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The effect of fatigue on postural control in individuals with multiple sclerosis: a systematic review



Fatigue is the most disabling symptom for individuals with multiple sclerosis (MS), which can significantly affect postural control (PC) by impairing the ability of the central nervous system to modulate sensory inputs and coordinate motor responses. This systematic review aimed to investigate the effect of fatigue on PC in individuals with MS..


This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and registered in PROSPERO with ID CRD42022376262. A systematic search was performed in the Web of Science, PubMed, Scopus, and Google Scholar until January 2023, and a manual search was performed using the reference lists of included studies. Two authors independently selected the studies, extracted data, and evaluated their methodological quality using the Downs and Black checklist. The process was later discussed with a third author..


Five studies were included in this review, of which consistent evidence investigating a direct relationship between fatigue and PC in individuals with MS. All the studies reported negative effects on PC. Four studies employed walking tests as their primary protocol for inducing fatigue, while one study implemented a strength testing protocol for both legs, serving as a fatigue-inducing activity.


The evidence suggests that individuals with MS may experience PC deficits due to fatigue. However, the present body of literature exhibits limitations regarding its quality and methodology. Gender differences, balance, fatigue task, and muscle function are essential factors that need to be considered when investigating the relationship between fatigue and PC deficits in MS. Further high-quality research is necessary to comprehend the complex interplay between MS-related fatigue and PC deficits after physical activity.

Peer Review reports


Multiple sclerosis (MS) is the most prevalent progressive and chronic disabling neurologic disease that affects the central nervous system (CNS) through demyelination, inflammation, and axonal loss [1, 2]. In 2020, MS was estimated to affect up to 2.8 million people worldwide with a significant impact on physical, emotional, social, and cognitive functioning [3, 4]. MS exhibits varying symptoms depending on the affected region, encompassing cerebellar, motor, sensory, emotional, and sexual manifestations [5]. Among the many symptoms associated with MS, fatigue has been identified as a significant concern, with up to 50–60% of patients experiencing this symptom [5]. In addition to the limitation in MS individuals’ activities of daily living and social lives from fatigue, it also hurts cognitive functions, decreasing attention and concentration [6]. In some disorders like MS, fatigue may be associated with motor and, or mood disorders, so it is challenging and sometimes impossible to determine whether fatigue is an aspect of these features or a symptom [7]. Fatigue physiologically is defined as “the inability of a muscle or group of muscles to sustain the required or expected force” by Bigland-Ritchie et al. [8]. Fatigue may occur from failure at force-generating capacity within the muscle itself (peripheral fatigue), or because of a disability to maintain the central drive to spinal motor neurons (central fatigue) [7].

Also, impaired balance is typically the primary symptom of MS, and it arises from a combination of slowed somatosensory conduction and impaired central integration [9] which cause abnormal gait control, and many fall frequently [10,11,12,13,14]. In this aspect, MS can potentially impact the entire CNS, resulting in various impairments in neurological functions [11]. Integrating various sensorimotor modalities, including visual, vestibular, and proprioceptive information, plays a significant role in postural control (PC) and sustaining an upright stance [15, 16]. These complex sensorimotor processes contribute to regulating body sway and facilitate coordinated movement patterns that maintain the center of mass within the limits of stability. Therefore, Understanding how different sensory inputs interact and contribute to PC is essential to developing interventions that can improve balance and prevent falls in vulnerable populations [15, 16]. Due to the presence of impairments in multiple processes, individuals with MS tend to exhibit weaker PC, as indicated by greater amounts of postural sway when compared to healthy controls [11, 12, 17, 18].

According to a recent systematic review study, individuals with MS experienced a positive impact on their fatigue levels as a result of sensory integration-based interventions. This led to improved balance and an overall increase in their quality of life. These findings may have important implications for managing symptoms and improving outcomes for individuals with MS [19]. Also, brain structural and functional alterations are seen in MS-related fatigue [20]. Particularly, sensorimotor network impairment and abnormal activation of the thalamus are associated with fatigue.

