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From the Mellen Center for Multiple Sclerosis (EKW, ABS) and Sleep Disorders Center (EKW, MD), Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.

Short Report: Impact of Sleep Disorders on Depression and Patient-Perceived Health-Related Quality of Life in Multiple Sclerosis

The Multiple Sclerosis Resiliency Scale (MSRS) was designed to assess factors connected to resilience when facing MS-related challenges. Although the MSRS has demonstrated good internal consistency and construct validity, its test-retest reliability has yet to be established. Identifying the minimal detectable change (MDC) of the scale will also improve its utility as an outcome measure for resilience-based interventions. This study aimed to determine the test-retest reliability and MDC of the MSRS.

Participants were 62 persons with MS who completed the MSRS twice, with a mean ± SD of 16.60 ± 3.97 days (range, 14–30 days) between assessments. Test-retest reliability was evaluated using a 2-way, random-effects, single-measurement intraclass correlation coefficient (ICC), with agreement between time 1 and time 2 visualized with a Bland-Altman plot. The MDC was calculated using the standard error of measurement with a 95% CI.

At time 1, the mean ± SD MSRS score was 77.19 ± 11.97 (range, 45.83–97.00); at time 2, the mean ± SD score was 76.38 ± 12.75 (range, 46–98). The MSRS total score had good test-retest reliability (ICC = 0.88), with the subscale ICCs ranging from 0.77 (MS Peer Support) to 0.93 (Spirituality). The MDC for the total score was 11.95.

These findings suggest that the MSRS has good test-retest reliability and that persons with MS with a difference of 12 points or more between assessments have experienced a reliable change. The results support the utility of the MSRS as a potential outcome measure for MS-related resilience.

From the Mandell Center for Multiple Sclerosis, Mount Sinai Rehabilitation Hospital, Trinity Health Of New England, Hartford, CT, USA (ESG, LON, JAR, ACL); Department of Rehabilitative Medicine (ESG, LON, JAR) and Department of Medical Sciences (ESG, JAR), Frank H. Netter MD School of Medicine at Quinnipiac University, North Haven, CT, USA; Department of Neurology, University of Connecticut School of Medicine, Farmington, CT, USA (ESG); Multiple Sclerosis Center of Excellence West, Veterans Affairs, Seattle, WA, USA (APT); Rehabilitation Care Service, VA Puget Sound Health Care System, Seattle, WA, USA (APT); Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA (APT); Department of Family Medicine and the Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT, USA (TA); Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA (FWF); and Holy Name Medical Center Multiple Sclerosis Center, Teaneck, NJ, USA (FWF). 

: Elizabeth S. Gromisch, PhD, 490 Blue Hills Ave, Hartford, CT 06112, USA; 

: elizabeth.gromisch@trinityhealthofne.org.

The authors thank Dotty Wakefield, MS, for coding and management of the REDCap data collection and Aprille Gangi, Carly Gillen, and Amelia Kroschwitz for their assistance with data verification. The PDDS scale is provided for use by the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry (http://www.narcoms.org/pdds). NARCOMS is supported in part by the Consortium of Multiple Sclerosis Centers (CMSC) and the CMSC Foundation.

 The authors declare no conflicts of interest.

 The views and opinions expressed in this article are those of the authors and do not necessarily reflect those of the US Department of Veterans Affairs.

 grant from St Francis Hospital and Medical Center. Dr Gromisch is a Harry Weaver Scholar of the National Multiple Sclerosis Society.

 The results of this study were presented at the Consortium of Multiple Sclerosis Centers 2021 annual meeting; October 25-28, 2021; Orlando, Florida.

Persons with multiple sclerosis (MS) can be confronted with several challenges related to their disease, to which they can have any number of responses. Among persons with MS, resilience has been described as “bouncing back,”

 Resilience can have several positive effects on the lives of persons with MS. For example, high resilience has been associated with greater levels of physical activity and participation, better diet and psychological well-being, and lower levels of fatigue.

 On the opposite end of the spectrum, persons with lower levels of resilience may experience more psychological distress.

Measuring resilience to MS-related challenges provides the opportunity for clinicians to identify relative strengths as well as areas of needed support, both of which can inform targets for intervention. The Multiple Sclerosis Resiliency Scale (MSRS) is a multidimensional measure based on the conceptual framework that MS-related resilience is an interaction between protective factors and risk factors, which include psychological, physical, and social components.

