Skip to main content
PLOS One logoLink to PLOS One
. 2022 May 13;17(5):e0268125. doi: 10.1371/journal.pone.0268125

Development of a scale for the evaluation of the quality of the shared decision process in multiple sclerosis patients

Elena Álvarez-Rodríguez 1, César Manuel Sánchez-Franco 2, María José Pérez-Haro 3, Laura Bello-Otero 1, Marta Aguado-Valcarcel 1, Inés González-Suárez 1,*
Editor: Fatih Özden4
PMCID: PMC9106213  PMID: 35560185

Abstract

In the last years, therapeutic decisions in multiple sclerosis (MS) have become challenging due to expanded options with different treatment profiles attending to efficacy, safety, and route and frequency of administration. Moreover, patients with multiple sclerosis (PwMS) increasingly wish to be involved in their therapeutic decision process. Therefore, a new, patient-centric shared decision model (SDM), is gaining relevance. However, validated scales oriented to assess the quality of the process itself are lacking. The AGA-25 scale is a fit-for-purpose 25-item scale based on two validated scales in MS (Treatment Satisfaction Questionnaire for Medication (TSQM) and Decisional Conflict Scale (DCS)). The aim of this work is to develop and validate the AGAS-25 in Spanish. Two hundred and three PwMS (aged 17 to 67; 155 [76.4%] females) undergoing stable disease modifying treatment in the last 6 months were consecutively recruited. The Principal Component Analysis suggested a four-factor structure for the 25-item version of the questionnaire: 1) satisfaction with the SDM process 2) adverse events with the DMT, 3) convenience of the chosen-DMT and 4) information reliability. The internal consistency of the measurement was adequate (Cronbach’s alpha = 0.88). Our results support the use of the AGAS-25 scale to assist SDM in Spanish-speaking PwMS.

Introduction

Multiple sclerosis (MS) is a chronic neurodegenerative disorder characterized by inflammation and progressive neurological destruction and degeneration [1]. Although there is no cure for MS, several disease modifying treatments (DMTs) have demonstrated to be effective by reducing the frequency of clinical relapses and disability progression. Nowadays, more than 15 different DMTs are available, the approved DMT landscape includes drugs with different therapeutic profiles based on their efficacy, safety, and route and frequency of administration, which impact patient preference and adherence [2].

Patients’ satisfaction is recognized as an important dimension of the quality of care since it has been related to patient compliance, doctor-patient information exchange and continuity of care. An important dimension of patient satisfaction is shared decision making (SDM) [3]. SDM is defined as an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options and to achieve informed preferences [4]. Therefore, the exchange of information is the central axis of the process.

In this exchange, both parts add their experience, ethos, and preferences to the final decision [5]. SDM recognizes that both the patient and the clinician shared different but equally valid experiences and expertise to the decision making process. At best, clinicians add their knowledge of the treatments, their outcomes and prognosis, whether the patient displays how the disease impacts their life, their personal values and risk tolerance [6]. SDM is recommended in the majority of healthcare decisions where there is more than one feasible option and is particularly suited for a chronic condition such as MS, improving patients’ perceptions of the benefits and risk of the different DMT [7].

Although the most widespread practice is explicit information, several approaches have been investigated. While there is currently no ‘gold standard’ in terms of measurement, the need to measure the process as well as the outcome is apparent [6]. Only a few scales are available to assess SDM process from the patients satisfaction point of view [8]. The 9-item Shared Decision Making Questionnaire (SDM-Q-9) [9], have revealed poor quality of evidence, suggesting that its value as an assessment tool may be limited [10]. Prior studies evaluating multiple sclerosis patients (PwMS) understandings and preferences have been developed [7].

A recent review demonstrated that the experience of many patients with information during the standard process does not provide satisfactory understanding of the risks and benefits of DMTs. It is known that PwMS tend to underestimate the risks associated with DMTs, which could lead to lower adherence rates and greater discontinuation due to adverse events [6]. However, some studies have observed an overestimated point of view of the DMTs that can also have an impact on adherence [11].

Therefore, the evaluation of the SDM process should not only assess the PwMS degree of involvement in making decisions but also whether the information received was consistent with their subsequent personal experience in terms of risk, efficacy, and satisfaction [12, 13].