To date, no systematic review has synthesized the available data on the effect of fatigue on the PC of individuals with MS. By providing a comprehensive evaluation of existing research, this review aimed to address this knowledge gap and shed light on the relationship between fatigue and PC in this population.


This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline [21] and was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with registration number: CRD42022376262.

Search strategy

The relevant studies were identified through a systematic computerized search in Web of Science, PubMed, Scopus, and Google Scholar from 1st January 1974 until 1st January 2023. Two authors independently (M.A. and F.H.) searched four electronic databases. Any disagreement between the two authors was resolved by discussion and a third author’s opinion (P.S.) until a consensus was reached. The search strategy included MeSH terms and text words for a comprehensive search. Three sets of entry strings were mixed with ‘AND’ to produce the following syntax: (“postur*” OR “postural control” OR “postural sway” OR “postural stability” OR "postural steadiness” OR balance OR equilibrium OR sway) AND (fatig* OR lassitude OR exhaustion) AND (“multiple sclerosis”). Restrictions applied were human studies and full-text articles. Also, the reference lists of included studies screening and a grey literature search were performed to identify additional eligible studies. Retrieved studies were transferred into Endnote, and duplicates were deleted. The specific search strategy for each database is presented in Table 1.

Table 1 Search strategy

Eligibility criteria and screening methods

Following the search process, two authors (M.A. and F.H.) independently screened all titles and abstracts generated by the search procedure. Studies were selected according to the inclusion and exclusion criteria based on Population, Intervention, Comparison, and Outcome (PICO framework) [22] (Table 2).

Table 2 Eligibility criteria based on PICO

Data extraction

The two reviewers, M.A. and F.H., independently extracted data from eligible studies, if available, and verified it with another reviewer, P.S. The study details, including the author, year of publication, type of study, demographic variables of the participants such as sample size, age, gender, MS type, disability level, and disease duration, baseline therapies or pharmacological interventions, fatigue protocol, fatigue evaluation, outcome measures, measurement position, main outcomes, and conclusion were extracted. If some necessary information was not provided in the paper, corresponding authors were contacted via email up to three times to obtain the requested data.

Quality assessment

Since this review included different types of articles, two authors (M.A. and F.H.) independently assessed the quality of evidence of the studies using the criteria proposed by Downs and Black checklist [23], as this checklist is the best option to evaluate the quality and risk of bias for both randomized and non-randomized controlled trials [24]. The checklist included 27 questions grouped into five categories, including reporting (10 items), external validity (3 items), bias (7 items), confounding (6 items), and power (1 item). The power item (item 27) was simplified to a binary score based on whether or not a sample size calculation was performed. A score of 1 indicates the presence of a sample size calculation, while a score of 0 indicates its absence. With this alternation, the best possible score is 28. The quality of induced studies consists of the following score ranges: excellent [26,27,28], good [20,21,22,23,24,25], fair [15,16,17,18,19], and poor (≤ 14). This modification was done in previous reviews related to MS [19, 25]. The opinion of a third author (P.S.) was taken if a disagreement arose.

Data synthesis

A narrative data synthesis was performed. For complete reporting and transparency in the manuscript, this systematic review followed the PRISMA statement [21], and due to the different fatigue protocols and outcomes, meta-analysis was impossible. This was conducted by Synthesis Without Meta-analysis (SWiM) reporting guideline [26].


Study identification

The electronic databases search process is presented as a flow diagram (Fig. 1) based on the PRISMA guideline [21]. The search strategy retrieved 2136 articles through manual search. After removing duplicates, 1308 studies remained for further screening according to the inclusion and exclusion criteria. Only five studies successfully met eligibility criteria and entered the quality assessment phase.