 The 25-item MSRS produces a total score as well as 5 subscale scores: Emotional and Cognitive Strategies (13 items), Physical Activity and Diet (3 items), MS Peer Support (2 items), Support From Family and Friends (5 items), and Spirituality (2 items).

 For each item, respondents use a 4-point Likert scale ranging from “strongly agree” to “strongly disagree.”

Several of the psychometric properties of the MSRS have been evaluated, including its internal consistency, convergent validity, and divergent validity.

 Meaningful scores have also been derived with the Connor-Davidson Resilience Scale as a reference,

 including high (81) and low (75) resilience cutoff values and values for detecting psychological distress (ie, anxiety and depressive symptom severity; 68–72).

 However, the test-retest reliability and minimal detectable change (MDC) of the MSRS have yet to be determined. Besides being an important component of a measure’s psychometric properties, test-retest reliability and MDC are necessary for clinical trials to examine whether there has been a reliable change after an intervention.

Given the physical and psychological benefits of resilience,

 there have been several interventional studies focusing on promoting resilience in MS,

 but these studies have used the 10-item Connor-Davidson Resilience Scale

 which are not specific to MS. As such, this study aimed to establish the test-retest reliability and MDC of the MSRS to increase its utility as an outcome measure for resilience-focused interventions for persons with MS.

The study was conducted at the Joyce D. and Andrew J. Mandell Center for Comprehensive Multiple Sclerosis Care and Neuroscience Research at Mount Sinai Rehabilitation Hospital, Trinity Health Of New England. Surveys were collected between November 2020 and March 2021. Individuals were eligible for the study if they were diagnosed as having MS by a neurologist, were aged 18 to 89 years, were able to read and respond to English-language questionnaires, and had access to the internet and a valid email address. Participants were individuals who had previously expressed interest in research and were contacted about the present study via email or phone call. A sample size of 62 was determined to be adequate to power the test-retest reliability analyses and account for a 20% dropout rate.

The study was approved by the Trinity Health Of New England institutional review board. All data collection was performed using REDCap electronic data capture tools hosted at the University of Connecticut Health Center.

 At time 1, participants completed the MSRS, a demographics questionnaire, and the Patient-Determined Disease Steps (PDDS) scale to measure MS-related disability.

 Two weeks later, participants received an email from REDCap to complete the MSRS a second time. This time frame was selected because resilience was not thought to change during this small window. Another REDCap email was automatically sent a week later if participants had not yet taken the second survey. Follow-up phone calls were made as needed to ensure that participants were receiving the survey links. Participants who completed both parts of the study were compensated with a $25 gift card.

A statistical software program (IBM SPSS Statistics for Windows, version 26.0; IBM Corp) was used to conduct the analyses. First, descriptive statistics were run for the demographics and the MSRS scores at time 1 and time 2. If there were missing items on the MSRS (n = 2 at time 1; n = 1 at time 2), the value was imputed using the participant’s mean score on the particular subscale (eg, Emotional and Cognitive Strategies). No participants had more than 1 missing response on the MSRS, and none of the missing responses occurred in the smaller subscales (eg, MS Peer Support). The internal consistency of the total and subscale scores was evaluated using Cronbach α or, if there were only 2 items in the subscale, Spearman-Brown statistic. Test-retest reliability was assessed using a 2-way, random-effects, single-measurement intraclass correlation coefficient (ICC),

 with agreement between time 1 and time 2 visualized with a Bland-Altman plot.

 Proportional bias in the Bland-Altman plot was tested using a simple linear regression, with the mean of the time 1 and time 2 measurements as the independent variable and the difference between the 2 measurements as the dependent variable. Based on the ICC, the test-retest reliability could be described as poor (<0.50), moderate (0.50–0.75), good (>0.75–0.90), or excellent (>0.90).

 The standard error of measurement (SEM) was determined using the mean square error term’s square root,

 which was then used to calculate the MDC with a 95% CI using the following equation: MDC = SEM * 1.96 *√2.

Of the 64 participants who completed time 1, 62 (96.9%) completed time 2 (

 which is available at IJMSC.org). Most participants were White (82.3%), were female (77.4%), and had relapsing-remitting MS (85.5%). They had MS for a mean ± SD of 14.42 ± 8.32 years, with a median PDDS scale score of 2 (moderate disability). The mean ± SD time between the 2 assessments was 16.60 ± 3.97 days (range, 14–30 days).

