people with MS.

 The PROMIS Fatigue-MS has excellent test-retest reliability in MS.

The Quality of Life in Neurological Disorders (Neuro-QOL) Fatigue is an 8-item standardized measure of fatigue with established reliability in people with neurologic conditions.

The MS Quality of Life 54 (MSQOL-54) is a 54-item multidimensional health-related quality-of-life measure that combines generic items from the Short Form Health Survey and MS-specific items for scores in both physical and mental health dimensions, with higher scores indicating better quality of life. The MSQOL-54 has established reliability and validity in MS.

Self-reported functioning in domains other than fatigue and quality of life was assessed with valid and reliable measures including PROMIS Depression 8a

; PROMIS Pain Interference 4a and Pain Intensity 3a

; National Institutes of Health (NIH) Toolbox Item Bank – Perceived Stress

; Godin Leisure-Time Exercise Questionnaire

 T scores were used for analyses for all PROMIS and NIH measures. MSWS-12 scores were converted to a 0 to 100 scale.

Data analyses were performed using IBM SPSS Statistics version 28.0.1. Responses were screened for inclusion criteria and analyzed for total and individual MS subtypes. In all analyses, individuals who endorsed having primary progressive MS or secondary progressive MS were combined into a single PMS group. Mann-Whitney U tests were used to examine differences in fatigue among people with RRMS and PMS. Spearman correlation was used to assess the convergent validity of the FSIQ with other measures of fatigue in both RRMS and PMS phenotypes. Spearman correlation was also used to assess the divergent validity of the FSIQ with other measures of MS symptoms, including pain, mobility, mood, and quality of life. Correlation coefficients of less than 0.25 were interpreted as little or no relation, greater than or equal to 0.25 to less than or equal to 0.5 as a fair relation, greater than or equal to 0.5 to less than or equal to 0.75 as a moderate to good relation, and greater than or equal to 0.75 as a good to excellent relation.

 greater than or equal to 0.50 for reporting results.

Of the 1224 responses received, 806 (65.8%) participants consented and completed full demographic information. Of these, 637 (79.0%) self-reported as having RRMS, and 169 (21.0%) self-reported as having a progressive form of MS (109 [13.5%] secondary progressive MS; 60 [7.4%] primary progressive MS). A majority of participants were female (83%) and White (85.2%), with an average age of 50.5 years and a symptom duration of 3.6 years (

). Participants who reported PMS were, on average, older with a longer average symptom duration and reported an increased level of disability compared with those with RRMS (Table S1).

Comparing Fatigue Domains and Severity in RRMS and PMS Subtypes 

Compared to people with RRMS, people with progressive forms of MS reported significantly higher scores (ie, more severe fatigue) in all domains of the FSIQ except the cognitive domain (

). Participants with PMS reported significantly greater fatigue severity on the FSS (P < .001), and greater physical and psychosocial impacts of fatigue on the MFIS (P < .001). As observed with the cognitive domain of the FSIQ, there was no significant difference in cognitive fatigue on the MFIS between participants with RRMS and PMS. Additionally, participants with PMS self-reported increased symptoms of fatigue through the PROMIS fatigue scale (P < .001), and significantly greater impact of fatigue on quality of life in the Neuro-QOL fatigue scale (P < .001).

All subsections of the FSIQ demonstrated moderate to excellent convergent validity with the FSS, MFIS Physical, MFIS Cognitive, MFIS Psychosocial, MFIS Total, PROMIS Fatigue, and Neuro-QOL Fatigue in persons with RRMS (

There were significant correlations between the FSIQ Physical and all other measures for both RRMS and PMS (Table 1); however, all correlations were stronger for the RRMS cohort than the PMS cohort. Further, the FSIQ Physical had the weakest correlations with the MFIS Cognitive in both MS types. Together, these findings demonstrate good convergent validity of the FSIQ Physical as a measure of physical fatigue in people with PMS and suggest that there are modest to null associations between physical fatigue and cognitive fatigue in PMS.

The FSIQ Cognitive demonstrated strong and significant correlations with the MFIS Cognitive and good correlations with the MFIS Psychosocial in both RRMS and PMS. There were also good strength correlations between the FSIQ Cognitive with all other measures and the MFIS Physical for both MS phenotype groups (Table 1).