Fig. 1
figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram for new systematic reviews, including searches of databases and registers

Study characteristics

All reviewed articles were published between 2010 and 2023 and written in English. One hundred twenty individuals participated in these studies. Three out of five studies [27,28,29] were performed without a comparison group [28, 30, 31]. In four studies [27,28,29,30], both sexes were included, whereas in one study [31], only female individuals were included. The mean age of the participants was 46.6 years (SD 9.9), and 35% were male [28, 30, 31, 33, 34]. The Six-Minute Walk Test (6MWT) was used in four of the studies [27,28,29,30], while one study [31] used strength testing for both legs. Additionally [34, 39], Table 3 summarized the specific characteristics and retrieved data of all five studies.

Table 3 Characteristics of included studies


All the studies included in the review [27,28,29,30,31, 27,28,29, 32, 34] reported negative effects on PC. Four studies [27,28,29,30] executed similar fatigue protocols (walking test), and one study [31] used a strength testing protocol for both legs, which served as a fatigue-inducing activity [32]. Th [36]e 6MWT is not specifically designed to measure fatigue, it indirectly provides information about fatigue-related factors during the test, such as gait speed [33]. Studies have shown that individuals with MS experience motor fatigue in both 6MWT distance and speed when compared to healthy controls [34]. The following procedures for fatigue for PC assessment in four studies [27, 28, 30, 31] were performed with eyes closed and eyes open. Drebinger et al. [30] explored fatigue effects on PC using visual perceptive computing. Jallouli et al. [27] examined the effect of a fatiguing task by using a stabilometric platform. Sanni et al. [28] evaluated the relationship between fatigue and following balance assessment with a force plate. Karpatkin et al. [29] investigated a direct relationship between fatigue and PC by performing the Berg balance scale test. Emmerik et al. [31] investigated the changes in balance at different levels of self-reported fatigue.

Quality assessment

The quality assessment results ranged from 13 to 16 (Mean: 14.8), presented in Table 4. Perfect agreement between authors across the 27 items with the Cohen’s kappa value 1 (0-0.20, poor agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, good agreement; and 0.81-1.00, perfect agreement) [35] was obtained. All studies reported the aims, main outcome measures, participant characteristics, and probability values. Regarding the external validity, bias, and confounding items of the checklist, studies didn’t provide any information on the participants blinding to intervention, outcome assessors blinding, analysis adjusts for different lengths of follow-up of participants, allocation concealment, adjustment for confounding variables in the main analysis and adjustment for loss to follow-up in the main analysis. Only, one study reported sample size calculation [27].

Table 4 Quality assessment of included studies


This systematic review aimed to investigate whether there is a relationship between fatigue and PC in individuals with MS. The current literature on the impact of fatigue on PC in individuals with MS is limited. Tasks that induce fatigue have been shown to negatively affect PC in individuals with MS [27,28,29,30,31]. This observation is consistent with previous research highlighting the negative impact of fatigue on various aspects of motor function in individuals with MS. There are several possible reasons for this finding which will be argued in the following paragraphs.

By reviewing the methodological quality of included studies, a large amount of lack of loss to follow-up was noted [27,28,29,30,31], and may not have distinguished all possible causes of missing follow-up data which entailed a risk of bias. Also, while this was an exploratory study, and we evaluated factors that predicted loss to follow-up, it is still possible that bias may have been introduced due to loss to follow-up. Additionally, it is essential to note that all of the included articles in our systematic review lacked blinding [27,28,29,30,31]. Lack of blinding in a study can introduce biases in various ways, depending on who remains unblinded among the study’s participants. Individuals assigned to the experimental group may have more positive expectations or report better outcomes to appease treatment providers. In contrast, those in the control group may have lower expectations and report poorer outcomes [36]. Thus, implementing blinding protocols where possible in research studies examining fatigue in MS can improve the quality and reliability of study results.