Total scores could range from 25 to 100. Participants had mean ± SD scores of 77.19 ± 11.97 at time 1 and 76.38 ± 12.75 at time 2 (

). The total score had good test-retest reliability (ICC = 0.88), and the Bland-Altman plot (

) was not indicative of proportional bias (mean = 0.81; 95% CI, −11.13 to 12.75; 

 = .302). With an SEM of 4.31, the total score’s MDC was 11.95.

Emotional and Cognitive Strategies subscale scores could range from 13 to 52, with mean ± SD scores of 40.48 ± 7.97 at time 1 and 40.55 ± 8.18 at time 2 (Table 1). Good test-retest reliability was noted (ICC = 0.85), and the scale had an MDC of 8.79. The Bland-Altman plot (

) was not indicative of proportional bias (mean = −0.07; 95% CI, −8.85 to 8.71; 

With a score range of 3 to 12, the mean ± SD Physical Activity and Diet subscale scores were 8.98 ± 2.00 at time 1 and 8.82 ± 1.95 at time 2 (Table 1). The Physical Activity and Diet subscale had good test-retest reliability (ICC = 0.86), with an MDC of 2.05. The Bland-Altman plot (

) was not indicative of proportional bias (mean = 0.16, 95% CI, −1.88 to 2.20; 

The MS Peer Support subscale scores could range from 2 to 8, with mean ± SD scores of 5.76 ± 1.98 at time 1 and 5.44 ± 2.03 at time 2 (Table 1). Good test-retest reliability was noted (ICC = 0.77), and the scale had an MDC of 2.66. The Bland-Altman plot (

) was not indicative of proportional bias (mean = 0.32; 95% CI, −2.33 to 2.97; 

With a score range of 5 to 20, mean ± SD Support From Family and Friends subscale scores were 15.92 ± 3.17 at time 1 and 15.56 ± 3.48 at time 2 (Table 1). The Support From Family and Friends subscale had good test-retest reliability (ICC = 0.87), with an MDC of 3.30. The Bland-Altman plot (

) was not indicative of proportional bias (mean = 0.36; 95% CI, −2.93 to 3.65; 

Spirituality subscale scores could range from 2 to 8, with mean ± SD scores of 6.05 ± 2.21 at time 1 and 6.02 ± 2.08 at time 2 (

). Excellent test-retest reliability was noted (ICC = 0.93), with the scale having an MDC of 1.55. The Bland-Altman plot (

) was not indicative of proportional bias (mean = 0.03; 95% CI, −1.52 to 1.58; 

The MSRS demonstrated good test-retest reliability, both in terms of the overall measure and its individual subscales. The present results suggest that a change of 12 points or more on the MSRS total score is indicative of reliable change. This value can be used to identify individuals who have significant improvements (or declines) after a resilience training program. The MDCs for the subscales ranged from 2 to 9. Although clinicians or researchers may focus on change on the overall MSRS, focusing on an individual subscale’s change may be beneficial for targeted interventions. For example, if the goal is to increase the use of active coping strategies (vs emotion-focused coping), an increase of 9 points on the Emotional and Cognitive Strategies subscale may be more meaningful than a 12-point increase across the entire measure.

Although these findings strengthen the known psychometric properties and clinical utility of the MSRS, there are limitations to the present study. First, although the obtained sample size was adequate for the intended test-retest reliability analyses,

 the lower number of participants may have contributed to the reduced Cronbach α for the Physical Activity and Diet (α = 0.68) and Support From Family and Friends (α = 0.69) subscales at time 1. For reference, in larger cohorts, the internal consistency for these subscales has ranged from 0.77 to 0.84 for Physical Activity and Diet

 and from 0.74 to 0.80 for Support From Family and Friends.