Among people with PMS, the FSIQ Coping demonstrated excellent correlation with the Neuro-QOL fatigue measure, the PROMIS fatigue measure, and the MFIS Total, and good strength correlations with all other measures (Table 1). The weakest association of the FSIQ Coping for people with PMS was with the MFIS Cognitive, but this was also the weakest for people with RRMS. Of note, the correlations for the FSIQ Coping tended to be higher than those for the FSIQ Physical for people with PMS, suggesting that the coping questions may be particularly relevant for assessing the impact of fatigue in PMS (and RRMS). Again, the strength of associations was stronger for the RRMS cohort than the PMS, which may be due to differences in sample size. Nonetheless, all associations were acceptable to excellent for demonstrating convergent validity in PMS.

Finally, the FSIQ Daily also demonstrated good to excellent correlations in RRMS (Table 1) and moderate to good correlations among people with PMS.

The FSIQ Physical demonstrated a moderate to excellent correlation with health-related quality of life (MSQOL physical), self-reported physical functioning (PROMIS Physical), and self-reported walking function (MSWS-12) in individuals with RRMS and individuals with PMS. The fair relation with sleep and the moderate relation with cognition observed in RRMS were not found in the PMS cohort (

In the PMS group, the FSIQ Cognitive demonstrated a moderate correlation with the MSQOL physical and mental, as well as self-reported depression, cognition, anxiety, and pain, which was similar to the findings for the RRMS group (Table 2).

Finally, both the FSIQ Coping and FSIQ Daily demonstrated moderate correlations with the MSQOL physical and mental, as well as self-reported depression, anxiety, stress, cognition, and pain, which was similar to the findings for the RRMS group (Table 2).

Compared with people with RRMS, we found that people with PMS, on average and across all measures, reported more severe fatigue. Considering the different types of fatigue across all included measures, people with PMS reported more severe physical and psychosocial fatigue than people with RRMS. There were no differences between the RRMS and PMS groups in cognitive fatigue measured by either the FSIQ Cognitive/Emotional scale or the MFIS Cognitive scale. This may be because cognitive fatigue was relatively mild in both groups on both cognitive fatigue measures. Indeed, among the different fatigue domains, physical fatigue was the most severe, especially in people with PMS. It has been reported that fatigue is more prevalent in people with progressive forms of MS.

 Our findings suggest that it is not only more prevalent, but it is also more severe and predominantly driven by the subjective experience of physical fatigue. The current findings also suggest that it may be important to rate physical fatigue separately from other types of fatigue. In this regard, the FSIQ and the MFIS, which provide physical and cognitive subscores, may be optimal.

Overall, the FSIQ subdomains (physical fatigue, cognitive fatigue, and coping) demonstrated good to excellent convergent validity with other more established measures of self-reported fatigue in people with PMS (Neuro-QOL fatigue, PROMIS fatigue, MFIS total, FSS), as well as the respective physical and cognitive subdomains of the MFIS. The finding that the FSIQ Physical was least strongly related to cognitive fatigue on the MFIS Cognitive subscale provides further evidence that physical and cognitive/emotional dimensions of fatigue are discrete entities and may need to be measured separately (in both RRMS and PMS). Indeed, when talking spontaneously about fatigue, people with PMS characterize physical and mental aspects of fatigue separately.

 Thus, instruments that help clinicians differentiate whether perceptions of fatigue and fatigability are associated with physical and/or cognitive/emotional impacts, such as the FSIQ and MFIS, may have greater clinical value over other fatigue measures that generate a single overall fatigue score. The current data provide new evidence that the FSIQ has good to excellent validity in people with PMS.

The FSIQ Coping Impacts subdomain had the strongest convergent validity for people with PMS compared with the other FSIQ subdomains. The exception was that the FSIQ Coping score demonstrated weaker correlations with MFIS Cognitive scores than with the FSIQ Cognitive scores. The high convergent validity of the FSIQ Coping Impacts also suggests that this subdomain may be particularly relevant to evaluating self-reported fatigue in people with PMS. The FSIQ Coping section includes questions that ask about both difficulty (motivation to perform routine daily activities, taking part in social activities) as well as frequency of needing to take a nap, take a break, and rearrange plans. Two of the 5 items of the FSIQ Coping subdomain are also included in the FSIQ Physical fatigue score, which may explain their similarly strong correlations with other existing fatigue measures. However, 4 of 5 items on the FSIQ Physical Activity rate difficulty rather than frequency. It may be the combination of both symptom frequency and its associated difficulty that makes the FSIQ Coping particularly sensitive to detecting fatigue in people with PMS.

