Journal of Multiple Sclerosis

Shared Decision-Making and Satisfaction with the Treatment Decision-Making Process in Multiple Sclerosis

Emily C. Casady¹, Brian C. Healy^1,2,3, Tarun Singhal^1,2, Jonathan D. Zurawski^1,2, Tanuja Chitnis^1,2, Howard L. Weiner^1,2 and Bonnie I. Glanz^{1,2* *}Correspondence: Bonnie I. Glanz, Brigham Multiple Sclerosis Center, Brigham and Women’s Hospital, Boston, MA, United States, Email:

Author info »

Introduction

Multiple Sclerosis (MS) is a chronic demyelinating disease of the central nervous system with a usual onset between the ages of 20 and 40 years. Persons with MS (pwMS) have wide-ranging physical, emotional, and cognitive symptoms and a variable disease course typically characterized by early relapsing and remitting disease with a risk of gradual disease progression in the later stages [1,2]. Today, there are more than 20 Disease-Modifying Therapies (DMTs) approved by the US Food and Drug Administration which have shown efficacy in reducing disease activity in MS, primarily in the relapsing-remitting form of the disease [3]. Increasingly, DMTs for progressive forms of MS are also available, although these are comparatively less effective, and the treatment of progressive MS remains a challenge [4,5]. While there are clear benefits of treatment in reducing relapses and clinical disability, DMTs also have potentially serious side effects [1,4-6]. Additionally, there are multiple approaches to treatment and many points in the disease course when treatment decisions need to be made; however, there is no indicated single best course of treatment [6-8]. While the consensus among physicians seems to be that early intervention is crucial in disease management, the array of treatment options available and potential risks involved in using high-efficacy medications require that pwMS establish long-term relationships with their healthcare providers who can adapt strategies based on disease and individual factors or preferences to optimize the management of the disease [9].

Shared Decision-Making (SDM) is a method of medical decision-making distinguished by its emphasis on patient knowledge, flow of information between patient and physician, and a shared responsibility between patient and physician regarding the final treatment choice [10,11]. SDM has been characterized as an ethically imperative approach to treatment decision-making given its focus and impact on patient satisfaction with care compared with previous models of patient-physician relationships such as paternalism and physician-as-expert [11-13]. SDM can also be viewed as a practical approach for balancing the potentially conflicting ethical principles of patient autonomy and beneficence, with the ultimate aim of improving patient outcomes and patient satisfaction [14]. While SDM is not feasible in every medical encounter, such as in acute emergency situations where there is often a single agreed-upon action that needs to be taken by a physician, it is ideally suited for conditions where long-term care is necessary, particularly in chronic conditions where there are multiple courses of action available to the physician and patient which rely heavily on preference and information [11]. This framework makes it well-suited for MS, a disease often diagnosed in early adulthood with a lifelong variable course that requires treatment decisions at different points using treatments that carry wide ranging risks [7,8,12,15].

There are several validated methods of assessing SDM in medical encounters. The nine-item Shared Decision Making Questionnaire (SDM-Q-9) is a patient-reported measure synthesized from the full Shared Decision making Questionnaire [16]. The SDM-Q-9 has been shown to be a reliable, brief, well-accepted instrument to measure the process of SDM [17]. Further, the SDM-Q-9 has demonstrated good psychometric properties and therefore may be appropriate in assessing SDM in pwMS [18]. In this study, we sought to characterize SDM among pwMS followed at a specialty MS Center using the SDM-Q-9. In addition, we examined whether experiences related to the treatment decision-making process differed based on individual and/or disease characteristics. Finally, we asked pwMS to identify additional information that they would have liked to have had available at the time of a treatment decision (in retrospect), and what kinds of decision aids they believe would have been most helpful.