 In addition, the alphas were greater than 0.70 at time 2, and the averaged alphas over time points always exceeded 0.70. Second, although nearly half of the participants completed time 2 exactly 14 days later, there were several outliers, with almost 18% (n = 11) responding to the second survey 3 weeks or more after the first. That said, these outliers do not seem to have a significant effect on the test-retest reliability scores of the MSRS. For example, the ICC for the total score decreased from 0.88 to 0.86 after removing these 11 individuals. For the subscales, the change in ICCs ranged from 0.007 (Spirituality) to −0.013 (MS Peer Support). Finally, data were not collected about any stressors, such as relapses, COVID-19, or other life events, that occurred between administrations of the MSRS and may have skewed responses and/or contributed to differences in responses between time 1 and time 2. Because all but the Emotional and Cognitive Strategies subscale had lower average scores at time 2, there is the possibility that participants experienced additional stressors within the 2-week period that could have lowered their scores at the second administration. Future research may consider validating the present findings in a second sample of persons with MS in a more controlled time frame and accounting for potential stressors that may affect scores.

Overall, the MSRS has good test-retest reliability, with a change of 12 points or more indicating reliable change. Each of the MSRS subscales had good to excellent test-retest reliability, with their MDCs ranging from 2 to 9. Combined with previous work on its psychometric properties,

 these results support use of the MSRS as a potential outcome measure for MS-related resilience.

Establishing the Test-Retest Reliability and Minimal Detectable Change of the Multiple Sclerosis Resiliency Scale

People with multiple sclerosis (MS) are living longer but not necessarily better lives, and this portends reduced health-related quality of life (HRQOL). Physical activity (PA) may be a correlate of HRQOL for people with MS. We examined differences in HRQOL and PA between older adults with and without MS to determine whether PA is associated with HRQOL and whether it accounts for group differences in HRQOL.

Thirty-one older adults with MS and 30 age- and sex-matched controls without MS completed the 36-Item Short Form Health Survey (SF-36) and the Godin Leisure-Time Exercise Questionnaire (GLTEQ). Data were analyzed using the Baron and Kenny approach for examining PA via the GLTEQ as a mediator of group differences in HRQOL.

The MS group had significantly lower component scores on the SF-36 and the GLTEQ than the control group. The GLTEQ scores were correlated with SF-36 physical component scores (

 = 0.52), whereas the correlation with mental component scores (

 = 0.23) was small and nonsignificant. Group assignment initially explained 31% of the variance in physical component scores (β = 0.55) and adding GLTEQ to the model accounted for an additional 12% of the variance in physical component scores. Thus, group (β = 0.42) and GLTEQ (β = 0.37) were both significant correlates of physical component scores. The group effect was modestly attenuated with the addition of GLTEQ in step 2 (step 1 β = 0.55; step 2 β = 0.42) and indicated partial rather than full mediation.

These results provide cross-sectional support for future research examining approaches to increase PA to possibly improve the physical component of HRQOL in older adults with MS.

From the Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA (RWM) and the Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, AL, USA (JFB). 

 Robert W. Motl, PhD, University of Illinois Chicago, 1919 W Taylor St, Chicago, IL 60612, USA; email: robmotl@uic.edu.

 This paper was supported by the National Multiple Sclerosis Society (grant CA-1708).

We are witnessing a demographic shift in the population of persons living with multiple sclerosis (MS) in the United States. Of the nearly 1 million adults living with MS in the United States,

 those in the 55- to 64-year-old age group are the most prevalent, and men 65 to 74 years old and women 65 to 74 years old are the second and third most prevalent groups, respectively.

 This suggests that there are more older adults than young and middle-aged adults living with MS; the shift has major implications for health and well-being.

The shifting age demography of MS indicates that it is a lifelong disease wherein the effects of normal aging occur in conjunction with the effects of MS (ie, aging with a long-term disabling disease).

 People with MS are living longer but not necessarily better lives, and this portends reduced health-related quality of life (HRQOL) in older adulthood. There are many changes in health status among older adults with MS, including increased rates of comorbid conditions such as hypertension, hyperlipidemia, heart disease, and diabetes.

 There are other data indicating reduced lower-extremity physical function in older adults with MS.

 Such changes can compromise HRQOL in older adults with MS, and this is supported by research documenting reductions in physical and/or mental health status compared with age-matched adults without MS.

Physical activity (PA) too is reduced in older adults with MS and may be a leading correlate of HRQOL in this population. There are data supporting lower levels of PA in older adults with MS compared with controls of the same age without MS

 and also data of declines in PA with increasing age in people with MS.

 Physical activity has been associated with HRQOL in older adults in the general population

 as well as in young and middle-aged adults with MS.

