Sage Journals: Discover world-class research

Abstract

Background:

Multiple Sclerosis (MS) is a neurological disease, related to different factors and systems. It causes fatigue, anxiety and depression. Virtual reality is used during rehabilitation. To evaluate the reaction time in MS patients and the interference of fatigue, anxiety and depression in performing a virtual task (VT).

Methods:

A cross-sectional study comparing MS patients (GMS) with people without MS (GC), by evaluating the Beck Inventory (BDI depression, BAI anxiety) and VT Simple Reaction Time (RTs) in GMS and GC and Modified Fatigue Impact Scale (MFIS) in GMS. The study period was January-June 2018. GMS sample 57, minimum age 23, maximum 77 years old (M=45.86, SD=12.24), GC sample 27, minimum age 23, maximum 57 years old (M=45.00, SD =12.09), both genders.

Results:

A positive correlation between MFIS, anxiety, and depression; between BDI and RTs. GMS had a lower performance (M=406.76) in RTs and all fatigue measurements.

Conclusion:

GMS showed an increase in RT indices, which may be related to neuronal impulse delay or depression. A fatigue scored on the MFIS did not interfere with VT.

Keywords

multiple sclerosis virtual reality reaction time depression

1 Introduction

Multiple Sclerosis (MS) is a degenerative and progressive neurological disease, related to several factors, different systems and causes various symptoms. [1, 2, 3, 4, 5, 6]

Oliveira et al. [7] (2013) mention that fatigue affects physique and cognition. Pereira [8] (2013)

showed a significant correlation between physical disability, cognition and depression. Cerqueira and Nardi [9] (2011) demonstrate a positive association between depression and cognition. Depression is related to the changes caused by the disease. Utilizing the Beck Inventory, Nogueira et al. [10] (2017) established a correlation between depression and anxiety.

Rehabilitation programs have used virtual reality (VR) to monitor, control, maintain or improve functionality and increase transfer from virtual to reality. [11, 12, 13, 14, 15, 16]

Although VR is used in rehabilitation, little is known about the influence of fatigue, anxiety and depression on virtual task (VT) performance in people with MS. [17, 18, 19, 21, 22, 23, 24]

The objective of this study was to evaluate the reaction time in MS patients and the interference of fatigue, anxiety and depression in performing VT. The hypothesis was fatigue, anxiety and depression will reduce VT performance in the MS group (GMS) and the control group (GC). However, it is anticipated the reduction will be more significant in GMS.

2 Methods

A cross-sectional study evaluated the GMS group and GC for anxiety, depression and fatigue (GMS only). The study analyzed Simple Reaction Time (RTs) and performance fatigue of the initial and final RT, in performing VT, in both groups, between January and June 2018.

According to McDonald 2017 [4], in the Brazilian Association of Multiple Sclerosis - ABEM, 100 eligible patients were diagnosed with MS. The patients presented motor alterations in the upper limbs and visual, outbreak in the previous two months, fatigue, errors in the execution of the evaluations and VT were excluded.

2.1 Participants

The GMS 57 patients, between ages 23 and 77 years old (M = 45.86, SD = 12.24), with all MS types; Expanded Disability Status Scale (EDSS) [25] between 0.0-8.5, both sexes, sampling error of 0.5%, a confidence level of 90%, internal validity 0.25 (d Cohen), using the GPower 3.1.9.4 software.

The MS types are: Relapsing Remitting (RRMS), flares and recovery; Secondary Progressive (SPMS), symptom progression between 10-20 years; Primary Progressive (PPMS), without flare and gradual progression; Progressive with Relapse (PSMS) and onset of relapses between 10-20 years. [3, 26]

The EDSS evaluates the functional systems, quantifying disabilities on a 0.5-point scale, 0.0 (absence of disability) to 10.0 (death), the neurologist assigns the data. [3, 4, 25]

After definition GMS, CG sample was 27 people without MS, between ages 23 and 57 years (M = 45.00, SD = 12.09), participants were patient companions and ABEM employees, matched by mean and standard deviation of age.