Treatment decision-making questionnaire for climb study participants

1. What treatment are you currently taking for your multiple sclerosis? (Tick the box)

2. Please think back to your most recent treatment decision making process.

3. How satisfied are you with your most recent treatment decision making process?

4. When you made your most recent treatment decision, what additional information would you have liked? (Check all that apply)

₁ Ability of different treatments to reduce number of relapses

₂ Ability of different treatments to reduce disability worsening

₃ Likelihood of minor side effects on different treatments

₄ Likelihood of serious side effects on different treatments

₅ Impact of different treatment on fatigue or quality of life

₆ Cost of different treatments

₇ Convenience of different treatments

₈ Other: (Please specify) ____________________________

5. If you wanted more information, what decision aids would have been most helpful?

₁ Additional time with my doctor

₂ Pamphlets at the Partners MS Center

₃ Monthly emails about treatments from the Partners MS Center

₄ Videos about treatments

₅ Information on the Partners MS Center website

₆ Other: (Please specify) ____________________________

6. In general, people often face risks when making financial, career or other life decisions. Overall, how would you place yourself on the following scale from 1-7?

7. Please estimate the risk of your MS worsening over the short- (2 years), medium- (5 years) and long-term (10 years).

8. What is your age?

₁ 18 years-24 years

₂ 25 years-34 years

₃ 35 years-44 years

₄ 45 years-54 years

₅ 55 years-64 years

₆ Age 65 or older

9. What is your gender?

₁ Female

₂ Male

₃ Other

10. Which of the following statements best describes your MS symptoms? Choose one.

₁ I have had one episode of neurologic change (attack) suggestive of MS, but do not yet carry the diagnosis of MS.

₂ I have changes on MRI suggestive of MS, but I have not had any neurologic symptoms and do not yet carry the diagnosis of MS.

₃ I had flare ups (also called relapses, attacks, or exacerbations) when I first developed MS, and I continue to have flare ups of my MS.

₄ I had flare ups (also called relapses, attacks, or exacerbations) when I first developed MS, but am currently stable.

₅ I had flare ups (also called relapses, attacks, or exacerbations) when I first developed MS. My level of function is now getting steadily worse during and between flare ups, and I am having fewer and fewer (or none at all) flare ups.

₆ I have never had flare ups (also called relapses, attacks, or exacerbations) of my MS. Instead, my level of function has gotten steadily worse since the onset of my disease.

11. Please select the one statement that best describes how much your MS has restricted your activity over the last 4 weeks. Choose only one.

₁ I have no difficulty walking 25 feet (25 feet is approximately the length of two parking spaces on a city street) and I have no other neurologic symptoms due to my MS.

₂ I have no difficulty walking 25 feet (25 feet is approximately the length of two parking spaces on a city street), but I have the following neurologic symptoms due to my MS… Check all that apply.

_2a Vision problems

_2b Weakness, numbness, or tingling in my arms or legs

_2c Coordination or balance problems

_2d Speech problems

_2e Swallowing problems

_2f Bladder problems

_2g Bowel problems

_2h Attention, memory, or thinking problems

₃ I have difficulty walking 25 feet, but I do not use a cane or some other form of support (such as a splint, brace, or crutch) to help me walk (25 feet is approximately the length of two parking spaces on a city street.)

₄ I can walk 25 feet without a cane or some other form of support (such as a splint, brace, or crutch) but I use this occasionally or for longer distances (25 feet is approximately the length of two parking spaces on a city street.)

₅ To be able to walk 25 feet, I must use a cane or some other form of support on one side such as holding on to furniture or touching the wall. I may use a scooter or wheelchair for longer distances (25 feet is approximately the length of two parking spaces on a city street).

₆ To be able to walk 25 feet, I must use two canes, a walker, or two crutches. I may use a scooter or wheelchair for longer distances (25 feet is approximately the length of two parking spaces on a city street).

₇ My only form of mobility is a wheelchair or scooter.

Methods

PwMS enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women’s Hospital (CLIMB) were eligible to participate. CLIMB is an ongoing prospective observational cohort study that began following patients in 2000 [19]. More than 2000 participants have been enrolled. Additional inclusion/exclusion criteria for the study include diagnosis of MS according to the revised McDonald criteria, age 18 or older, and a patient visit to the Brigham MS Center in the last 24 months [20]. Recruitment letters and questionnaires were mailed to 500 randomly chosen CLIMB participants meeting inclusion/exclusion criteria for this study. Surveys could be completed and mailed back in postage paid envelopes. Surveys could also be completed online using a provided URL. Thirty-eight questionnaires were returned for having incorrect addresses and 161 were completed and returned for a response rate of 35%. Of the 161 participants who returned the questionnaire, 27 reported that they were not on treatment and did not contribute to our analysis, leading to a final sample size of 134 participants. This study was approved by the Mass General Brigham Human Research Committee (Protocol 2017P002814) as exempt human subjects research and consent was not required.