Two of the most used scales in clinical practice are the Treatment Satisfaction Questionnaire for Medication (TSQM) and the Decisional Conflict Scale (DCS), both were designed as global scales. TSQM is used as a general measure of treatment satisfaction with medication. TSQM has been validated in several diseases, including MS [1416]. The scale evaluates the patient’s drug satisfaction in terms of effectiveness, side effects, convenience, and global satisfaction. The 16-item decisional conflict scale was developed to elicit information concerning the decision makers: 1) uncertainty in making a choice; 2) modifiable factors contributing to the uncertainty, such as lack of information, unclear values, and inadequate social support; and 3) perceived effective decision making [17]. Based on these scales we developed a 25-item scale to analyze the quality of the SDM in a MS outpatient clinic. The main aim of this work is to assess the psychometric properties of the self-administered questionnaire AGA-25 developed in Spanish to ensure the quality of the SDM process in MSPw.

Materials and methods

A written informed consent was obtained from each participant. The Clinical Research Ethics Committee of Galicia gave its approval to the study with a research code 2018/271. The investigations were consistent with the principles outlined in the Declaration of Helsinki.

Questionnaire development

The team used the Medline database to search prior studies. The keywords “shared-decision process”, “satisfaction questionnaire”, and “multiple sclerosis” were considered, and relevant information was classified and discussed afterwards [6, 7].

Formulation of the AGA questionnaire

  1. The Treatment Satisfaction Questionnaire for Medication (TSQM) comprises 14 items across four domains focusing on effectiveness (three items), side effects (five items), convenience (three items), and global satisfaction (three items) of the medication. TSQM was designed to assess patient treatment satisfaction in chronic diseases and has been validated into Spanish [18] and used in several diseases, including MS [1416]. This scale was developed for the analysis of satisfaction with oral medications. However, some authors believed that satisfaction measures are often misleading, as high satisfaction scores are more likely to be the result of low expectations than a high quality SDM process [18].

  2. The decisional conflict scale (DCS) is a measure of the uncertainty surrounding a treatment choice and patient confidence in making that decision; it also evaluates modifiable factors contributing to the uncertainty, such as lack of information, unclear values, and inadequate social support and perceived effective decision making [17]. However, it is not a measure of the quality itself.

Based in the Weaber conceptual Framework for Treatment Satisfaction [20]; three experienced physicians selected the most reliable items and developed a 25-items scale (see Table 1) looking for 4 different dimensions: 1) satisfaction with the information during the SDM (items 01, 02, 03, 04, 05, 06, 11, 12 and 25), exploring whether the doctor involved the patient in the decision, offered detailed information, answered questions or had to search information from other sources (INTERNET, patients, associations); 2) the adverse events during the selected treatment (items 15, 16, 17, 18 and 19), presence of adverse events with DMT and the interference in his/her day-to-day life; 3) DMT convenience (items 7,8,10,20 and 21), if the chosen DMT fits into the PwMS rhythm of life and 4) information reliability (items 13, 14, 22, 23 and 24), how satisfied the patient is with the current treatment and his/her confidence in him/herself to control the medication. The answers ranged from totally disagree, disagree, agree, to totally agree (1,2,3,4).

Table 1. Questionnaire design.

Item 01 Se me explicó detalladamente la razón por la que era necesario iniciar un tratamiento para la esclerosis múltiple.
The doctor explained me in detail why it was necessary to start a treatment for MS.
Item 02 Se me consultó cómo de implicado me gustaría estar a la hora de tomar decisiones con respecto al nuevo tratamiento.
I was asked how involved I would like to be in making decisions about my new treatment.
Item 03 Se me explicó que para mí esclerosis múltiple había diferentes opciones de tratamiento.
The doctor explained me, that there were multiple sclerosis treatment options.
Item 04 Se me informó de manera detallada de las ventajas de cada uno de los tratamientos para la esclerosis múltiple.
I was informed in detail of the benefits of each of the MS treatments.
Item 05 Se me resolvieron las dudas de manera que pude entender la información.
If I had doubts, they were resolved in a way that I could understand all the information.
Item 06 ¿En qué grado entendió en qué consistía el tratamiento elegido?
To what degree, did you understand the chosen treatment?
Item 07 ¿En qué grado entendió los efectos secundarios relacionados con la 1ª dosis?
To what degree, did you understand the side effects related to the first dose?
Item 08 ¿En qué grado entendió los efectos secundarios a largo plazo?
? To what degree, did you understand the long-term side effects?
Item 09 ¿Cómo percibe el riesgo de presentar un evento adverso a lo largo del tratamiento?
How do you perceive the risk of experiencing an adverse event throughout the treatment?
Item 10 En algún momento del proceso de toma de decisiones tuve que buscar información en otros medios.
During the decision-making process, I had to look for information in other media.
Item 11 La decisión fue tomada de manera conjunta entre el especialista y yo.
The decision was made jointly by the specialist and me.
Item 12 Tras decidir el tratamiento adecuado, decidimos el modo de proceder adecuado.
After deciding on the appropriate treatment, we decided the best procedure.
Item 13 ¿En qué grado cree que el medicamento es capaz de prevenir un brote de su enfermedad?
To what degree, do you think the medicine is able to prevent an outbreak of your disease?
Item 14 ¿En qué grado cree que el medicamento es capaz de prevenir la progresión de su enfermedad?
? To what degree, do you think the medicine is able to prevent the progression of your disease?
Item 15 ¿Padece efectos secundarios a consecuencia del medicamento?
Do you have any side effects from this medicine?
Item 16 ¿En qué grado le molestan esos efectos secundarios en su día a día?
To what degree, do these side effects bother you in your day-to-day life?
Item 17 ¿Hasta qué punto interfieren esos efectos secundarios en su salud física?
? To what degree, do these side effects interfere with your physical health?
Item 18 ¿Hasta qué punto interfieren esos efectos secundarios en su salud emocional?
To what degree, do these side effects interfere with your emotional health?
Item 19 ¿Hasta qué punto influyen estos efectos secundarios en su satisfacción con el medicamento?
To what degree, do these side effects influence your satisfaction with the medication?
Item 20 La forma de administración del medicamento le parece sencillo.
How simple do you find the way to administer the medicine?
Item 21 La planificación de la toma del medicamento le parece sencilla.
How simple is it to plan the shot?
Item 22 ¿Qué percepción tiene de la eficacia del medicamento a la hora de controlar su enfermedad?
What is your perception of the effectiveness of the drug in controlling your disease?
Item 23 Teniendo en cuenta ventajas e inconvenientes del fármaco, ¿cómo está de satisfecho con el mismo?
Considering the advantages and disadvantages of the drug, how satisfied are you with it?
Item 24 Las ventajas superan a las desventajas del fármaco.
The advantages outweigh the disadvantages of the drug.
Item 25 La información recibida durante el proceso de toma de decisiones ha sido acorde a la experiencia con el fármaco.
The information received during the decision-making process has been consistent with my experience with this drug.