2.2 Material

These instruments were validated in Brazil.

a) Beck inventory, on average Cronbach's alpha (a) 0.81. The 21questions is associated with depressive symptoms (BDI) and anxiety (BAI) separately. The two scales are: minimal (no depression or anxiety), mild, moderate, and severe. [27]

b) Modified Fatigue Impact Scale (MFIS), a 0.82 on average. Questionnaire has 21 physical, cognitive, psychosocial questions. Each response ranges from 0 to 4, totaling up to 84 points, with results below 38 (absence of fatigue), the higher the score, the more fatigue. [28]

c) The Simple Reaction Time (RTs) evaluates, in milliseconds, the reaction between the beginning of the stimulus and the beginning of the motor response. The RTTS2012 software was built

and validated by Crocetta et al [29] (2017). It

analyzes the total RTs which consists of the appearance of a yellow rectangle (b) (configurable) in the center of the monitor at predefined time intervals (1.5 to 6.5 ms). When presented, the participant must react as quickly as possible by pressing the spacebar on the keyboard. In the

fatigue reaction time (c) the participant observes the black line (large cursor), which moves from left to right, where the participant must respond as quickly as possible by pressing the spacebar on the keyboard when the yellow stimulus appears (d) and keep the space bar pressed, observing the movement of the yellow stimulus bar until it disappears, when the spacebar must be released. The reaction time when pressing (RTi) and releasing (RTf) close to what happened on the screen (Fig. 1).

Fig. 1

Representation of the reaction time and fatigue task. Simple reaction time: (a) the participant observes de center of the computer screen; (b) the yellow rectangle stimulus; Reaction time fatigue:

2.3 Procedure

Participants were informed about the study and answered the sociodemographic questionnaire (SQ) with sex, age, duration of disease, last outbreak, type MS, EDSS, fatigue. Each participant was evaluated and individually underwent VT. They were positioned 1.5 meters from the computer, the chair and footrest adjusted and, when necessary, the patient used their wheelchair.

Next, the task was verbally explained and a demonstration was performed then, each participant made a single attempt to verify the understanding of the instructions.

2.4 Data analysis

The data were analyzed using the SPSS 24 statistical system (Statistical Package for the Social Sciences), with descriptive data analysis of mean, frequency and standard deviation, correlation of sociodemographic variables, psychological assessments and VT (chisquare) and one-way ANOVA.

TRs, calculated the means of two blocks of seven trials with fatigue calculated based on reaction time and the averages of two blocks of five attempts were calculated for the reaction to the initial (RTi) and final stimulus (RTf). Results were statistically significant for a 95% confidence interval and significance level of p <0.05.

3 Result

In the Multiple Sclerosis Group (GMS) 66.7% women, 75.40% Relapsing Remitting (RRMS), Expanded Disability Status Scale (EDSS) grouped between, 0.0-4.0 (59.65%) and 4.5-6.5 (35.10%) (p = 0.029). There was a positive correlation between MS type and pooled EDSS, a higher concentration of RRMS and Secondary Progressive (SPMS) with EDSS between 4.5-6.5 (p = 0.002). 45.60% of patients were diagnosed for more than 10 years and a significant correlation between the time of diagnosis and increased EDSS (p = 0.006). 36.8% of the patient's last outbreak occurred between 1-3 years.

In the sociodemographic questionnaire (SQ), 86.00% of the patients reported having fatigue. There is a correlation between fatigue perception and EDSS (chisquare p = 0.016). 50.87% of patients with a perception of fatigue and EDSS (0.0 to 4.0); 33.33% showed an EDSS between 6.5-7.5, demonstrating that people who do not have limitations according to the EDSS, they perceive fatigue.

MFIS correlates fatigue with anxiety and depression (chisquare p = 0.000). 56.14% of those who perceived fatigue, in the SQ, did not score in the MFIS, and 1.75% of the patients did not perceive fatigue, in the SQ, score in the MFIS.

Patients with an outbreak between 1-3 years scored 21.05% for anxiety and 15.78% for depression.

In the BAI significant correlations (p = 0.017) with grouped age (31—40 years), 19.29% of patients; RRMS 36.84% of patients have anxiety. There was a positive correlation (p = 0.034) between BDI and grouped age (31—40 years), 14.03% of patients have depression. The correlation between anxiety and depression (p = 0.001), demonstrates a greater possibility of joint depression and anxiety. CG scored anxiety and depression (Table 1).