The Treatment Decision-Making Questionnaire for CLIMB Study Participants (see Supplementary Materials) was completed anonymously and included questions designed to measure SDM, satisfaction with the most recent treatment decision-making process, and patient and disease characteristics. First, participants were asked about their most recent treatment decision. Participants described the treatment chosen, completed the SDM-Q-9 to describe the process, and completed an additional question about satisfaction with the most recent treatment decision-making process. Satisfaction with the most recent treatment decision-making process was measured on a seven-point scale that ranged from extremely satisfied to extremely dissatisfied. Second, participants were asked about any additional information or decision aids that would have been helpful in the treatment decision-making process. Third, participants were asked about risk propensity and disease expectations in the short-, medium- and long-term. Risk propensity was measured using a single-item seven-point scale that ranged from extremely comfortable taking risks to extremely uncomfortable taking risks [21]. Disease expectations were measured by asking participants to estimate the likelihood of MS worsening in the short- (2 years), medium- (five years) and long-term (10 years) using a five-point scale that ranged from extremely unlikely to extremely likely. Finally, participants were asked demographic and clinical questions including patient-reported disease category and patient-reported disability [22].

Statistical methods

Summary statistics for each of the demographic and clinical questions were calculated as proportions because only categorical data were collected due to the anonymous nature of the questionnaire. For the SDM-Q-9 and the question about satisfaction with the treatment decision-making process, the proportions of participants who provided each answer were calculated. In addition to the descriptive statistics, the mean scores on the SDM-Q-9 questions and satisfaction with the treatment decision-making process question were examined in five separate two-group comparisons: high efficacy treatment vs. low efficacy treatment, relapsing MS vs. progressive MS, high disability vs. low disability, high risk propensity vs. low risk propensity, and high expectations of disease worsening vs. low expectations of disease worsening. The high efficacy treatments were ocrelizumab, fingolimod, rituximab, and natalizumab; the low efficacy treatments were all forms of interferon-beta, glatiramer acetate, dimethyl fumarate, and teriflunomide. Participants who reported being on other treatments were not included in this comparison. Participants were classified as progressive or relapsing using the patient-reported disease category question (Question 10). Participants were classified as high disability based on whether the score on the patient-reported disability question (Question 11) was greater than 2; participants were classified as low disability if the score was 2 or less. Participants were classified as having higher risk propensity if the participant was comfortable taking risk (score of 1-3 on the risk propensity measure) (Question 6), and participants were classified as having lower risk propensity if the participant was neither comfortable nor uncomfortable (score of 4-7 on the risk propensity measure). Participants were classified as having high expectations of disease worsening in the next five years if the participant thought they were likely or extremely likely to have disease worsening (score of 4-5 on the disease expectation question) (Question 7), and participants were classified as having low expectation of disease worsening in the next five years if the participant thought they were unlikely or neither likely or unlikely to have disease worsening (score of 1-3 on the disease expectation question). For each two-group comparison, the two sample t-test was used, and a 95% confidence interval for the estimated difference in group means was also calculated. All statistical analysis was completed in R version 3.6.3 (www.r-project.org).

Results

Participants

The demographic characteristics of participants are provided in Table 1. Given that the questionnaire was completed anonymously, information from the clinical record could not be combined with the questionnaire responses. Therefore, all clinical information was based on self-report. Study participants were primarily relapsing-remitting MS (RRMS) and they reported some disability due to the disease. In terms of treatments, participants were on a variety of treatments, and Ocrelizumab was the most common (21%) (Table 1).

Table 1. Demographic and clinical characteristics of study participants.

SDM

The results from the SDM-Q-9 are presented in Table 2. Participants described their most recent treatment decision as SDM across all the questions and the two most common responses for each of the nine items was strongly agree or completely agree. The highest level of agreement was for the final question: “My doctor and I reached an agreement on how to proceed”. Participants were also satisfied with their most recent treatment decision-making experience.