Data collection

One hundred and forty-two PwMS were randomly selected from the MS outpatient clinic. The study was conducted during 2019 at the Hospital Álvaro Cunqueiro, a public hospital in Vigo, Spain. Inclusion criteria were 1) MS diagnosis using McDonald criteria 2010; 2) stable treatment in the last 6 months; 3) absence of relapses in the past 6 months; 4) capacity to sign an informed consent. Demographic data were collected including age and sex, DMT and time on DMT, type of MS, age at onset of the first symptom and to diagnosis, EDSS, TAB and total number of relapses, prior DMT and reason for the switch.

Patients understood the goal of the study and received the printed questionnaire. The survey was completed anonymously.

Statistical analysis

A descriptive analysis has been carried out. Quantitative features are shown through the mean (SD), median and range (minimum and maximum). On the other hand, qualitative variables are described by absolute and relative (%) frequencies.

The model was built with all the data, extracting the number of factors through a principal components analysis (PCA). Before performing PCA, Kaiser-Meyer-Olkin (KMO) index and Bartlett’s sphericity test were calculated to analyze its efficiency. The internal consistency of items and the reliability of each dimension were assessed through the Cronbach’s alpha reliability coefficient.

Statistical analyzes have been carried out with free software R (R Core Team 2020). The significance level was set up at 0.05.

Results

Descriptive analysis

A summary of the demographic and clinical features is shown in Table 2. Most of the patients (76.4%) were women with a mean age of 41.32 years (SD 8.73). Most of the patients were on stable first-line therapy (61.1% vs 38.9%). Patients were mildly disabled with a mean EDSS score of 2.14 (SD 1.76) and a mean disease duration of 8.80 years (SD 6.34).

Table 2. Descriptive analysis.

Gender
Men 48 (23.6%)
Women 155 (76.4%)
DMT
Alemtuzumab 25 (12.3%)
Interferon beta-1a 10 (4.9%)
Interferon beta-1b 2 (1.0%)
Cladribina 2 (1.0%)
Glatiramer acetate 19 (9.4%)
Dimetilfumarate 31 (15.3%)
Fingolimod 27 (13.3%)
Natalizumab 21 (10.3%)
Ocrelizumab 3 (1.5%)
Sc interferon beta-1a 31 (15.3%)
Rituximab 1 (0.5%)
Teriflunomide 31 (15.3%)
Age at first symptom
Mean (SD) 30.025 (8.901)
Median 29.715
Range (Min-Max) 10.564–59.767
Years from diagnosis
Mean (SD) 8.803 (6.335)
Median 7.381
Range (Min-Max) 0.367–24.400
Age
Mean (SD) 41.324 (8.726)
Median 41.142
Range (Min-Max) 17–67
EDSS
Mean (SD) 2.140 (1.755)
Median 2.000
Range (Min-Max) 0.000–7.000

Item selection

The consistency of the scale was analyzed through Cronbach’s alpha reliability coefficient. For the global questionnaire, the alpha was 0.88 and it did not improve when any of the items were deleted. However, when the item-total correlation was assessed, low item-total correlations show that that item doesn’t correlate well with the scale overall. Item 9 showed the worst correlation in comparison to the other items (Table 3) and was therefore deleted from the final questionnaire.