Table 1

Percentage of anxiety and depression

Anxiety% Depression%
	GMS	GC	GMS	GC
Male	14.03	7.40	8.77	3.70
Female	26.31	25.92	19.29	22.22
31—40 years old	19.29	—	14.03	—
Diagnosed for more than 10 years	14.03	—	17.54	—
Outbreak 1—3 years	19.29	—	19.29	—
Minimal	5.60	31.60	71.90	35.10
Mild	29.80	10.50	17.50	10.50
Moderate	7.00	3.50	8.80	1.80
Severe	3.50	1.80	1.80	0

In the GMS, RTs (M = 406.90 ms, SD = 121.87), RTFatigue, RTi (M= 509.52 ms, SD = 168.15) and RTf (M = 503.85 ms, SD = 166. 96). The difference between RTi and RTf shows that 56.10% had increased reaction time.

For the GMS, there was a significant correlation between BDI and RTs (p<0.05), demonstrating a slowing response by depression. GC showed no correlation between BAI, BDI, RTs, RTi or RTf. There was a correlation between GMS

(p < 0.001) and GC (p = 0.016) in the BDI and BAI, demonstrating the interference of anxiety in depression and vice versa.

Repeated measures ANOVA reveals no main effect for the RTs blocks (first 7 attempts x last 7), and there was no significant interaction between the RTs blocks and GMS and GC. There was a significant effect for the group, F (1.82) = 10.03, p = 0.002, rj² = 0.11, indicating that, disregarding all other variables, the performance in the RTs of the GMS was lower (M = 406, 76) compared to the GC (M = 330.60).

For fatigue analyses based on reaction time, the ANOVA of repeated measures was significant for the block, with Greenhouse-Geisser correction, F (2.03, 167.00) = 4.60, p = 0.011, rj² = 0.05; significant effect for group, F (1.82) = 9.44, p = 0.003, r² = 0.10, indicating that when disregarding all other variables, the performance of the GMS was lower (M = 508.57) compared to the GC (M = 408.59). Post hoc tests with Bonferroni correction indicated the GMS performance was inferior in all measures of the blocks for fatigue compared to the CG, as shown in Fig. 2.

Fig. 2

Performance on the simple reaction time task (a), assessment of fatigue from the reaction time for the initial (b) and final (c) stimulus, for multiple sclerosis (GEM) and control (GC) groups.

RTiB1: First block of 5 attempts to assess fatigue with the perception of the beginning of the stimulus;

RTiB2: Second block of 5 attempts to assess fatigue with the perception of the beginning of the stimulus;

RTfB1: First block of 5 attempts to assess fatigue with the perception of the end of the stimulus;

RTfB2: Second block of 5 attempts to assess fatigue with the perception of the end of the stimulus.

4 Discussion

The hypothesis was partially confirmed, there was a reduction in reaction time in VT in patients with depression, but no reduction was observed in the CG.

The significant correlation between the time of diagnosis and the increase in EDSS, perceiving fatigue and EDSS, type MS, and EDSS are justified by the progressive and degenerative disease. [3, 4, 30]

Scoring fatigue in MFIS had a significant relationship with anxiety and depression, which suggests the treatment of comorbidities, as fatigue, anxiety, and depression can debilitate the patient even if the EDSS does not incapacitate the patient. [1, 2, 7, 8, 9, 10, 24, 26, 31]

An outbreak between 1—3 years demonstrates anxiety and depression. Mendes [32], Tremlett [33] and Simone [34] indicate depression can be associated with the response to the disease, type of medication, or organicbasis. The interruption of secondary cortico-subcortical pathways due to demyelination may explain the correlation between the intensity of depression and functional disability, according to Mendes [32].

Significant correlations were obtained for BAI and BDI in the 31—40 year age GMS age representing the onset or peak of the illness occurring, around 10 years before the pathology may worsen. Therefore, the treatment of MS and comorbidities is necessary. [1, 2, 7, 8, 9, 10, 17, 20, 21, 24, 26]

There is a correlation between anxiety and depression, demonstrating the joint presence of the two comorbidities. The data is justifiable by the evolution of the disease. [1, 2, 7, 8, 9, 10, 17,

20, 21, 24, 26]

The CG showed anxiety and depression, as shown in Table 1. However, these symptoms did not interfere with the VT performance, as with the GMS, which with depression showed slow responses. [8, 9, 10, 20, 21]

Significant correlations were obtained between BDI and RTs. The performance of GMS in RTs and all measurements for fatigue was inferior compared to the CG. The results demonstrates impaired or slowed responses that worsen with depression and may be related to neuronal impulse delay. [8, 9, 10, 20, 21] Fatigue, scored in MFIS, has not been shown to interfere with VT. [6, 20, 31]

Few studies address the relationship between fatigue, anxiety and depression in VT and MS. Perhaps due to the lack of standardization of anxiety and depression assessment instruments in MS or because the symptoms are less noticeable or are confused with the symptoms of fatigue.