Table 2. SDM-Q-9 and satisfaction with the treatment decision-making process.

Comparisons of SDM-Q-9 and satisfaction with the most recent treatment decision across groups are provided in Table 3. There were no differences between the high efficacy and low efficacy treatments in terms of the SDM process or the satisfaction with the treatment decision-making process. Similarly, in participants with relapsing and progressive disease and low and high disability, there were no differences in SDM or satisfaction with the treatment decision-making process. In terms of risk propensity and disease expectations, there were also no differences in the decision-making process with the exception of a difference on question 2 of the SDM-Q-9, “My doctor wanted to know exactly how I want to be involved in making the decision.” For this question, participants with higher risk propensity had higher satisfaction, but this result should be interpreted cautiously given the number of multiple comparisons.

Table 3. Comparison of treatment decision-making questions by high efficacy vs low efficacy, progressive vs relapsing, high disability vs low disability, high risk propensity vs low risk propensity, and high risk of worsening vs low risk of worsening.

Decision aids

Participants were asked to identify additional information that they would like to have known at the time of their treatment decision and the best approach to disseminating this information (Table 4).

Table 4. Additional information for treatment decision-making.

Participants were most interested in additional information on the likelihood of serious side effects with different treatments, the impact of different treatments on fatigue or quality of life, and the ability of different treatments to reduce disability worsening, but at most 30% of participants expressed interest in each of these topics. Eighty-eight of 134 participants (65.7%) expressed interest in additional information on at least one topic. In terms of decision aids, additional information via email or a website were the most common responses, but all decision aids were chosen by fewer than 30% of participants.

Discussion

In this study, pwMS reported that nine previously identified practical steps of SDM are commonly used by providers at a specialty MS center. PwMS indicated that their providers made it clear that a treatment decision needed to be made and asked them how they wanted to be involved in the treatment decision-making process. Their providers told them that there were different treatment options, described the advantages and disadvantages of each treatment option, and helped them to understand the information. Their providers also asked them which treatment they preferred and weighed the options with them. Finally, PwMS and their providers selected a treatment together and reached an agreement on how to proceed. In addition, pwMS indicated that they were satisfied with the process used to make their most recent treatment decision. These findings suggest a shift away from the paternalistic style or physician-as-expert approaches previously used in healthcare. Instead, providers are combining their medical expertise and knowledge of DMTs with the personal preferences of the patient. Although we did not survey pwMS about treatment decision related to the management of relapses or physical symptoms, a recent systematic review found that pwMS demonstrate a strong preference for SDM in general, and particularly with respect to the management of MS-related physical symptoms including gait, balance, and fatigue [23].

Interestingly, we found that the SDM approach and individuals’ satisfaction with the process were observed in pwMS with different treatment regimens (high efficacy vs. low efficacy), disease courses (relapsing vs. progressive), disability levels (high vs. low disability), risk propensity profiles (high vs. low risk propensity), and expectations of disease worsening (high vs. low expectation of worsening). These findings suggest that providers at an academic MS center are using SDM strategies broadly across the clinical spectrum of MS, taking into consideration clinical presentation and preferences in the decision-making process. This is in contrast to previous studies suggesting that physicians are not meeting the needs of pwMS in complex medical decision-making [9].

Although SDM may be preferred due to ethical considerations, there is little evidence to support other potential benefits [9,13,24]. A 2015 review of SDM for a variety of disorders including asthma, cancer, depression, diabetes, epilepsy and HIV found that SDM resulted in improved patient satisfaction, but there was no clear evidence that SDM resulted in improved behavioral outcomes such as adherence or health outcomes such as improved symptoms or quality of life [25]. In a narrative review of SDM in MS, Ben Zacharia et al. found that overall there was weak evidence supporting the benefits of SDM on DMT adherence [26]. Positive effects on adherence were mostly seen in observational studies that relied on surveys and questionnaires. There are several limitations associated with the use of self-report measures, including that they are subject to social desirability and memory biases that may result in an overestimation of DMT adherence. Ben Zacharia et al. stressed the difficulties inherent in studying the impact of SDM in chronic conditions with partially effective treatments and few standardized measures [26,27].