Table 3. Item-total correlation without the item itself.

Item-Total Correlation
Item 01 0.41
Item 02 0.48
Item 03 0.56
Item 04 0.57
Item 05 0.50
Item 06 0.54
Item 07 0.43
Item 08 0.50
Item 09 0.17
Item 10 0.32
Item 11 0.50
Item 12 0.48
Item 13 0.34
Item 14 0.42
Item 15 0.46
Item 16 0.49
Item 17 0.42
Item 18 0.44
Item 19 0.39
Item 20 0.35
Item 21 0.35
Item 22 0.50
Item 23 0.54
Item 24 0.56
Item 25 0.58

Principal component analysis

Principal component analysis proved to be a strong mechanism to factorize the data via Bartlett’s sphericity test (p < 0.001) and the Kaiser-Meyer-Olkin index (KMO = 0.85).

To guarantee the best structure, Horn’s Parallel analysis (PA) was performed. This methodology [21] compares the eigenvalues of the original dataset to the eigenvalues from other randomly generated same-size data. All the principal components associated with eigenvalues lower than those from the generated dataset will be excluded. A graphical representation of the PA outcome is shown in Fig 1. According to this exploration, there are 4 dimensions to retain.

Fig 1. Parallel analysis.

Fig 1

In addition, the Kaiser’s rule [22] was checked, and conforming to the previous outcome, 4 dimensions should be selected as well. These four eigenvalues are 6.2, 3.3, 2.0, and 1.1, and they explain 28.8%, 19.7%, 9.1%, and 6.4% of the variance. That is, 64.2% of the total variance.

The loading factors settle the weight of each item in each dimension. A varimax rotation has been performed to simplify the results. All the outputs have been collected in (Table 4). The calculated Cronbach’s alpha reliability coefficient for each dimension was within the range from 0.63 to 0.93, showing an acceptable internal consistency for all the cases.

Table 4. Factorial loadings.

SatisfactionSDM AdverseEffectsDMT Information Reliability DMTConvenience
Item 01 -0.17 0.00 -0.02 0.10
Item 02 -0.35 0.03 -0.06 0.11
Item 03 -0.37 -0.02 -0.06 0.02
Item 04 -0.42 -0.03 -0.09 -0.10
Item 05 -0.23 0.02 0.03 0.05
Item 06 -0.20 -0.02 0.10 -0.13
Item 07 -0.24 0.02 0.09 -0.31
Item 08 -0.31 0.00 0.16 -0.39
Item 10 -0.06 -0.05 0.05 0.08
Item 11 -0.37 -0.01 -0.05 -0.04
Item 12 -0.27 0.02 0.02 0.02
Item 13 0.06 0.04 0.45 -0.11
Item 14 0.03 0.04 0.43 -0.10
Item 15 -0.01 -0.29 -0.02 0.03
Item 16 -0.02 -0.48 0.00 -0.01
Item 17 0.04 -0.53 0.02 -0.02
Item 18 -0.00 -0.52 -0.02 -0.09
Item 19 0.02 -0.35 0.03 0.08
Item 20 -0.17 -0.02 0.04 0.60
Item 21 -0.13 0.02 0.09 0.48
Item 22 0.03 -0.00 0.43 -0.03
Item 23 0.01 -0.04 0.41 0.13
Item 24 -0.02 -0.05 0.39 0.16
Item 25 -0.17 -0.02 0.15 0.11

Discussion

We created the first questionnaire to assess the quality of the information administered during the SDM process in MS patients. Our study demonstrated that AGA-25 scale is a feasible, reliable and valid questionnaire for use in clinical practice with patients with MS.

In recent years, a focused-centered approach in which the patient is the core of the healthcare system has been increasingly recognized. Year by year MS drugs are increasing. The increasingly approved DMT landscape includes drugs with different profiles in terms of routes of administration (injectable, oral, and infusion), frequencies, mechanisms of action, and safety and tolerability profiles. In this increasingly complex scenario, it seems vital to include the patient’s preferences and values at the center of the decision. This kind of decisions have been called, preference-sensitive and they reflect the fact that the medical evidence is necessary, but not sufficient. A new, patient-centric shared decision model (SDM), is gaining relevance. SDM has been associated with higher adherence rates [19]. Thus, is increasingly recommended as the preferred approach for choosing a DMT in MS.