[1, 2, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,

21, 22, 23, 24, 26, 32, 33, 34, 335]

This study shows that there is a higher percentage of patients reporting fatigue in the SQ, however, in the MFIS, which specifically assesses fatigue, the scores were lower, demonstrating that fatigue did not interfere with the execution of VT, even in patients with depression or anxiety.

5 Limitation and futures studies

The study presents findings on the impact of fatigue, anxiety, and depression on reaction times in MS patients using virtual tasks. However, several limitations should be presented: Firstly, the control group comprises only 27 individuals, a sample size that may not capture the variance in the general population without MS adequately, thus limiting the extent to which the findings can be generalized. Secondly, the cross-sectional design of the study captures data at a single point in time, precluding the ability to determine the directionality of the observed relationships or to make causal inferences. We advocate for a longitudinal study using virtual reality tasks to confirm these results.

Thirdly: this study evaluated the characteristics of depression, anxiety and fatigue in people with different types of multiple sclerosis. No correlation was made between symptoms and the type of RRMS, which is the predominant one. Based on the data obtained in our study, we do not believe that the results specifically in a RRMS group would be very different from those found in this research.

Finally, a virtual reality game was used which was not originally designed for individuals with multiple sclerosis. It is crucial to encourage the development of virtual activities that take into account the specific limitations imposed by multiple sclerosis and to promote research that validates their applicability for this population.

Footnotes

Acknowledgements

All authors thank patients and Brazilian Association of Multiple Sclerosis - ABEM,

Ethical approval

Approved by the Research Ethics Committee of the University of Sao Paulo (CAAE:25702619. 5.0000.5390).

Consent to participate

Participants signed an informed consent form

Declaration of conflicting interests

This study and theirs authors have no conflicts of interest

Funding information

None.

Data availability statement

None.

Author contribution

Ana Maria Canzonieri analyzed the data and prepared the first draft of the manuscript. Ana Maria Canzonieri and Tania Crocetta participated in the conception and design of the study, Carlos Monteiro constructively revised the manuscript; Ana Maria Canzonieri participated in data collection and organization; Ana Maria Canzonieri, Tania Crocetta and Carlos Monteiro participated in and supervised the study throughout, and they share corresponding authorship. All authors commented on previous versions of the manuscript and approved the final version.

Nurse, Psychologist, Masters (2007) and Doctorate (2014) in Health Sciences from the Federal University of Sao Paulo; researcher at the Brazilian Association of Multiple Sclerosis (ABEM) from 2008 to 2018, development of the ABEM Open University; clinical psychologist in the care of chronic illnesses; research grants from CAPES (Brazil) 2011-2012, at the University of Porto (Portugal); Participant of the research line group "Relational Contexts of Development", coordinated by Prof. Dr. Paula Mena Matos, at the Faculty of Psychology of the University of Porto (2011-2012); more than 10 books and 20 articles and 40 posts in annuals of published conferences; participation in the production of more than 5 videos on multiple sclerosis; research interests in multiple sclerosis and rare diseases.

Tania Brusque Crocetta received her doctoral degree in Health Sciences from the Faculdade de Medicina do ABC, Sao Paulo, Brazil (2018). She was a scholarship holder for the iBrasil Erasmus Mundus mobility doctoral program (2016) at the Faculty of Management Science and Informatics, at the University of Zilina, in Slovakia. Currently works at the Universidade do Estado de Santa Cataroina, Brazil, as a database specialist, working mainly on the following topics: reaction time, motor control, augmented reality and virtual reality. She has published many high-quality papers on journals including Current Psychology, Games for Health Journal, Cyberpsychology, Behavior, and Social Networking, Measurement in Physical Education and Exercise Science, and Virtual Reality.