Our survey results demonstrate that many pwMS felt that they had adequate information needed to make a treatment decision, but about 65% of pwMS were interested in additional information about the treatments. The likelihood of serious side effects, the impact of DMTs on fatigue or quality of life, and the ability of treatments to reduce disability worsening were the most common areas pwMS wanted more information. This finding is consistent with previous reports of unmet information needs in MS [27]. Educational programs have been developed to address whether or not to initiate early treatment, pregnancy and injectable DMT choice, side-effects, and relapses [28-31]. Given the increasing number of FDA-approved therapies for the treatment of MS and the lack of studies showing head-to-head treatment comparisons, it is challenging to draw conclusions regarding relative efficacy or risk across DMTs. 9 Despite these challenges, this information is needed to fully satisfy the requirements of the SDM process.

Two groups recently published papers on the development of decision aids to promote SDM in pwMS. Col et al. described the development of an interactive online decision aid, MS-SUPPORT (Sharing and Understanding Personal Preferences and Objectives Regarding Treatment), to encourage patient-provider collaboration and improve SDM [32,33]. The decision aid guides users through a series of structured modules to gather information on the patient’s goals, preferences, needs, situation, adherence, and health behaviors. A concise summary is then made available for patients to share with their providers. Col et al. compared the effects of MS-SUPPORT to usual care on DMT decisions, the SDM process, and quality of life in a multisite randomized controlled trial [33]. More than 80% of participants randomized to MS-SUPPORT reported that they would recommend it to others and that it helped them talk to their doctor and understand their options and the importance of taking the DMT as prescribed. Kremer et al. developed a prototype for a decision aid for pwMS to reduce the cognitive burden of considering treatment options [34]. The decision aid collects demographic and clinical data and asks pwMS to attach weights to characteristics such as reducing relapses, reducing progression, or safety that are most important to them. The decision aid then ranks treatment options according to patient preferences. Testing of the decision aid has not yet been completed. Future development and assessment of decision aids are important areas of investigation to aid in SDM for pwMS.

This study has several limitations. First, it was conducted at an MS specialty center with clinicians who focus primarily on the treatment of individuals with MS and may not be generalizable to other healthcare settings. Additional studies that include pwMS from different centers or regions are needed to improve the applicability of the findings. Second, the response rate was low which may indicate a response bias. It is possible that individuals who were happier with the treatment decision-making process were more likely to complete and return the questionnaire. This would mean that the missing data were not random. Third, the pwMS who participated in the study had a relatively mild disease course, and the findings may not reflect the larger population of MS patients with higher levels of disability.

In summary, pwMS treated at a specialty MS center reported high levels of SDM and satisfaction with the treatment decision-making process. Despite these findings, about 65% of pwMS were interested in receiving additional information about treatments. Additional research is needed to better understand the impact of SDM on behavioral and health measures in pwMS and to assess the role of decision aids in supporting and enabling pwMS to participate more fully in the SDM process.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Brigham Research Institute Patient-Centered Comparative Effectiveness Research Center.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Ms. Casady has no disclosures to report; Dr. Healy has received research support from Analysis Group, Celgene (Bristol-Myers Squibb), Verily Life Sciences, Merck-Serono, Novartis, and Genzyme; Dr. Singhal has received research support from Novartis Pharmaceuticals and Genzyme-Sanofi, consulting fees from Novartis Pharmaceuticals, speaking fees from Tiziana Life Sciences, and research funding from Nancy Davis Foundation's “Race to Erase MS” program, Ann Romney Center for Neurologic Diseases, Harvard Neuro-Discovery Center, National Multiple Sclerosis Society, Department of Defense, and Water Cove Charitable Foundation; Dr. Zurawski Dr. Zurawski has received research support from NIH, Novartis Pharmaceuticals, I-Mab Biopharma, and the Race to Erase MS Foundation; Dr. Chitnis has received compensation for consulting from Banner Life Sciences, Biogen, Bristol Myers Squibb, Novartis Pharmaceuticals, Roche Genentech, and Sanofi Genzyme. She has received research support from the National Institutes of Health, National MS Society, US Department of Defense, Sumaira Foundation, Brainstorm Cell Therapeutics, Bristol-Myers Squibb, EMD Serono, I-Mab Biopharma, Mallinckrodt ARD, Novartis Pharmaceuticals, Octave Bioscience, Roche Genentech, Sanofi Genzyme, and Tiziana Life Sciences; Dr. Weiner has received research support from Cure Alzheimer’s Fund, EMD Serono, Inc., Genentech, Inc., National Institutes of Health, National Multiple Sclerosis Society, Sanofi Genzyme, and Verily Life Sciences. He has received payment for consulting from Genentech, Inc, IM Therapeutics, I-MAB Biopharma, MedDay Pharmaceuticals, Tiziana Life Sciences, and vTv Therapeutics. He has served on the advisory board for: The Guthy Jackson Charitable Foundation, Teva Pharmaceutical Industries Ltd., Biogen Idec, Novartis, Sanofi-aventis, Tilos Therapeutics, Tiziana Life Sciences, CBridge Capital, IM Therapeutics, Magnolia Therapeutics, Genentech, Genzyme, vTv Therapeutics, MedDay Pharmaceuticals, Weston Foundation, and IMAB; Dr. Glanz reports research support from Merck Serono and Verily Life Sciences.