 This finding supports the notion that PA might represent an important target of behavior change interventions to improve HRQOL among older adults with MS. To date, there is limited research comparing HRQOL and PA between older adults with MS and older adults without MS that further examines whether PA levels account for group differences in HRQOL.

This study examined levels of HRQOL and PA in older adults with MS compared with age- and sex-matched controls. We hypothesized differences in HRQOL and PA between groups, and further hypothesized that PA would be associated with HRQOL and account for group differences in HRQOL. If correct, such results would provide cross-sectional support for future research examining approaches for increasing PA behavior and improving HRQOL in older adults with MS.

This study represents a secondary analysis of data from a project examining cardiorespiratory fitness and brain structure in older adults with MS and controls without MS.

 Participants were recruited in the Birmingham, AL area through flyers posted in the community, letters sent to past research volunteers, and word of mouth. Those who responded to the call for participants were screened for the following study inclusion criteria: (1) physician-confirmed diagnosis of MS; (2) no MS relapses in the past 30 days; (3) 55 years or older; (4) ambulatory with or without an assistive device; (5) no contraindications for MRI (eg, free of metal implants); and (6) low risk of contraindications for strenuous exercise based on answering “no” to all the questions on the Physical Activity Readiness Questionnaire

 or a single response of “yes” with a physician’s approval. The controls were matched on age (±5 years) and sex, and inclusion criteria for the control group were the same except for MS diagnosis and relapse status. All control participants were free of neurologic disorders (eg, stroke, Parkinson disease).

The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) was appropriate for all study participants and provides a generic measure of HRQOL.

 The survey answers were summed into 2 scores (0–100) representing physical health and mental health; higher scores indicated better HRQOL.

 The validity and reliability of the SF-36 is well established among persons with MS

The Godin Leisure-Time Exercise Questionnaire (GLTEQ) provides a self-reported measure of health-promoting PA

 and is a reliable and valid measure in persons with MS

 The total GLTEQ score is calculated by multiplying the number of 15-minute or longer mild, moderate, and strenuous bouts of PA recorded in the past 7 days by weights of 3, 5, and 9, respectively, and then summing those values into an overall score (0–119). The health contribution score (HCS), our measure of health-promoting PA, is calculated by adding only the moderate and strenuous scores into an overall score (0–98).

The study procedure was approved by the University of Alabama at Birmingham institutional review board, and participants provided written informed consent before enrolling in the study. Participants completed 3 study visits, and all data contained in the present analyses were collected during the first study visit. The first visit took place at our laboratory, where all participants completed a demographics questionnaire, including clinical characteristics for those with MS, as well as the SF-36 and GLTEQ. Participants with MS further underwent a neurologic evaluation by a Neurostatus-certified examiner (level C) to establish disability status based on the Expanded Disability Status Scale. The participants completed 2 subsequent visits for other measures of the primary study, but these are not applicable to the present study.

Data were analyzed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp) with an unadjusted significance level of 

 < .05. We first examined differences in age, sex, and race between participants with MS and participants without MS using independent-samples 

 tests, as appropriate. We then examined the data using the 3-step approach outlined by Baron and Kenny

 to investigate whether PA was an explanatory variable (ie, mediator) accounting for group differences in HRQOL. Step 1 tested group differences in HRQOL and PA using independent-samples 

 tests. The magnitude of differences between groups was expressed as Cohen’s 

 with values of 0.2, 0.5, and 0.8 interpreted as small, moderate, and large, respectively.

 In step 2, bivariate Pearson correlations (

) were calculated between PA and measures of HRQOL in the overall sample rather than in separate subsamples. Values of 0.1, 0.3, and 0.5 were interpreted as small, moderate, and large, respectively.

 The last step involved hierarchical linear regression analyses with direct entry of variables using the overall sample rather than separate subsamples, whereby we regressed HRQOL (SF-36 physical and mental composite scores) by group (MS and control) in step 1, and then added PA (the HCS from the GLTEQ) in step 2. We examined the β coefficients to identify the independent contributions of the variables in the model as well as model fit based on 

 per step of the model. Mediation was determined based on a comparison of the group’s standardized β coefficient between step 1 and step 2 of the regression, whereby attenuation of the group β coefficient toward zero and nonsignificance indicated full mediation; partial mediation was based on attenuation of the β coefficient.