Bachelor's in Physiotherapy and Physical Education, Master's in Developmental Disorders, PhD in Sciences in the field of Neurology from the University of Sao Paulo, Postdoctoral fellowship by the Department of Maternal and Child Health at the School of Public Health, University of Sao Paulo. Professor of the Physical Education and Health course at the School of Arts, Sciences, and Humanities of the University of Sao Paulo (EACH/USP). Mainly works on the following topics: physical disability, functional skills, mobility, gross motor function, physical independence and especially the development and application of computational tasks and virtual reality in rehabilitation.

References

Akbar

Honarmand

Feinstein

. Self-assessment of cognition in Multiple Sclerosis: the role of personality and anxiety. Cogn Behav Neurol 2011, 24(3): 115-121. DOI: 10.1097/ WNN.0b013e31822a20ae.

Bruce

Lynch

SG.

Personality traits in multiple sclerosis: association with mood and anxiety disorders. J Psychosom Res 2011, 70(5): 479-485. DOI: 10.1016/j.jpsychores.2010.12.010.

Russo

Neurological aspects of multiple sclerosis. In: Giacomo MCB. coordinator. Multiple sclerosis: The path of knowledge between stones and flowers, 2011, 16-33.

Thompson

Banwell

Barkhof

, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018, 17(2): 162-173. DOI: 10.1016/S1474-4422(17)30470-2.

Alves

BCA

Angeloni

Azzalis

L.A

, et al. Multiple sclerosis: review of the main treatments for the disease. Saude Meio Ambient 2014; 3(2): 19-34.

Padgett

Kasser

SL.

Exercise for managing the symptoms of multiple sclerosis. Phys Ther 2013, 93(6): 723-728. DOI: 10.2522/ptj.20120178.

Oliveira

ACFR

Andrade

Gontijo

, et al. Characterization and complaints related to occupational performance: considerations of individuals with multiple sclerosis. Rev. Ter. Ocup 2013; 24(2): 112-20.

Pereira

AG.

Evolution of cognitive, psychic and motor functions in patients with multiple sclerosis. Master Thesis. Pontificia Universidade Catolica do Rio Grande do Sul, Brazil, 2013.

Cerqueira

ACR

Nardi

. Depression and multiple sclerosis: An overview. Rev Bras Neurol 2011; 47(4): 11-16.

10.

Nogueira

Porto

ACP

Germano Silva

, et al. Assessment of anxiety, depression and quality of life in patients with multiple sclerosis. Rev Inspirar Mov Saude 2017; 43(14): 6-10.

11.

Castro

Sanchez

MJR

Sedeno

TDP.

New technological developments applied to psychological treatment. Acta Colomb psicol 2014; 17(2): 91-101. DOI:10.14718/ACP.2014.17.2.10.

12.

Bohil

Alicea

Biocca

FA.

Virtual reality in neuroscience research and therapy. Nat Rev Neurosci 2011, 12(12): 752-762. DOI: 10.1038/nrn3122.

13.

Crocetta

Guarnieri

Massetti

, et al. Concurrent validity and reliability of alternative computer game for the coincidence-anticipation timing task. Meas Phys Educ Exerc Sci 2019, 23(2): 169-185. DOI: 10.1080/1091367x.2018.1560297.

14.

Bezerra

IMP

Crocetta

Massetti

,et al. Functional performance comparison between real and virtual tasks in older adults: a cross-sectional study. Medicine 2018, 97(4): e9612. DOI: 10.1097/MD.0000000000009612.

15.

Gutierrez

Galan Del Rio

Cano de la Cuerda

, et al. A telerehabilitation program by virtual reality-video games improves balance and postural control in multiple sclerosis patients. NeuroRehabilitation 2013, 33(4): 545-554. DOI: 10.3233/NRE-130995.

16.

Lozano-Quilis

Gil-Gomez

, et al. Virtual rehabilitation for multiple sclerosis using a kinect-based system: randomized controlled trial. JMIR Serious Games 2014, 2(2): e12. DOI:10.2196/games.2933.

17.

Massetti

Trevizan

Arab

, et al. Virtual reality in multiple sclerosis - A systematic review. Mult Scler Relat Disord 2016, 8: 107-112. DOI: 10.1016/j.msard.2016.05.014.

18.

Harand

Mondou

Chevanne

, et al. Evidence of attentional impairments using virtual driving simulation in multiple sclerosis. Mult Scler Relat Disord 2018, 25: 251-257. DOI: 10.1016/j.msard.2018.08.005.