Several approaches have been checked as the best method to the SDM process including text-, video- and web-based [2326]. However, their reliability is not well demonstrated. Although the studies pointed out that the SDM process improved patient satisfaction and lower decisional conflict [26], all the evidence is centered in positive outcomes due to the use of decision aids, not in the perceived quality of the process itself. There are several elements of the decision-making process that can be measured, including the outcome of decision, readiness to plan, and decision quality [26].

Decision quality is known as the consistency of the individual’s decision with their beliefs, satisfaction with the decision, participation in decision-making and patient-clinician communication [13]. Therefore, simply measuring decision outcomes is not a meaningful indicator of quality, as the eventual outcome can be dependent upon many external factors [24].

We developed a questionnaire based on two already validated scales, TSQM and DCS, looking to assess 4 important aspects of the decision quality: the satisfaction with the information during the SDM (items 01, 02, 03, 04, 05, 06, 11, 12 and 25), the adverse events during the treatment (items 15, 16, 17, 18 and 19), the convenience with the chosen-DMT (items 7,8,10,20 and 21) and information reliability (items 13, 14, 22, 23 and 24). The exploratory factor analysis found the existence of these four dimensions in the AGA-25 scale. Moreover, this questionnaire has demonstrated a satisfactory internal reliability for all the factors, showing a Cronbach’s coefficient higher or equal to 0.7 in all the subscales and a global coefficient of 0.88. This is the first scale, developed for MS patients, however, this scale could be validated for other chronic conditions.

Other questionnaires, such as the Control Preferences Scale or the 9-item Shared Decision-Making Questionnaire (SDM-Q-9) have been previously developed and validated in Spanish for different pathologies including MS [28, 29]; however, our scale is unique in the sense of evaluating satisfaction with the information obtained during the process itself and offers an improvement opportunity for physicians and in their relationship with patients.

In future work, test stability of the questionnaire will be assessed to ensure that, there is no temporal change in the responses and all patients understood correctly all the items. Moreover, a bigger sample of PwMS is been collected to probe the internal four structure of the survey through confirmatory factor analysis (CFA). Nevertheless, we are aware of limitations of this study due to lower rates of higher-activity DMT, which could lead to bias.

Conclusion

This is the first questionnaire evaluating the quality of information given during the SDM process in multiple sclerosis patients. This questionnaire aims to determine if the chosen method for the SDM process is useful and well accepted in the outpatient clinic and offers an improvement opportunity.

Our study demonstrated good reliability. This questionnaire evaluated 4 aspects of the SDM process and DMT satisfaction; each subscale demonstrated also acceptable reliability.

Supporting information

S1 File

(XLSX)