Demographic and clinical characteristics are provided in 

, which is published online at IJMSC.org. The MS group (n = 31) and the control group (n = 30) were similar in age, sex, and race. The MS group predominantly had relapsing-remitting MS, a mean disease duration of 18.3 years, and moderate disability based on a median Expanded Disability Status Scale score of 4.0.

The statistics for HRQOL and PA for the MS and control groups are provided in 

. The MS group had significantly lower scores on the mental and physical components of the SF-36 and, furthermore, had a statistically significantly lower HCS on the GLTEQ. The differences in the physical component of HRQOL (

 = 0.80) were large in magnitude, whereas the differences between groups in the mental component of HRQOL (

Associations Between PA and HRQOL in the Overall Sample

The GLTEQ HCS had a large correlation with the physical component score from the SF-36 (

 < .001). The HCS from the GLTEQ had a small and nonsignificant correlation with the mental component score from the SF-36 (

 = .09). There was no correlation between the mental and physical SF-36 component scores (

 = .99), and this is consistent with the notion of the 2 distinct concepts being measured by the SF-36.

Regression Analyses for HRQOL in the Overall Sample 

The results of the regression analysis examining whether PA accounted for group differences in HRQOL are provided in 

. We did not perform regression analyses for the mental component of HRQOL because it was not associated with PA and did not meet the conditions set forth by Baron and Kenny.

 Group differences initially explained 31% of the variance in the physical component of HRQOL (β = 0.55). The inclusion of PA in the model accounted for an additional 12% of the variance in the physical component of HRQOL, and group (β = 0.42) and PA (β = 0.37) were both significant correlates of HRQOL. The effect of group was modestly attenuated with the addition of PA in step 2 (step 1 β = 0.55, step 2 β = 0.42), and this is consistent with partial rather than full mediation.

This study examined levels of HRQOL and PA in older adults with MS compared with age- and sex-matched controls without MS. We observed differences in HRQOL and PA between groups, and PA partially accounted for group differences in the physical component of HRQOL.

Older adults with MS reported lower levels of physical and mental HRQOL than age- and sex-matched controls without MS. The differences between groups exceed a half standard deviation (ie, Cohen’s 

 of 0.5), the threshold established for meaningful HRQOL differences.

 The difference in the physical component of HRQOL was nearly 2.5 times larger than the difference in the mental component of HRQOL. This corresponds with the emergence of declines in both walking performance and lower-extremity physical function that occur with increasing age in MS,

 whereas there are less pervasive changes in domains of mental health with increasing age in MS.

 To that end, the changes in the physical component of HRQOL may represent a perceived outcome of declines in physical function, and this should be examined in future research. Importantly, previous research has documented associations between PA, walking performance, and lower-extremity function in older adults with MS,

 and this would align with our interest in PA as an important determinant of physical HRQOL in older adults with MS.

Older adults with MS further demonstrated a reduction in health-promoting PA compared with age- and sex-matched controls without MS. The difference between groups in HCSs was large in magnitude, and this was consistent with previous research reporting reductions in PA as a function of aging in MS

 and when comparing older adults with MS and older adults without MS.

 The reduction in PA among older adults with MS has been explained, in part, by variables from Social Cognitive Theory,

 and this may support future efforts toward the design and delivery of theory-based behavior change interventions for this group.

We examined PA as a mediator of group differences in the physical component of HRQOL, as the mental component did not meet the necessary preconditions for mediator variable analysis (ie, there was no correlation between HCS and the mental component of HRQOL). The data indicated that PA, based on the HCS, satisfied the preconditions for examining mediation and partially accounted for the group effect on the physical component of HRQOL. This suggests that PA may be only 1 of many targets to improve the physical component of HRQOL in older adults with MS, and the partial mediation suggests that future research might examine factors other than PA to explain the reduced physical component of HRQOL. Such additional factors might include resilience, healthy lifestyle factors (eg, diet and nutrition), social support, depression, and fatigue.

 Nevertheless, our initial results lend some support for developing theory-based behavior change interventions to increase PA and for examining the secondary effects on the physical component of HRQOL in older adults with MS.