References

1. Dobson, Ruth, and Gavin Giovannoni. "Multiple sclerosis–a review." Eur J Neurol. 26.1 (2019): 27-40.

   [Google Scholar] [Crossref]

2. Weiner, Howard L., and James M. Stankiewicz, eds. “Multiple sclerosis: diagnosis andtherapy.” John Wiley Sons. 2012.

   [Google Scholar] [CrossRef]

3. Olek MJ. “Multiple Sclerosis”. Ann Intern Med. 2021

   [CrossRef]

4. Montalban, Xavier, et al. "Ocrelizumab versus placebo in primary progressive multiple sclerosis." N Engl J Med. 376.3 (2017): 209-220.

   [Google Scholar] [CrossRef]

5. Florou, Despoina, et al. "Anti-CD20 agents for multiple sclerosis: spotlight on ocrelizumab and ofatumumab." Brain Sci. 10.10 (2020): 758.

   [Google Scholar] [CrossRef]

6. Heesen, Christoph, et al. "Decisions on multiple sclerosis immunotherapy: new treatment complexities urge patient engagement." J Neurol Sci. 306.1-2 (2011): 192-197.

   [Google Scholar] [CrossRef]

7. Duquette, Pierre, et al. "Balancing early aggression against risk of progression in multiple sclerosis." Can J Neurol Sci. 43.1 (2016): 33-43.

   [Google Scholar] [CrossRef]

8. Köpke, Sascha, et al. "Steroid treatment for relapses in multiple sclerosis–the evidence urges shared decision‐making." Acta neurol scand. 110.1 (2004): 1-5.

   [Google Scholar] [CrossRef]

9. Colligan, Erica, Abby Metzler, and Ezgi Tiryaki. "Shared decision-making in multiple sclerosis." Mult Scler J. 23.2 (2017): 185-190.

   [Google Scholar] [CrossRef]

10. Barry, Michael J., and Susan Edgman-Levitan. "Shared decision making—The pinnacle patient-centered care." (2012).

    [Google Scholar] [CrossRef]

11. Charles, Cathy, Amiram Gafni, and Tim Whelan. "Shared decision-making in the medical encounter: what does it mean?(or it takes at least two to tango)." Soc Sci Med. 44.5 (1997): 681-692.

    [Google Scholar] [CrossRef]

12. Heesen, Christoph, et al. "Shared decision making and selfmanagement in multiple sclerosis–a consequence of evidence." J Neurol. 254 (2007): II116-II121.

    [Google Scholar] [CrossRef]

13. Seminar, Salzburg Global. "Salzburg statement on shared decision making." Bmj 342 (2011).

    [Google Scholar] [CrossRef]

14. Varkey, Basil. "Principles of clinical ethics and their application to practice." Med Princ Pract. 30.1 (2021): 17-28.

    [Google Scholar] [CrossRef]

15. Kasper, J., et al. "Informed shared decision making about immunotherapy for patients with multiple sclerosis (ISDIMS): a randomized controlled trial." Eur J Neurol. 15.12 (2008): 1345-1352.