19.

Raphail AM

Vickers

Leist

, et al. The relationship between multiple sclerosis symptom severity measures and performance on driving variability metrics in a virtual reality simulator. Am J Phys Med Rehabil 2020, 99(4): 278-284. DOI:10.1097/PHM.0000000000001351.

20.

Ferrero

GM.

Comparison between real and virtual interaction devices during a coincident timing task in people with multiple sclerosis. Master Thesis. University of Sao Paulo, Brazil, 2021.

21.

Leonardi

Maggio

Russo

, et al. Cognitive recovery in people with relapsing/remitting multiple sclerosis: a randomized clinical trial on virtual reality-based neurorehabilitation. Clin Neurol Neurosurg 2021, 208: 106828. DOI: 10.1016/ j.clineuro.2021.106828.

22.

Visser

De Mul

Redekop

WK.

Innovative medical technology and the treatment decision-making process in multiple sclerosis: a focus group study to examine patient perspectives. Patient Prefer Adherence 2021, 15: 927-937. DOI: 10.2147/PPA.S306132.

23.

Ziemssen

Haase

Digital innovation in multiple sclerosis management. Brain Sci 2021, 12(1): 40. DOI: 10.3390/brainsci12010040.

24.

Felippe

LA.

Effects of cognitive-motor rehabilitation in patients with multiple sclerosis: a controlled clinical trial. Ph.D. thesis. Campo Grande: Mato Grosso do Sul University Foundation, Brazil, 2017.

25.

Kurtzke

JF.

Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983, 33(11): 1444-1452. DOI: 10.1212/wnl.33.11.1444.

26.

Negreiros

Landeira-Fernandez

Kirchmeyer

, et al. Cognitive impairments in Brazilian individuals with relapsing-remitting multiple sclerosis. J Bras Psiquiatr 2011; 60(4): 266-70.

27.

Cunha

. Manual of the Portuguese version of the Beck scales, Sao Paulo: Casa do Psicologo, 2001: 171.

28.

Pavan

Schmidt

Marangoni

, et al. Multiple Sclerosis: Cross-cultural adaptation and validation of the modified fatigue impact scale. Arq Neuropsiquiatr 2007; 65(3-A): 669-673. DOI: 10.1590/s0004- 282x2007000400024.

29.

Crocetta

Viana

Silva

, et al. Investigating the correlation between mental fatigue and total reaction time: a pilot study. Revista Brasileira de Prescrigao e Fisiologia do Exercicio 2017; 11(65): 143-155.

30.

Iwanowski

Losy

Immunological differences between classical phenothypes of multiple sclerosis. J Neurol Sci 2015, 349(1/2): 10-14. DOI: 10.1016/ j.jns.2014.12.035.

31.

Taveira

Braz

NFT

Comini-Frota

, et al. Disability as a determinant of fatigue in MS patients. Arq Neuropsiquiatr 2019, 77(4): 248-253. DOI: 10.1590/0004-282X20190035.

32.

Mendes

Charles Peter

Balsimelli

, et al. Depression in relapsing-remitting multiple sclerosis. Arq Neuropsiquiatr 2003; 61(3-A): 591-595.

33.

Tremlett

Luscombe

Wiles

CM.

Prescribing for multiple sclerosis patients in general practice: a case-control study. J Clin Pharm Ther 2001, 26(6): 437-444. DOI: 10.1046/j.1365-2710.2001.00377.x.

34.

Simone

Ceccarelli

Tortorella

, et al. Influence of Interferon beta treatment on quality of life in multiple sclerosis patients. Health Qual Life Outcomes 2006, 4: 96. DOI: 10.1186/1477-7525-4-96.

35.

Holden

Dyar

Virtual environment training: a new tool for neurorehabilitation. JNPT 2002; 26(2): 62-71.

A reaction time deficit in virtual tasks may be related to delayed neuronal impulse or depression in patients with multiple sclerosis

Abstract

Background:

Methods:

Results:

Conclusion:

Keywords

1 Introduction

2 Methods

2.1 Participants

2.2 Material

2.3 Procedure

2.4 Data analysis

3 Result

4 Discussion

5 Limitation and futures studies

Footnotes

Acknowledgements

Ethical approval

Consent to participate

Declaration of conflicting interests

Funding information

Data availability statement

Author contribution

References