There are obvious limitations to the present study. The most significant are the cross-sectional research design and the self-report measure of PA. The design precludes causality inferences among variables; this could be overcome in future longitudinal or intervention trials. The self-report measure of PA may support that the correlation with the physical component of HRQOL is driven by similar measurement methods (ie, self-report). We counter that concern based on the lack of an association between PA and the mental component of HRQOL but recognize that future research should include device-based measurement of PA. We did not examine the results as a function of disability status overall or subsystem scores within MS, and future researchers might do so with a larger sample to inform the design and delivery of targeted interventions by therapists and trainers. We did not include a measure of fatigue to consider its association with PA in the present analysis, and future research might clarify its bidirectional association with PA and, perhaps, HRQOL. Finally, the sample was primarily female and White race: the results cannot be broadly generalized.

The results provide initial support for future research examining approaches to increase health-promoting PA and to examine secondary improvements in the physical component of HRQOL among older adults with MS. Such future research, if positive, may support efforts toward maximizing longevity and quality of life among the growing population of older adults with MS in the United States.

Health-Related Quality of Life and Physical Activity in Older Adults With Multiple Sclerosis

 To access the article and evaluation online, go to 

 The target audience for this activity is physicians, advanced practice clinicians, nursing professionals, and other health care providers involved in the management of patients with multiple sclerosis (MS).

Recognize the importance of comorbidities in the progression of disability in patients with MS.

Apply evidence-based clinical strategies for managing comorbidities in patients with MS to improve clinical outcomes.

In support of improving patient care, this activity has been planned and implemented by the Consortium of Multiple Sclerosis Centers (CMSC) and Intellisphere, LLC. The CMSC is jointly accredited by the Accreditation Council for Continuing Medical Education, the Accreditation Council for Pharmacy Education, and the American Nurses Credentialing Center, to provide continuing education for the health care team.

This activity was planned by and for the health care team, and learners will receive 0.5 Interprofessional Continuing Education (IPCE) credit for learning and change.

 The CMSC designates this journal-based activity for a maximum of 0.5 

™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

: The CMSC designates this enduring material for 0.5 contact hour of nursing continuing professional development (none in the area of pharmacology).

 It is the policy of the CMSC to mitigate all relevant financial disclosures from planners, faculty, and other persons that can affect the content of this CE activity. For this activity, all relevant disclosures have been mitigated.

, has served as physician planner for this activity. He has disclosed no relevant financial relationships. 

, has disclosed no relevant financial relationships. Authors 

Marco Aurélio Gralha de Caneda, MD, MSc, Camila Batista de Oliveira Silva, PhD

, have disclosed no relevant financial relationships.

, CMSC, and Intellisphere, LLC, who are in a position to influence content, have disclosed no relevant financial relationships. 

, CMSC continuing education director, has served as a planner and reviewer for this activity. She has disclosed no relevant financial relationships.

Release Date: September 1, 2024; Valid for Credit through: September 1, 2025

(1) Review the continuing education information, including learning objectives and author disclosures.

(3) Complete the evaluation, which is available at https://www. highmarksce.com/mscare.

Statements of Credit are awarded upon successful completion of the evaluation. There is no fee to participate in this activity.

 This educational activity may contain discussion of published and/or investigational uses of agents that are not approved by the US Food and Drug Administration. The CMSC and Intellisphere, LLC, do not recommend the use of any agent outside of the labeled indications. The opinions expressed in the educational activity are those of the faculty and do not necessarily represent the views of the CMSC or Intellisphere, LLC.

 Participants have an implied responsibility to use the newly acquired information to enhance patient outcomes and their own professional development. The information presented in this activity is not meant to serve as a guideline for patient management. Any medications, diagnostic procedures, or treatments discussed in this publication should not be used by clinicians or other health care professionals without first evaluating their patients’ conditions, considering possible contraindications or risks, reviewing any applicable manufacturer’s product information, and comparing any therapeutic approach with the recommendations of other authorities.

Comorbidities negatively impact the course of multiple sclerosis (MS). Identifying them is essential, as they represent potentially modifiable prognostic factors that can adversely influence the disease course. However, comorbidity prevalence remains underexplored in certain populations, including in individuals in Brazil.

In this cross-sectional study, we describe the frequency of comorbidities and their correlation with MS disability progression in a Brazilian population by reviewing the medical records of patients from a single MS center in Brazil. Preexisting comorbidities and those present at the time of MS diagnosis were screened. We assessed the prevalence of comorbidities, their prevalence ratios (PR) and the association between them, their number, and the confirmed disability worsening (CDW) that emerged during the follow-up visits.