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

References

  • 1. Keegan BM and Noseworthy JH. Multiple sclerosis. Annu Rev Med 2002; 53: 285–302. doi: 10.1146/annurev.med.53.082901.103909 [DOI] [PubMed] [Google Scholar]
  • 2. Giovannoni G. Disease-modifying treatments for early and advanced multiple sclerosis: a new treatment paradigm. Curr Opin Neurol. 2018. Jun;31(3):233–243. doi: 10.1097/WCO.0000000000000561 [DOI] [PubMed] [Google Scholar]
  • 3. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: What does it mean? (or it takes al least two tango). Soc Sci Med 1997; 44:681–92. doi: 10.1016/S0277-9536(96)00221-3 [DOI] [PubMed] [Google Scholar]
  • 4. Elwyn G, Coulter A, Laitner S, Walker E, Watson P, Thomson R. Implementing shared decision making in the NHS. BMJ. 2010;341:c5146. doi: 10.1136/bmj.c5146 [DOI] [PubMed] [Google Scholar]
  • 5. Elwyn G, Edwards A, Kinnersley P, Grol R. Shared decision making and the concept of equipoise: the competences of involving patients in healthcare choices. Br J Gen Pract 2000; 50(460):892–9. [PMC free article] [PubMed] [Google Scholar]
  • 6.Measuring Shared Decision Making. https://www.england.nhs.uk/wp-content/uploads/2013/08/7sdm-report.pdf
  • 7. Reen GK, Silber E, Langdon DW. Multiple sclerosis patients’ understanding and preferences for risks and benefits of disease-modifying drugs: A systematic review. J Neurol Sci. 2017. Apr 15;375:107–122. doi: 10.1016/j.jns.2016.12.038 [DOI] [PubMed] [Google Scholar]
  • 8. Ben-Zacharia A, Adamson M, Boyd A, Hardeman P, Smrtka J, Walker B, et al. Impact of Shared Decision Making on Disease-Modifying Drug Adherence in Multiple Sclerosis. Int J MS Care. 2018. Nov-Dec;20(6):287–297. doi: 10.7224/1537-2073.2017-070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Kriston L, Scholl I, Hölzel L, Simon D, Loh A, Härter M. The 9-item Shared Decision Making Questionnaire (SDM-Q-9): development and psychometric properties in a primary care sample. Patient Educ Couns. 2010;80:94–99. doi: 10.1016/j.pec.2009.09.034 [DOI] [PubMed] [Google Scholar]
  • 10. Doherr H, Christalle E, Kriston L, Härter M, Scholl I. Use of the 9-item Shared Decision Making Questionnaire (SDM-Q-9 and SDM-Q-Doc) in intervention studies: a systematic review. PLoS One. 2017;12:e0173904. doi: 10.1371/journal.pone.0173904 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Comellas M, Paz S, Poveda JL, Meletiche DM, Polanco C. Treatment adherence and other patient-reported outcomes as cost determinants in multiple sclerosis: a review of the literature. Patient Prefer Adherence. 2014;8:1653–1664. Published 2014 Dec 4. doi: 10.2147/PPA.S67253 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Cocco E, Caoci A, Lorefice L, Marrosu MG. Perception of risk and shared decision making process in multiple sclerosis. Expert Rev Neurother. 2017;17(2):173–180. doi: 10.1080/14737175.2016.1217155 [DOI] [PubMed] [Google Scholar]
  • 13. Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4(4):CD001431. Published 2017 Apr 12. doi: 10.1002/14651858.CD001431.pub5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Atkinson MJ, Sinha A, Hass SL, Colman SS, Kumar RN, Brod M et al. Validation of a general measure of treatment satisfaction, the Treatment Satisfaction Questionnaire for Medication (TSQM), using a national panel study of chronic disease. Health Qual Life Outcomes. 2004. Feb 26;2:12. doi: 10.1186/1477-7525-2-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Regnault A, Balp MM, Kulich K, Viala-Danten M. Validation of the Treatment Satisfaction Questionnaire for Medication in patients with cystic fibrosis. J Cyst Fibros. 2012. Dec;11(6):494–501. doi: 10.1016/j.jcf.2012.04.007 [DOI] [PubMed] [Google Scholar]
  • 16. Vermersch P, Hobart J, Dive-Pouletty C, Bozzi S, Hass S, Coyle PK. Measuring treatment satisfaction in MS: Is the Treatment Satisfaction Questionnaire for Medication fit for purpose? Mult Scler. 2017. Apr;23(4):604–613. doi: 10.1177/1352458516657441 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. O’Connor AM. Validation of a decisional conflict scale. Med Decis Making. 1995;15(1):25–30. doi: 10.1177/0272989X9501500105 [DOI] [PubMed] [Google Scholar]
  • 18. Munteis Olivas E, Navarro Mascarell G, Meca Lallana J, et al. Cultural adaptation and validation of a peninsular Spanish version of the MSTCQ (Multiple Sclerosis Treatment Concerns Questionnaire). Neurologia. 2017;32(1):29–39. doi: 10.1016/j.nrl.2014.12.011 [DOI] [PubMed] [Google Scholar]
  • 19. Koudriavtseva T, Onesti E, Pestalozza IF, Sperduti I, Jandolo B. The importance of physician-patient relationship for improvement of adherence to long-term therapy: data of survey in a cohort of multiple sclerosis patients with mild and moderate disability. Neurol Sci. 2012;33:575–584. doi: 10.1007/s10072-011-0776-0 [DOI] [PubMed] [Google Scholar]
  • 20. Weaver M, Patrick DL, Markson LE, Martin D, Frederic I, Berger M. Issues in the measurement of satisfaction with treatment. Am J Manage Care. 1997;3:579–94 [PubMed] [Google Scholar]
  • 21. Horn J. L. A rationale and test for the number of factors in factor analysis. Psychometrika 1965; 30:179–185. doi: 10.1007/BF02289447 [DOI] [PubMed] [Google Scholar]
  • 22. Kaiser H.F. The Application of Electronic Computers to Factor Analysis. Educ. Psychol. Meas. 1960; 20(1):141–151. doi: 10.1177/001316446002000116 [DOI] [Google Scholar]
  • 23. Solari A, Martinelli V, Trojano M,Lugaresi A, Granella F, Giordano A et al. An information aid for newly diagnosed multiple sclerosis patients improves disease knowledge and satisfaction with care. Mult Scler 2010; 16(11): 1393–1405. doi: 10.1177/1352458510380417 [DOI] [PubMed] [Google Scholar]
  • 24. Kopke’S Kern S, Ziemssen T, et al. Evidence-based patient information programme in early multiple sclerosis: A randomised controlled trial. J Neurol Neurosurg Psychiatry 2014; 85(4): 411–418. doi: 10.1136/jnnp-2013-306441 [DOI] [PubMed] [Google Scholar]
  • 25. Kasper J, Köpke S, Mühlhauser I, Nübling M, Heesen C. Informed shared decision making about immunotherapy for patients with multiple sclerosis (ISDIMS): a randomized controlled trial. Eur J Neurol. 2008;15(12):1345–1352. doi: 10.1111/j.1468-1331.2008.02313.x [DOI] [PubMed] [Google Scholar]
  • 26. Shay LA, Lafata JE. Where is the evidence? A systematic review of shared decision making and patient outcomes. Med Decis Making. 2015;35(1):114–131. doi: 10.1177/0272989X14551638 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Elwyn G, Miron-Shatz T. Deliberation before determination: the definition and evaluation of good decision making. Health Expect. 2010;13(2):139–147. doi: 10.1111/j.1369-7625.2009.00572.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Ballesteros J, Moral E, Brieva L, Ruiz-Beato E, Prefasi D, Maurino J. Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making Deliberation before determination modelling and confirmatory factor analysis. Health Qual Life Outcomes. 2017. Apr 22;15(1):79. doi: 10.1186/s12955-017-0656-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. De Las Cuevas C, Peñate W. Validity of the Control Preferences Scale in patients with emotional disorders. Patient Prefer Adherence. 2016. Nov 15;10:2351–2356. doi: 10.2147/PPA.S122377 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Fatih Özden