The strong relation between the FSIQ Physical and other physical symptom self-rating scales (MSQOL Physical, PROMIS Physical, and MSWS-12) is consistent with prior research that physical fatigue is associated with perceived limitations in physical performance.

 Based on the current data, this appears to be true for both RRMS and PMS. Interestingly, however, despite the perceived link between physical fatigue and physical function, meta-analyses of the effects of exercise interventions on fatigue indicate substantial heterogeneity.

 That is, improving physical function or exercise capacity does not consistently result in improved ratings of fatigue. Indeed, treatment of fatigue in MS remains a considerable challenge. This is most likely due to our limited understanding of fatigue mechanisms and because fatigue is a dynamic phenomenon that can vary greatly both among and within individuals.

 Evaluating fatigue on comprehensive instruments such as the FSIQ may be an important step in guiding person-centered interventions for fatigue.

Although sleep disturbance may appear as a cluster of symptoms that exacerbate fatigue,

 neither physical fatigue nor cognitive fatigue (FSIQ) had any association with sleep (PROMIS Sleep) in people with PMS and little to no relation in people with RRMS. The statistical significance of the weak relation between FSIQ Physical and FSIQ Cognitive scores and PROMIS Sleep in people with RRMS (r < 0.22) is likely due to the large RRMS group sample size and simply indicates that the weak relation has a high probability of being real (ie, very low probability of Type II error), though recent work suggests that subjective reports of sleep may not correlate with fatigue.

 It is possible that subjective reports of sleep disturbance do not adequately capture the relation between sleep and fatigue,

 necessitating the use of objective measures of sleep that better align with fatigue.

 Whereas FSIQ Cognitive/Emotional fatigue scores did not correlate with physical fatigue scores, they were associated with physical aspects of health-related quality of life, albeit only moderately for people with PMS. Cognitive fatigue was also associated with depression, lower mental health–related quality of life, and self-rated cognitive function. Fatigue arising from depression, anxiety, and pain may decrease the ability to cope with the disease and have an impact on daily living.

 Our findings suggest that subjective reports of cognitive function may be influenced by depression and fatigue, emphasizing the importance of cognitive, mood, and fatigue screening in routine clinical care.

The key strengths of this study were the large sample sizes for the 2 MS phenotypes, the use of multiple existing patient-reported outcomes, and the comparisons between people with PMS and RRMS. Our study had some limitations. Due to the survey nature of the study, we were not able to capture any functional measures of performance or objective measures of fatigability. The study also assessed fatigue at a single point in time, and it is well known that fatigue is a dynamic phenomenon that can vary dramatically both individually and among individuals on a daily basis.

 It is possible that some individuals were unaware of progressing from RRMS to secondary progressive MS, so future studies examining differences in fatigue between MS subtypes should consider confirming diagnosis prior to participation. Nevertheless, this study provides compelling evidence to support the validity and use of the FSIQ in people with PMS.

Mean fatigue ratings were higher in people with PMS than RRMS; furthermore, persons with PMS endorse both greater mental and physical fatigue. Measuring these subdomains of fatigue with a valid tool is critical for the development of targeted interventions and longitudinal monitoring. In the absence of a gold standard for subjective fatigue in people with MS, it is not possible to establish concurrent validity; however, the FSIQ is a valid and freely available tool with excellent convergent validity to capture the physical, mental, and emotional fatigue of people with PMS.

Characterizing Fatigue by Multiple Sclerosis Subtype and Determining Validity of a Fatigue Scale Specific to Persons With Progressive Multiple Sclerosis

Patient-generated data are a cornerstone of individualized multiple sclerosis (MS) treatment. MyMS, an interface for patient-reported outcomes (PROs) was developed by the Finnish MS Register to enable systematic collection of PROs.

MyMS collects data on demographics, lifestyle factors, disease-related factors, and validated questionnaires, including the Quality of Life Questionnaire (15D), the Multiple Sclerosis Impact Scale (MSIS-29), and the Fatigue Severity Scale (FSS). At the end of 2020, the patient-reported Expanded Disability Status Scale (PREDSS), the EuroQOL-5 Dimension (EQ-5D), the Fatigue Scale for Motor and Cognitive Functions (FSMC), and the Multiple Sclerosis Neuropsychological Questionnaire (MSNQ) were added.