Fatigue severity in MS may depend on several clinical factors, including the number of years since onset, the specific MS subtype, the level of baseline disability, and the degree of disease activity. To address these factors, we compared the included studies in terms of the level of disability, MS subtype, and disease duration. The included studies revealed a wide range of disability levels, as measured by the Expanded Disability Status Scale (EDSS), with scores ranging from 1 to 6, 0–4, 3–5, 1.5-6, and 2–6 [27,28,29,30,31]. The comparison of PC in MS patients with EDSS scores less than 2.5, indicating minimal impairment in functional subsystems, with healthy groups, using criteria such as sway area, velocity, and displacement of the center of pressure, demonstrates that their ability to maintain PC is similar to that of healthy individuals [37]. However, as the degree of disability increases, significant differences are observed between the more severely affected MS patients and those with only mild or moderate disability [38]. Hence, a wide range of EDSS of included studies may be considered as a confusing factor regarding the expression and progression of fatigue related to the PC. Also, different types of MS, including relapsing-remitting MS, secondary progressive MS, and primary progressive MS [39], may play a role in the various results observed during PC evaluation with fatigue. In this regard, cognitive-postural interference was found to be more pronounced in SPMS patients, as they exhibited a higher dual-task cost compared to those with RRMS and healthy controls [40]. This indicates a greater impact on PC when comparing different types of MS.

Disability progression in MS appears to be linked to heightened fatigue and PC issues. Motl et al. [41] found that higher levels of disability were significantly correlated with increased fatigue in individuals with MS. Another study by Prosperini et al. [42] demonstrated that disability progression was associated with worsening PC, as measured by the Berg Balance Scale. These findings are consistent with those of Karpatkin et al. [29] whose study also employed the Berg balance scale score. Hence, it is crucial to manage disability progression to mitigate the impact on fatigue and PC problems in individuals with MS. While it is recommended that individuals with MS engage in regular physical activity, such as walking, to improve their quality of life, it is important to note that disability progression may still occur even in the absence of relapses. However, they often engage in less physical activity due to increased fatigue, mobility impairment, and fear of falling after a previous fall [28]. Furthermore, the included studies identified other factors that may contribute to PC impairment in individuals with MS, such as gender differences [27] and lower leg muscle function [28]. These factors should be considered when assessing PC in individuals with MS, as they may require different management strategies..

Accuracy and reliability of physician outcomes versus patient-reported outcomes is another important topic to consider. Physicians may not always be aware of the full extent of a patient’s symptoms, particularly if the patient does not report them or if the physician does not ask about them specifically [43]. Patient-reported outcomes provide a more direct measure of the patient’s experience of their symptoms and the impact of various interventions on their quality of life [43]. Patients may be more likely to report symptoms that are not easily observable by physicians, such as fatigue or cognitive difficulties [44]. However, patient-reported outcomes may also be influenced by factors such as mood, anxiety, or other comorbidities that could affect their perception of their symptoms [44]. In this regard, the Fatigue Scale for Motor and Cognitive Functions Questionnaire demonstrates high sensitivity and specificity in detecting fatigue in patients with MS (Cronbach’s alpha a > 0.91 and test–retest reliability r > 0.80 [45]. The Visual Analogue Scale of Fatigue exhibits a strong correlation with the physical aspects of fatigue. While its reliability has been established for various conditions, it has not been specifically examined in the context of MS [46, 47]. The Fatigue Severity Scale is a widely used tool in both clinical and research settings to measure the severity of fatigue and identify distinguishing features between two chronic medical disorders [48]. All of the included studies utilized Patient-reported outcomes, which can enhance the accuracy and reliability of data, facilitating the derivation of significant conclusions from various research findings..

The mechanism underlying postural instability in individuals with MS is a multifaceted process involving various factors. These factors include impaired lower leg muscle function [28], compromised PC [31], and an increased risk of falls [29]. Fatigue in MS patients is often associated with motor exertion, which can lead to decreased performance on balance scales and increased postural sway [27, 30]. Gender differences were inconclusive in the context of a fatiguing task’s impact on PC in individuals with MS [27]. In addition to task complexity, vision, and symptomatic fatigue [31], another mechanism that contributes to postural instability in MS patients is the sensorimotor mechanism. Fatigue affects the performance of individuals with MS on the Berg Balance Score [29], a measure of balance. Maintaining balance relies on the integration of sensorimotor information [49], which involves combining sensory input with motor output for coordinated movement [50]. Also, muscle fatigue can disrupt the central perception system, leading to a lack of motor control and an increased risk of falls. Individuals with MS often have deficient sensory systems and rely more on vision to maintain their postural balance [31]. In this regard, CNS lesions in MS can impact sensory and motor function, leading to sensory loss and fatigue, which may contribute to poor balance control [31]. Moreover, in the context of the sensorimotor mechanism, lower leg muscle function becomes a crucial target for intervention to improve gait, balance, and fall risk among individuals with MS [28]. This is because muscle fatigue can be divided into central and peripheral components, with central fatigue originating in the CNS and peripheral fatigue occurring at or distal to the neuromuscular junction [51, 52]. Individuals with MS experience a greater level of peripheral muscle fatigue while walking compared to those who are healthy [53]. This may be why walking tests, such as the 6MWT, are commonly used in MS research to assess walking fatigue due to their reliability and validity [54]. Therefore, it appears that the primary issue is related to peripheral muscle fatigue. Understanding these mechanisms is crucial for developing effective interventions to improve postural stability and reduce the risk of falls in individuals with MS.

The strength of this systematic review lies in its adherence to the PRISMA guidelines, which ensure the use of optimal methods for conducting and reporting systematic reviews. However, there are some limitations to consider. The heterogeneity in disability levels and progression among individuals with MS can introduce a significant risk of bias. This is because the included studies may not adequately represent the entire MS population, particularly in terms of disability severity and progression. Another limitation of the studies included in the investigation is that they utilized different drugs, such as antifatigue drugs [29, 31] or fampridine [30]. This variation in medication could have influenced the severity of fatigue or the walking speed, resulting in inconsistent outcomes in terms of PC in individuals with MS who are dealing with fatigue. Furthermore, the fair methodological quality of the included studies may also increase the risk of bias, as these studies may have limitations that affect the reliability and validity of their findings.


Studies have shown that people with MS may struggle with balance due to fatigue, and 6MWT is commonly used to assess fatigue-related factors. However, more research is needed to account for confounding variables such as disability levels, disease progression, and medication use. To address this, researchers can use statistical techniques, match participants based on specific characteristics, or conduct longitudinal studies. By implementing these strategies, future studies can provide more accurate and reliable results, leading to a better understanding of the impact of fatigue on PC in individuals with MS.

Data Availability

Data are available on request from the corresponding author..



Central Nervous System


Multiple Sclerosis


Postural Control


  1. Andreu-Caravaca L, Ramos-Campo DJ, Chung LH, Rubio-Arias JÁ. Dosage and effectiveness of aerobic training on cardiorespiratory fitness, functional capacity, balance, and fatigue in people with multiple sclerosis: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2021;102(9):1826–39.

    Article  PubMed  Google Scholar 

  2. Sosnoff JJ, Shin S, Motl RW. Multiple sclerosis and postural control: the role of spasticity. Arch Phys Med Rehabil. 2010;91(1):93–9.

    Article  PubMed  Google Scholar 

  3. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, et al. Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS. Multiple Scler J. 2020;26(14):1816–21.

    Article  Google Scholar 

  4. Bakshi R. Fatigue associated with multiple sclerosis: diagnosis, impact and management. Multiple Scler J. 2003;9(3):219–27.

    Article  Google Scholar 

  5. Mollaoğlu M, Üstün E. Fatigue in multiple sclerosis patients. J Clin Nurs. 2009;18(9):1231–8.

    Article  PubMed  Google Scholar 

  6. Fisk JD, Pontefract A, Ritvo PG, Archibald CJ, Murray T. The impact of fatigue on patients with multiple sclerosis. Can J Neurol Sci. 1994;21(1):9–14.

    Article  CAS  PubMed  Google Scholar 

  7. Comi G, Leocani L, Rossi P, Colombo B. Physiopathology and treatment of fatigue in multiple sclerosis. J Neurol. 2001;248(3):174–9.

    Article  CAS  PubMed  Google Scholar 

  8. Bigland-Ritchie B, Jones D, Hosking G, Edwards R. Central and peripheral fatigue in sustained maximum voluntary contractions of human quadriceps muscle. Clin Sci Mol Med. 1978;54(6):609–14.

    Article  CAS  PubMed  Google Scholar 

  9. Cameron MH, Lord S. Postural control in multiple sclerosis: implications for fall prevention. Curr Neurol Neurosci Rep. 2010;10(5):407–12.

    Article  PubMed  Google Scholar 

  10. Soyuer F, Mirza M, Erkorkmaz Ü. Balance performance in three forms of multiple sclerosis. Neurol Res. 2006;28(5):555–62.

    Article  PubMed  Google Scholar 

  11. Corradini ML, Fioretti S, Leo T, Piperno R. Early recognition of postural disorders in multiple sclerosis through movement analysis: a modeling study. IEEE Trans Biomed Eng. 1997;44(11):1029–38.

    Article  CAS  PubMed  Google Scholar 

  12. Karst GM, Venema DM, Roehrs TG, Tyler AE. Center of pressure measures during standing tasks in minimally impaired persons with multiple sclerosis. J Neurol Phys Ther. 2005;29(4):170–80.

    Article  PubMed  Google Scholar 

  13. Daley ML, Swank RL. Quantitative posturography: use in multiple sclerosis. IEEE Trans Biomed Eng. 1981;9668–71.

  14. Nelson SR, Di Fabio RP, Anderson JH. Vestibular and sensory interaction deficits assessed by dynamic platform posturography in patients with multiple sclerosis. Annals of Otology Rhinology & Laryngology. 1995;104(1):62–8.

    Article  CAS  Google Scholar 

  15. Winter DA. Biomechanics and motor control of human movement. John Wiley & Sons; 2009.

  16. Peterka RJ. Sensorimotor integration in human postural control. J Neurophysiol. 2002;88(3):1097–118.

    Article  CAS  PubMed  Google Scholar 

  17. Rougier P, Faucher M, Cantalloube S, Lamotte D, Vinti M, Thoumie P. How proprioceptive impairments affect quiet standing in patients with multiple sclerosis. Somatosens Motor Res. 2007;24(1–2):41–51.

    Article  CAS  Google Scholar 

  18. Remelius JG, Hamill J, Kent-Braun J, Van Emmerik RE. Gait initiation in multiple sclerosis. Motor Control. 2008;12(2):93–108.

    Article  PubMed  Google Scholar 

  19. Mohebbirad M, Motaharinezhad F, Shahsavary M, Joveini G. Effects of sensory interventions on fatigue in people with multiple sclerosis: a systematic review. Int J MS care. 2022;24(1):29–34.

    Article  PubMed  Google Scholar 

  20. Barbi C, Pizzini FB, Tamburin S, Martini A, Pedrinolla A, Laginestra FG, et al. Brain structural and functional alterations in multiple sclerosis-related fatigue: a systematic review. Neurol Int. 2022;14(2):506–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Reviews. 2021;10(1):1–11.

    Article  Google Scholar 

  22. Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S, PICO. PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014;14(1):1–10.

    Article  Google Scholar 

  23. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Deeks JJ, Dinnes J, D’Amico R, Sowden AJ, Sakarovitch C, Song F, et al. Evaluating non-randomised intervention studies. Health Technol Assess (Winchester Eng). 2003;7(27):iii–173.

    Article  CAS  Google Scholar 

  25. Rooney S, Moffat F, Wood L, Paul L. Effectiveness of fatigue management interventions in reducing severity and impact of fatigue in people with Progressive multiple sclerosis: a systematic review. Int J MS care. 2019;21(1):35–46.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;368.

  27. Jallouli S, Ben Dhia I, Sakka S, Mhiri C, Yahia A, Elleuch MH, et al. Combined effect of gender differences and fatiguing task on postural balance, functional mobility and fall risk in adults with multiple sclerosis: a preliminary study. Neurol Res. 2022;12.

  28. Sanni AA, Lynall R, Backus D, McCully KK. Lower Leg muscle function: a contributory risk factor of Gait and Balance Impairment after Six minutes walk among people with multiple sclerosis. Med Res Archives. 2021;9(3).

  29. Karpatkin H, Cohen ET, Rzetelny A, Erlandsson K, Gibbons S, Griffith H, et al. Performance on the Berg Balance Scale in fatigued versus nonfatigued states in people with multiple sclerosis. Crit Reviews Phys Rehabilitation Med. 2013;25(3–4):223–30.

    Article  Google Scholar 

  30. Drebinger D, Rasche L, Kroneberg D, Althoff P, Bellmann-Strobl J, Weygandt M, et al. Association between Fatigue and Motor Exertion in patients with multiple Sclerosis-a prospective study. Front Neurol. 2020;11:11.

    Article  Google Scholar 

  31. Van Emmerik R, Remelius J, Johnson M, Chung L, Kent-Braun J. Postural control in women with multiple sclerosis: effects of task, vision and symptomatic fatigue. Gait Posture. 2010;32(4):608–14.

    Article  PubMed  Google Scholar 

  32. Chung LH, Remelius JG, Van Emmerik R, Kent-Braun JA. Leg power asymmetry and postural control in women with multiple sclerosis. Med Sci Sports Exerc. 2008;40(10):1717–24.

    Article  PubMed  Google Scholar 

  33. Chen S, Sierra S, Shin Y, Goldman MD. Gait speed trajectory during the six-minute walk test in multiple sclerosis: a measure of walking endurance. Front Neurol. 2021;12:698599.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Goldman MD, Marrie RA, Cohen JA. Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls. Multiple Scler J. 2008;14(3):383–90.

    Article  Google Scholar 

  35. McHugh ML. Interrater reliability: the kappa statistic. Biochemia Med. 2012;22(3):276–82.

    Article  Google Scholar 

  36. Kamper SJ. Blinding: linking evidence to practice. J Orthop Sports Phys Therapy. 2018;48(10):825–6.

    Article  Google Scholar 

  37. Santinelli FB, Barbieri FA, Pinheiro CF, Amado AC, Sebastião E, van Emmerik RE. Postural control complexity and fatigue in minimally affected individuals with multiple sclerosis. J Mot Behav. 2019.

    Article  PubMed  Google Scholar 

  38. Zanotto T, Sosnoff JJ, Ofori E, Golan D, Zarif M, Bumstead B, et al. Variability of objective gait measures across the expanded disability status scale in people living with multiple sclerosis: a cross-sectional retrospective analysis. Multiple Scler Relat Disorders. 2022;59:103645.

    Article  Google Scholar 

  39. Kupjetz M, Joisten N, Rademacher A, Gonzenbach R, Bansi J, Zimmer P. Cycling in primary Progressive multiple sclerosis (CYPRO): study protocol for a randomized controlled superiority trial evaluating the effects of high-intensity interval training in persons with primary Progressive multiple sclerosis. BMC Neurol. 2023;23(1):162.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Chamard Witkowski L, Mallet M, Bélanger M, Marrero A, Handrigan G. Cognitive-postural interference in multiple sclerosis. Front Neurol. 2019;10:913.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Motl RW, McAuley E, Snook EM. Physical activity and multiple sclerosis: a meta-analysis. Multiple Scler J. 2005;11(4):459–63.

    Article  Google Scholar 

  42. Prosperini L, Fortuna D, Giannì C, Leonardi L, Pozzilli C. The diagnostic accuracy of static posturography in predicting accidental falls in people with multiple sclerosis. Neurorehabilit Neural Repair. 2013;27(1):45–52.

    Article  Google Scholar 

  43. Williams AE, Vietri JT, Isherwood G, Flor A. Symptoms and association with health outcomes in relapsing-remitting multiple sclerosis: results of a US patient survey. Multiple sclerosis international. 2014;2014.

  44. McKay KA, Marrie RA, Fisk JD, Patten SB, Tremlett H. Comorbidities are associated with altered health services use in multiple sclerosis: a prospective cohort study. Neuroepidemiology. 2018;51(1–2):1–10.

    Article  PubMed  Google Scholar 

  45. Penner I-K, Raselli C, Stöcklin M, Opwis K, Kappos L, Calabrese P. The Fatigue Scale for Motor and cognitive functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Multiple Scler J. 2009;15(12):1509–17.

    Article  CAS  Google Scholar 

  46. Tseng BY, Gajewski BJ, Kluding PM. Reliability, responsiveness, and validity of the visual analog fatigue scale to measure exertion fatigue in people with chronic stroke: a preliminary study. Stroke research and treatment. 2010;2010.

  47. Kos D, Nagels G, D’Hooghe MB, Duportail M, Kerckhofs E. A rapid screening tool for fatigue impact in multiple sclerosis. BMC Neurol. 2006;6(1):1–8.

    Article  Google Scholar 

  48. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale: application to patients with multiple sclerosis and systemic Lupus Erythematosus. Arch Neurol. 1989;46(10):1121–3.

    Article  CAS  PubMed  Google Scholar 

  49. Gandolfi M, Geroin C, Picelli A, Munari D, Waldner A, Tamburin S, et al. Robot-assisted vs. sensory integration training in treating gait and balance dysfunctions in patients with multiple sclerosis: a randomized controlled trial. Front Hum Neurosci. 2014;8:318.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zabihhosseinian M, Yielder P, Berkers V, Ambalavanar U, Holmes M, Murphy B. Neck muscle fatigue impacts plasticity and sensorimotor integration in cerebellum and motor cortex in response to novel motor skill acquisition. J Neurophysiol. 2020;124(3):844–55.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ishigaki EY, Ramos LG, Carvalho ES, Lunardi AC. Effectiveness of muscle strengthening and description of protocols for preventing falls in the elderly: a systematic review. Braz J Phys Ther. 2014;18:111–8.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Gschwind YJ, Kressig RW, Lacroix A, Muehlbauer T, Pfenninger B, Granacher U. A best practice fall prevention exercise program to improve balance, strength/power, and psychosocial health in older adults: study protocol for a randomized controlled trial. BMC Geriatr. 2013;13:1–13.

    Article  Google Scholar 

  53. Motl RW, Sandroff BM, Suh Y, Sosnoff JJ. Energy cost of walking and its association with gait parameters, daily activity, and fatigue in persons with mild multiple sclerosis. Neurorehabilit Neural Repair. 2012;26(8):1015–21.

    Article  Google Scholar 

  54. Scalzitti DA, Harwood KJ, Maring JR, Leach SJ, Ruckert EA, Costello E. Validation of the 2-minute walk test with the 6-minute walk test and other functional measures in persons with multiple sclerosis. Int J MS care. 2018;20(4):158–63.

    Article  PubMed  PubMed Central  Google Scholar 

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The authors express their deepest gratitude to the reviewers for the valuable and insightful feedback provided.


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The study’s conception and design were a collaborative effort among all authors. The manuscript’s first draft was written by MA and FH, and they received feedback from PS on previous versions. MA and FH participated in implementing the search strategy, applying the inclusion/exclusion criteria, and quality scoring system. The final version of the manuscript was read and approved by all authors..

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Correspondence to Mohammad Alghosi.

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Sedaghati, P., Alghosi, M. & Hosseini, F. The effect of fatigue on postural control in individuals with multiple sclerosis: a systematic review. BMC Neurol 23, 409 (2023).

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