Comorbidities were present in 68.9% of individuals. The most prevalent comorbidities included cardiovascular diseases (19.3%), migraine (13.3%), psychiatric disorders (12.4%), smoking (12.4%), autoimmune diseases (12.0%), respiratory diseases (10.3%), and neoplasms (5.6%). Patients with 1 comorbidity and those with multiple comorbidities (≥ 3) had a significant PR for CDW (2.67, 

 = .03, respectively). Cardiovascular and autoimmune diseases presented significant PR for CDW (2.28, 

Comorbidities are more prevalent among Brazilian individuals with MS than in the general population and are associated with disease progression. Identifying and managing them may mitigate their adverse effects on disease course.

From the Multiple Sclerosis and Demyelinating Disorders Unit, Department of Neurology, Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul, Brazil (MAGC); Vecino Medical Center, Porto Alegre, Rio Grande do Sul, Brazil (CBOS); Multiple Sclerosis and Demyelinating Disorders Unit, Department of Neurology, Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul, Brazil (MCAV). 

Marco Aurélio Gralha de Caneda, MD, Desing Office Center, 630; Ramiro Barcelos St, 5th floor, CEP 90660-020; 

mcaneda@terra.com.br; magdecaneda@gmail.com.

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system that mainly affects young adults in the prime of their productive lives.

 Although substantial progress has been made in understanding MS, its etiology and heterogeneous course remain incompletely understood.

 Clinical, demographic, and psychosocial characteristics, along with individual genetic and epigenetic factors, are believed to influence disease onset and progression.

Emerging evidence also suggests that comorbidities may play a significant role in the MS clinical course.

 Comorbidities, chronic conditions distinct from the underlying disease, contribute to the overall burden of MS.

 Complications directly derived from the underlying disease, such as bladder disturbances, or transient conditions, such as acute infections, are not considered comorbidities.

 Regardless of disease phenotype, the most frequent MS comorbidities are depression, anxiety, hypertension, dyslipidemia, and chronic obstructive pulmonary disease (COPD), which are also highly prevalent even before MS diagnosis. The frequency does vary with age, highlighting the importance of monitoring these conditions across the entire population of individuals with MS.

Studies have demonstrated an association between comorbidities and various negative outcomes in MS, including delays in diagnosis, increased hospitalizations and mortality rates, higher social and individual costs, and worsening disability progression and quality of life.

 Additionally, comorbidities may influence the choice of disease-modifying drugs (DMDs) used for MS treatment, including switches between DMDs, since comorbidities may be associated with intolerance, worsening or onset of some pathologies, and adherence.

Therefore, identifying comorbidities is crucial for reducing their negative impact on the MS course, as they are potentially modifiable prognostic factors.

 Despite this, in many regions there is a paucity of research focusing on this aspect, particularly in Latin American countries, including Brazil.

 In this study, we aim to address this gap by describing the frequency of comorbidities and their correlation with disease progression in a population of individuals with MS from Brazil.

This cross-sectional study was conducted at the Multiple Sclerosis and Other Demyelinating Disorders Unit of Hospital Moinhos de Vento in Porto Alegre, Rio Grande do Sul. The local health department authority provides MS treatment, and according to information from this agency, 1944 individuals were undergoing treatment in the state of Rio Grande do Sul at the end of 2020.

The medical records of patients who received a diagnosis of MS from January 2, 1991, to December 30, 2020, designated as the prevalence day (PD; ie, the proportion of a population that has a specific characteristic in a given time period), were retrospectively reviewed. These were captured through coding, and MS diagnoses were confirmed by medical progress notes or laboratory test results. The comorbidities screened in the study were selected according to literature data and included both preexisting conditions and those present at the time of MS diagnosis.

 No particular scale was used for screening comorbidities. Instead, comorbidities were identified based on documentation in the medical records by health care providers. They encompassed cardiovascular diseases (hypertension, dyslipidemia, ischemic and other heart diseases, stroke, obesity, and type 2 diabetes mellitus); respiratory pathologies (COPD, asthma, and chronic bronchitis); neoplasms; autoimmune diseases (thyropathies, inflammatory bowel diseases, rheumatologic and dermatologic pathologies); liver diseases; epilepsy; migraine; smoking; and psychiatric disorders.

Emily K. White, PhD

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