25 Jan 2022

PONE-D-21-39578Development of a scale for the evaluation of the quality of the shared decision process in multiple sclerosis patients.PLOS ONE

Dear Dr. Suárez,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 10 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Fatih Özden, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

2. Please amend your current ethics statement to address the following concerns:

a) Did participants provide their written or verbal informed consent to participate in this study?

b) If consent was verbal, please explain i) why written consent was not obtained, ii) how you documented participant consent, and iii) whether the ethics committees/IRB approved this consent procedure.

3. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 1 in your text; if accepted, production will need this reference to link the reader to the Table.

4. Please upload a copy of Supporting Information S1 File. XLS file.  which you refer to in your text on page 6.

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Authors,

The reviewers now completed their reviews. Both three reviewers has indicated their good comments on the manuscript, however two of them requested minor revisions. Please carefully fulfill these comment, then submit it again. Please also provide a response to their comments with a separate form.

King Regards

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

********** 

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

********** 

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

********** 

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

********** 

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have read your article and it is fantastic!. I have just minor or may be technical errors you need to address. Otherwise the article is good to go for me.

1) You mentioned that you used PCA to select the factor structure. Are there other clustering methods you could have used to achieve thesame/similar results?

2) You mentioned you used Leave-One Item out Cross-validation (i.e. you deleted one item out, and re-examine their alpha value), which may be biased as the model was trained/built using the entire data. To avoid this kind of bias, one will have to do a Leave-one-out cross-validation (Leave one person out of the model) and refit the model with that person included, and check whether or not the alpha value changes. If the change in the alpha value with and without this person is too drastic, then the model is unstable with respect to the selected items. In that case, one will need to redefine the factor structure or use limited factors. This is what I call model validation. Therefore, in addition to the leave-one item out approach, that evluates the consistency of the factor structure, I will like the authors to do leave-one out validation (leave-one person out of the model) and refit their model with the 4 identified scales. You can then plot the observed alpha versus the predicted alpha to see how well the model agrees. This will guarantee that an already validated scale is applicable to Spanish cohort. Unless otherwise, please provide concrete details why this sort of analysis cannot be done. I am statisfied with the current results, and the achieve value of alpha =0.88.

Reviewer #2: A sound manuscript with a very thorough methodological context on a very particular and extremely important subject. In addition it provides extremely detailed data on the aspects that were investigated and consequently a solid indication of the patient's perspective on matters that may influence his/her reality

Reviewer #3: In the manuscript, the authors validate a decision-making scale in the Spanish language.

New therapeutic options available for the treatment of MS –even if more effective and easier to administer– may pose increased risks of severe side effects. Taking this into account, involvement of PwMS in the treatment decision-making process becomes even more imperative.

The work seems very solid to me, although the number of patients recruited is small. Only in the discussion, I think it is necessary to mention other decision-making scales validated in Spanish, such as the Control Preference Scale, and explain the advantages of using the scale evaluated in this manuscript.