As of January 1, 2023, 1201 individuals with MS (79% female) have added data to MyMS. Of the validated PRO measures (PROMs), the 15D, MSIS-29, and FSS are the most used. The mean PREDSS score is 3.0 and median disease duration is 6.4 years. According to the existing PROMs, patients report mildly compromised quality of life and problems with fatigue and cognition.

The patient interface of the Finnish MS Register consists of data from 17 of 21 counties with well-being services. The interface is used by 10% of Finnish individuals with MS. The addition of the PREDSS, EQ-5D, FSMC, and MSNQ to the interface has increased health care professional and patient interest in the use of PROMs. We suggest that PROs should be integrated into electronic health records to improve shared decision-making and diminish documentation burden.

From the Department of Psychology and Speech-Language Pathology (PH), Department of Mathematics and Statistics (MV), Division of Clinical Neurosciences (JR, MS-H), University of Turku, Turku, Finland; The Finnish Neuro Society, Masku, Finland (PH, JR); StellarQ Ltd, Turku, Finland (PH, MV); Department of Neurology, Tampere University Hospital, Tampere, Finland (HK); Department of Health and Social Management, University of Eastern Finland, Kuopio, Finland (HK); Turku University Hospital Neurocenter, Turku, Finland (JR, MS-H). 

 Päivi Hämäläinen, PhD, StellarQ Ltd, Eerikinkatu 3 A, 20100 Turku, Finland; 

paivi.hamalainen@neuroliitto.fi, paivi.hamalainen@stellarq.com.

The authors gratefully acknowledge the contribution of the StellarQ IT team that built and continuously refines the Finnish MS Register platform. All members of the steering committee of the Finnish MS registry as well as patients who have participated in the development of MyMS are gratefully acknowledged.

 The study was financially supported by Janssen-Cilag Oy. The funder had no role in or influence on any aspect of the study, including data collection, data analysis, writing of the manuscript, and decision to publish. Preparation of this manuscript was funded in part by the Strategic Research Council within the Academy of Finland (funding No. 358415). Efforts to increase the use of the Finnish MS registry have received funding from Biogen Idec, Merck, Novartis, Sanofi Genzyme, Roche, Teva, and Business Finland.

 Päivi Hämäläinen and Matias Viitala are employees of StellarQ Ltd. The other authors of this manuscript do not declare any conflicts of interest related to this study.

High-quality patient registers are needed to improve the monitoring and treatment of progressive diseases such as multiple sclerosis (MS). MS registers are in use in many European countries,

 and the need to further develop register-based data collection is recognized worldwide.

 Registers allow individualized follow-up by providing systematic data on disease history, real-world data on the natural history of the disease, as well as data on treatment effectiveness, tolerability, and safety.

 They may also be used to predict an individual’s risk of conversion to secondary progressive multiple sclerosis (SPMS).

 Optimally, registers combine patient-reported data with data contributed by health care professionals (HCPs).

Along with the development of clinician-based registers, there is a growing need to implement patient engagement options, combining objective disease data with subjective patient-reported outcomes (PROs). This would promote patient autonomy, shared decision-making, and cost-effective individualized care.

 A PRO is any report of the status of an individual’s condition that comes directly from the individual, without interpretation by anyone, such as a clinician. PRO measures (PROMs) are standardized tools such as surveys, scales, or single-item measures. It is important that HCPs and the treatment team know how individuals perceive their disease, quality of life (QOL), treatment effects, and adverse events, and that means measuring outcomes that matter most to people with MS.

 Systematic collection, storage, and use of PRO data are likely to improve disease management and help the treatment team and the patient determine the best treatment options together.

Finland’s national MS register was launched in 2014

 to enable systematic disease monitoring as well as to monitor disease incidence, prevalence, and progression. A PRO interface was added in 2017 to enhance patient-centeredness and autonomy. Individuals are able to record data on medication usage, suspected relapses, lifestyle factors, and QOL, as well as keep track of disease severity, symptoms, and impact. The interface allows patients to follow their disease in a visual format and acts as a communication tool with the treatment team.

The objective of this study was to describe the contents and status of MyMS, the PRO interface of the Finnish national MS register. We illustrate the core variables and data collected by MyMS at the beginning of 2023. We also discuss future challenges, perspectives, and needs related to PRO-based data collection.

The Finnish MS Register is a browser-based register for public health care organizations.

Verena Witzig-Brändli, MScN

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