    [Google Scholar] [CrossRef]

16. Doherr, Hanna, et al. "Use of the 9-item Shared Decision Making Questionnaire (SDM-Q-9 and SDM-Q-Doc) in intervention studies—a systematic review." PLoS One 12.3 (2017): e0173904.

    [Google Scholar] [CrossRef]

17. Kriston, Levente, et al. "The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample." Patient Educ Couns. 80.1 (2010): 94-99.

    [Google Scholar] [CrossRef]

18. Ballesteros, Javier, et al. "Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis." Health Qual Life outcomes. 15.1 (2017): 1-6.

    [Google Scholar] [CrossRef]

19. Gauthier, Susan A., et al. "A model for the comprehensive investigation of a chronic autoimmune disease: the multiple sclerosis CLIMB study." Autoimmun Rev. 5.8 (2006): 532-536.

    [Google Scholar] [CrossRef]

20. Thompson, Alan J., et al. "Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria." Lancet Neurol. 17.2 (2018): 162-173.

    [Google Scholar] [CrossRef]

21. Maestas, Cherie D., and William M. Pollock. "Measuring generalized risk orientation with a single survey item." Available at SSRN 1599867 (2010).

    [Google Scholar] [CrossRef]

22. Minden, S. L., et al. "Improving the quality of mental health care in multiple sclerosis." J Neurol Sci. 335.1-2 (2013): 42-47.

    [Google Scholar] [CrossRef]

23. Ben-Zacharia, Aliza Bitton, et al. "Shared decision-making in multiple sclerosis physical symptomatic care: a systematic review." Ther Adv Chronic Dis. 14 (2023): 20406223231172920.

    [Google Scholar] [CrossRef]

24. Alonso, Ricardo, et al. "Shared decision making in the treatment of multiple sclerosis: A consensus based on Delphi methodology." Mult Scler Relat Disord. 70 (2023): 104465.

    [Google Scholar] [CrossRef]

25. Shay, L. Aubree, and Jennifer Elston Lafata. "Where is the evidence? A systematic review of shared decision making and patient outcomes." Med Decis Mak. 35.1 (2015): 114-131.

    [Google Scholar] [CrossRef]

26. Ben-Zacharia, Aliza, et al. "Impact of shared decision making on disease-modifying drug adherence in multiple sclerosis." Int J MS Care. 20.6 (2018): 287-297.

    [Google Scholar] [CrossRef]

27. Köpke, Sascha, et al. "Information provision for people with multiple sclerosis." Cochrane Database Syst Rev 10 (2018).

    [Google Scholar] [CrossRef]

28. Solari, A., et al. "An information aid for newly diagnosed multiple sclerosis patients improves disease knowledge and satisfaction with care." Mult Scler J.16.11 (2010): 1393-1405.

    [Google Scholar] [CrossRef]

29. Köpke, Sascha, et al. "Evidence-based patient information programme in early multiple sclerosis: a randomised controlled trial." J Neurol Neurosurg Psychiatry. 85.4 (2014): 411-418.

    [Google Scholar]

30. Prunty, Martine C., et al. "The motherhood choice: a decision aid for women with multiple sclerosis." Patient Educ Couns. 71.1 (2008): 108-115.

    [Google Scholar] [CrossRef]

31. Colligan, Erica A., Abby Metzler, and Ezgi Tiryaki. "Which Injectable Medication Should I Take for Relapsing-Remitting Multiple Sclerosis? A Decision Aid to Discuss Options With Your Doctor." (2016).

    [Google Scholar] [CrossRef]

32. Col, Nananda, et al. "A novel tool to improve shared decision making and adherence in multiple sclerosis: development and preliminary testing." MDM Policy Pract.4.2 (2019).

    [Google Scholar] [CrossRef]

33. Col, Nananda F., et al. "Implementing Shared Decision-Making for Multiple Sclerosis: The MS-SUPPORT Tool." Mult Scler Relat Disord. 80 (2023).

    [Google Scholar] [CrossRef]

34. Kremer, I. E. H., et al. "Patient decision aid based on multi-criteria decision analysis for disease-modifying drugs for multiple sclerosis: prototype development." BMC Med Inform Decis Mak. 21 (2021): 1-18.

    [Google Scholar] [CrossRef]