********** 

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Valery Fuh-Ngwa

Reviewer #2: Yes: Dimitrios Kitsos

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2022 May 13;17(5):e0268125. doi: 10.1371/journal.pone.0268125.r002

Author response to Decision Letter 0


3 Mar 2022

You mentioned that you used PCA to select the factor structure. Are there other

clustering methods you could have used to achieve the same/similar results?

Yes, indeed. We could have used non metric multidimensional scaling and cluster analysis for

ordinal data, for instance.

Non metric multidimensional scaling is a statistical methodology used to visualize high dimen-

sional and complicated data sets in few dimensions (preferably in two). The goodness of the fit is

made through the stress parameter. Stress could be described as a value showing the difference

between the distance in the reduced dimension in comparison to the complete dimensional space.

Stress values greater than 20 are considered as a poor dimension selection, values between 10

and 20, reasonable, between 5 and 10, good, between 2.5 and 5, excellent and between 0 and

2.5, perfect.

The principal goal of cluster analysis for ordinal data is to classify the observations in groups, in

the way that, the categorical observations in one group are most similar to each other and the

differences between the groups are the most different as possible. This technique can be used as

dimensional reduction as it is able to describe the hidden structure of the objects.

Nevertheless, to perform principal components analysis was for us a priority because this statis-

tical tool is able to rank the dimensions/components based on the amount of variance explained

by the data and we want to explain the maximum amount of information by the new selected

dimensions.

V ar(P C1) > V ar(P C2) > ... > V ar(P CN )

Where N are the number of original variables and PC are the principal components.

2. You mentioned you used Leave-One Item out Cross-validation (i.e. you deleted one

item out, and re-examine their alpha value), which may be biased as the model was

trained/built using the entire data. To avoid this kind of bias, one will have to do

a Leave-one-out cross-validation (Leave one person out of the model) and refit the

model with that person included, and check whether or not the alpha value changes.

If the change in the alpha value with and without this person is too drastic, then

the model is unstable with respect to the selected items. In that case, one will need

to redefine the factor structure or use limited factors. This is what I call model

validation. Therefore, in addition to the leave-one item out approach, that evluates

the consistency of the factor structure, I will like the authors to do leave-one out

validation (leave-one person out of the model) and refit their model with the 4

identified scales. You can then plot the observed alpha versus the predicted alpha

to see how well the model agrees. This will guarantee that an already validated scale

is applicable to Spanish cohort. Unless otherwise, please provide concrete details

why this sort of analysis cannot be done. I am statisfied with the current results,

and the achieve value of alpha =0.88.

Cronbach’s alpha coefficients using the whole sample N = 203 are presented in the table 1.

Where raw alpha is the alpha that we would obtain if we deleted the item itself. (Leave-One

Item out Cross-validation). The global value of raw alpha is 0.88.

And for each factor, using the whole sample:

• SatisfactionSDM

The overall Cronbach’s alpha is 0.874.

• AdverseEffectsDMT

The overall Cronbach’s alpha is 0.925.

InfoReliability

The overall Cronbach’s alpha is 0.873.

DMTConvenience

The overall Cronbach’s alpha is 0.625.

If we perform leave one (person) out cross validation, the resulted alpha’s for each subset (N-1)

are plotted in figure 1.

Where the solid lines are the expected alpha values and the dotted lines the observed for each

dimension, as well as for the overall alpha, when all the items are included.

As we can see the observed alpha values are quite stable and they are in agreement with the

expected values.

Reviewer #3 The work seems very solid to me, although the number of patients recruited is small.

Only in the discussion, I think it is necessary to mention other decision-making scales validated in

Spanish, such as the Control Preference Scale, and explain the advantages of using the scale evaluated

in this manuscript. Other scales are now mentioned and the main differences with prior scales have

been highlighted. Our scale is unique in evaluating satisfaction with the information obtained during

the process itself and offers an improvement opportunity for physicians and in their relationship with

patients.

Attachment

Submitted filename: Respuestas.pdf

Decision Letter 1

Fatih Özden

25 Apr 2022

Development of a scale for the evaluation of the quality of the shared decision process in multiple sclerosis patients.

PONE-D-21-39578R1

Dear Dr. Suárez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Fatih Özden, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Excellent work! I am very statisfied with the current results following the Leave-one-person Out cross-validation. Both the observed and expected alpha seems very reasonable to me. Great job there!

Reviewer #4: Authors well answered to all comments. Statistical analyses wer well performed and the paper was clear and well written.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Valery Fuh-Ngwa

Reviewer #4: No

Acceptance letter

Fatih Özden

6 May 2022

PONE-D-21-39578R1

Development of a scale for the evaluation of the quality of the shared decision process in multiple sclerosis patients.

Dear Dr. Suárez:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Fatih Özden

Academic Editor

PLOS ONE


Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES