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Spasticity in multiple sclerosis: Associations with impairments and overall quality of life

Multiple Sclerosis and Related Disorders, January 2016, Pages 34 - 39



  • (1) To determine the association between spasticity and quality of life (QOL) in multiple sclerosis (MS).
  • (2) To investigate the associations between spasticity and impairments of function and activity limitations.


Cross-sectional survey.


A convenience sample of people with MS routinely attending an appointment with their local MS service.


701 patients with clinically definite MS.

Main outcome measures

Demographic details were obtained and patients completed a battery of measures including spasticity (Multiple Sclerosis Spasticity Scale – 88), fatigue (Neurological Fatigue Index – MS), urinary dysfunction (Qualiveen-SF), pain (Neuropathic Pain Scale), mood disorder (Hospital Anxiety and Depression Scale), disability (World Health Organisation Disability Assessment Schedule) and QOL (Leeds Multiple Sclerosis QOL Scale).


85.7% of patients reported spasticity. Patients with higher levels of spasticity were more likely to be disabled, suffer from depression and anxiety, have higher levels of fatigue and report more pain and bladder problems (p<0.01). Spasticity remained as a significant direct effect upon QOL in a multivariate model adjusted for other impairments, activity limitation and depression.


There is a strong association between spasticity and fatigue, depression, anxiety, pain and bladder problems. The retention of a significant direct relationship with QOL in a multivariate model emphasises its influence upon the everyday lives of people with MS.


  • Spasticity affects up to 85% of patients with multiple sclerosis.
  • After adjusting for confounders spasticity was found to be an independent determinant of worse quality of life in multiple sclerosis.
  • There is a strong association between spasticity and fatigue, depression, anxiety, pain and bladder problems.
  • Effective pharmacological and non-pharmacological treatments of spasticity are needed to improve the quality of life of patients with multiple sclerosis.

Keywords: Multiple sclerosis, Spasticity, Quality of life, Disability, Fatigue, Depression.

1. Introduction

Spasticity affects the majority of patients with multiple sclerosis (MS) and is rated as one of the most disabling symptoms (Rizzo et al, 2004 and Paisley et al, 2002). It can cause pain, reduces ability to move and interferes with personal hygiene (Stevenson, 2010 and Thompson et al, 2005). Spasticity has also been shown to be the main contributing factor to disability in the lower limbs (Barnes et al., 2003). As a result of such significant disabilities associated with spasticity, it could be expected that spasticity may adversely impact upon Quality of Life (QOL). The World Health Organisation (WHO) defines QOL as ‘the individual's perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns’ (WHO, 1995). The term health-related QOL (HRQOL) or health status, refers to health aspects of QOL such as activities and participation (Schipper and Clinch, 1996).

Despite spasticity being one of the most common impairments associated with MS, little is known regarding its relationship with QOL. In contrast, several studies have previously reported that spasticity is associated with worse health status. Two large American studies totalling over 22,000 people with MS found that patients with spasticity had significantly lower scores on the physical components of SF-36 and SF-12 (Rizzo et al, 2004 and Wu et al, 2007). Similar findings were reported in two European studies. Arroyo et al. found significant negative correlations between spasticity (measured by patient rated Numerical Rating Scale (NRS) and the Ashworth scale) and the SF-12 (Arroyo et al., 2013). Another study by Flachenecker et al. (2014) reported that patients with moderate (NRS 4–6) and severe (NRS 7–10) spasticity scored significantly worse on the Multiple Sclerosis QOL −54 (MSQOL-54) scale and EuroQoL-5D.

Although the above studies suggest that spasticity is associated with worse health status, its relationship with QOL is unclear. Despite the studies concluding that spasticity affects QOL, all studies employed health status measures, measuring functioning, rather than overall QOL instruments which are designed to obtain the patients' perception of their QOL. Literature regarding this distinction suggests that HRQOL and QOL are unique constructs and should be assessed individually (Smith et al, 1999 and Ferrans et al, 2005). Secondly, QOL in MS is known to be affected by factors other than spasticity, such as depression, pain, fatigue and bladder dysfunction etc. (Lobentanz et al., 2004; Hemmett et al., 2004; Amato et al., 2001; Goksel Karatepe et al., 2011). Since there is evidence to suggest that spasticity may be related to other impairments of MS for which the previous studies did not account, it could not be concluded that spasticity is an independent determinant of QOL (Oreja-Guevara et al., 2011).

The aim of this study is to investigate the relationship between spasticity and overall QOL using a disease specific measure, the Leeds Multiple Sclerosis QOL (MSQOL) (Ford et al., 2001). The study will also explore, the relationships between spasticity and other impairments. Finally, the study aims to examine socio-demographic (age, sex, marital status, duration, and type of MS) and impairment and activity limitation variables (anxiety, fatigue, pain, bladder, and disability), together with depression, using multivariable logistic regression analysis, to explore the association between spasticity and Quality of Life within a multivariate context.

2. Methods

2.1. Study participants

One thousand one hundred thirty-seven patients with clinically definite MS were recruited to the TONiC (Trajectories of Outcomes in Neurological Conditions) study by five MS services in the UK (Liverpool, Preston, Manchester, Leeds, Sussex). All patients with MS capable of informed consent who did not have a second diagnosis believed to influence their quality of life, such as malignancy, were eligible irrespective of age, disability level, duration or type of MS. Each participant gave written, informed consent. Sociodemographic (age, sex, employment and marital status) and clinical details (type of MS, duration, Extended Disability Status Scale (EDSS) score, disease modifying therapy) were obtained upon enrolment into the study by a clinician or researcher. Questionnaire packs containing the measures described below were given to the participants for completion. Reminder telephone calls were made in cases when participants did not return the questionnaire pack within a 4-week period or the questionnaire was incomplete. Demographic characteristics of the non-responders were compared to those of responders. The study received full ethical approval from the local research committee (11/NW/0743).

2.2. Measurement instruments

2.2.1. Spasticity

Spasticity was assessed using the ‘stiffness’ subscale of the Multiple Sclerosis Spasticity Scale-88 (MSSS-88), a self report measure (Hobart et al., 2006). Two other subscales were not included in the regression model due to high inter-correlation (r=0.86-0.9), which would adversely affect multicollinearity in the multiple regression. In addition, there were no differences in correlation between MSSS-88 subscales and LMSQOL.

2.2.2. Quality of life

The LMSQOL is an 8-item instrument developed specifically to measure overall QOL in MS (Ford et al., 2001). A total score is calculated by adding up all the items. Higher score indicates worse QOL.

2.2.3. Disability

WHO Disability Assessment Schedule (WHODAS) 2.0 consists of 36 items covering 7 domains (Üstün, 2010). The total score was calculated by adding the item responses and transforming to 0-100 scale using the SPSS syntax obtained from the WHO website.

2.2.4. Impairments and mood disorder

Fatigue was assessed using the Neurological Fatigue Index – MS (NFI-MS), which has been shown to have robust psychometric properties for measuring fatigue in MS populations (Mills et al., 2010). The 10-item subscale of overall fatigue was used in the analysis.

The Neuropathic Pain Scale (NPS) consists of 11 items addressing different types and qualities of pain (Galer and Jensen, 1997). Although originally developed for evaluation of pain in peripheral nerve disorders, NPS has been shown to be valid in conditions characterised by central causes of pain, such as MS (Rog et al., 2007). A total pain score is calculated by adding up 10 items of the NPS (1 item on temporality of pain is excluded).

SF-Qualiveen provides a brief assessment of bladder dysfunction and its impact on the patient's life (Bonniaud et al., 2008). The 8-item scale has been previously validated in MS (Bonniaud et al., 2008).

Mood disorder assessment was carried out using Hospital Anxiety and Depression Scale (HADS) (Zigmond and Snaith, 1983). HADS consists of anxiety and depression subscales, each containing 7 items and has been previously validated in MS (Honarmand and Feinstein, 2009).

2.3. Statistical analysis

Socio-demographic characteristics were analysed using descriptive statistics. All ordinal Patient Reported Outcome measures (PROM's) were converted into categorical values based upon their inter-quartile ranges. Where clinical cut points were available (e.g. anxiety), these cut points were used to categorise patients. Thus these categorical variables were then entered into a logistic regression analysis with the LMSQoL split at the median as the dependent variable. Initially unadjusted univariate odds ratios for each quartile (lowest as reference) were determined for each predictor variable. Significant variables were then entered into a multivariate model.

3. Results

3.1. Characteristics of the study sample

Data were available from 701 participants (61.7% response). Socio-demographic and disease characteristics are summarised in Table 1. Mean age was 48.8 years (SD 11.7, range 18–82) and 505 (72%) were female. The sample was representative of a wide range of disabilities, types of MS and disease durations. No significant differences in demographic characteristics were detected when the sample was compared with non-responders.

Table 1 Socio-demographic and clinical characteristics of the sample.

Total sample n=701
Mean (SD) 48.8 (11.7)
Range 18–82
Sex (%)
Female 505 (72.0)
Male 190 (27.1)
Unknown 6 (0.9)
MS type (%)
Relapsing–remitting 407 (58.1)
Rapidly evolving* 40 (5.7)
Secondary progressive 155 (22.1)
Primary progressive 68 (9.7)
Unknown 31 (4.4)
Duration of disease (years)
Median (IQR) 9 (13)
Mean (SD) 11.5 (9.0)
Range 0–49
EDSS score (%)
0–4 261 (37.2)
4.5–6.5 334(47.6)
7.0–7.5 41 (5.8)
8.0–9.5 52 (7.4)
Unknown 1 (0.1)
Disease modifying therapy (%) 259 (37)
Glatiramer acetate 68 (9.7)
Interferon beta 69 (9.8)
Natalizumab 94 (13.4)
Employment status (%)
Full time employment 163 (23.3)
Medically retired 199 (28.4)
Unemployed 48 (6.8)
Part-time 114 (16.3)
Retired 92 (13.1)
Not working for other reasons 64 (9.1)
Unknown 20 (2.9)
Marital status (%)
Single/separated 115 (16.4)
Married/cohabiting 505 (72.0)
Divorced 48 (6.8)
Widowed 18 (2.6)
Unknown 15 (2.1)

TWo or more disabling relapses in 1 year or significant increase in T2 lesions as compared with previous recent MRI

SD: standard deviation, IQR: interquartile range

3.2. Spasticity characteristics

Most of the patients reported some degree of spasticity (599, 85.4%). Other than those at the floor of the scale, the distribution appeared mostly uniform (Fig. 1.) Older age and time since diagnosis showed a strong significant gradient across increasing levels of spasticity (quartiles) (ANOVA p<0.001). Likewise EDSS level showed a strong association with spasticity (Chi-Square 190.5: p<0.001). Those with a Progressive type of MS reported significantly higher levels of spasticity compared to those with a relapsing–remitting type of the condition (Chi-Square 76.1: p<0.001). The majority of other impairments and activities also showed strong associations with spasticity (Chi-Square <0.001); however, there was no association between gender, marital status and level of spasticity. When different DMTs were compared, no significant statistical difference was detected, however glatiramer acetate was associated with lower levels of spasticity compared to natalizumab and beta interferons.

Fig. 1

Fig. 1 Distribution of the stiffness variable of the Multiple Sclerosis Spasticity Scale-88 (MSSS-88).

3.3. Spasticity, health status. depression and QOL

Table 2 shows the unadjusted odds-ratios for all demographic and health status variables with respect to their impact upon QOL. Apart for age and gender, all variables are shown to have a significant association with QOL. For example, those in the upper quartile score for spasticity increase the odds of an above median (poor) QOL by 9.2 times. Likewise, those with an upper quartile score of fatigue increase the odds of a poor QOL by 15.4 times. Activities, anxiety and depression show strong gradients for increasing odds for a poor QOL.

Table 2 Unadjusted odds ratios indicating associations between demographic, clinical and health status measures and quality of life (above median – poor). Reference for Independent variables presented as quartiles is the lower quartile (best) score.

Sig Odds Lower CI Upper CI Percent correct Nagelkerke R2
Age 0.931 1.001 0.988 1.013 53.9 0.000
male 0.591 1.096 0.785 1.531 53.2 0.001
Marital status
Married 0.029 0.691 0.496 0.962 55.1 0.009
Working 0.012 53.8 0.017
Part time 0.049 1.609 1.003 2.583
Not working 0.003 1.709 1.196 2.441
Type of MS
Progressive 0.030 1.422 1.034 1.956 54.9 0.009
Spasticity 0.000 63.4 0.125
Lower–middle quartile 0.026 1.711 1.066 2.745
Upper–middle quartile 0.000 3.525 2.206 5.634
Upper quartile 0.000 9.207 5.611 15.106
Bladder problems 0.000 64.3 0.119
Lower–middle quartile 0.038 1.497 1.023 2.190
Upper–middle quartile 0.000 3.277 2.143 5.012
Upper quartile 0.000 5.969 3.509 10.153
Pain 0.000 65.2 0.142
Lower–middle quartile 0.692 0.914 0.584 1.429
Upper–middle quartile 0.000 2.245 1.441 3.495
Upper quartile 0.000 4.993 3.177 7.847
Fatigue 0.000 67.0 0.230
Lower–middle quartile 0.000 4.365 2.532 7.523
Uppe–middle quartile 0.000 6.994 4.139 11.818
Upper Quartile 0.000 15.444 8.983 26.552
Activities and participation 0.000 69.0 0.253
Lower–middle quartile 0.000 3.199 1.888 5.421
Upper–middle quartile 0.000 6.188 3.732 10.259
Upper quartile 0.000 16.958 9.791 29.370
Anxiety 0.000 70.0 0.235
Possible 0.000 3.309 2.264 4.837
Probable 0.000 10.251 6.549 16.046
Depression 0.000 70.3 0.231
Possible 0.000 5.231 3.466 7.894
Probable 0.000 15.123 7.352 31.108

3.4. Multivariate logistic regression analysis

Significant determinants of QOL identified in the univariate analyses were entered into the multivariable regression models. The results are summarised in Table 3 where, for brevity, only the significant odds are shown. No demographic variables remain significant, but the impairments of Spasticity and Fatigue do so, along with Activities, and the mood disorder variables of Anxiety and Depression. Thus those in the upper quartile of the spasticity scale have an increased odds of a poor QOL of 2.239, adjusted for all the other variables in the model. Those scoring in the upper quartile of the Activities and Participation scale (WHODAS-2) have increased odds of a poor QOL of 4.18. The model correctly predicts 76.2% of QoL allocation and satisfies model fit requirements (Hosemer & Lemeshow 0.388; NagelKerke R square 0.448).

Table 3 Adjusted odds ratios indicating significant associations between demographic, clinical and health status measures and Quality of Life (above median – poor). Reference for Independent variables presented as quartiles is the lower quartile (best) score – significant quartiles shown.

Sig Odds Lower CI Upper CI
Spasticity 0.037
Upper quartile 0.033 2.239 1.069 4.686
Fatigue 0.044
Lower–middle quartile 0.010 2.361 1.225 4.550
Upper–middle quartile 0.025 2.184 1.104 4.320
Upper quartile 0.009 2.664 1.276 5.562
Activities and participation 0.010
Lower–middle quartile 0.036 2.006 1.046 3.849
Upper–middle quartile 0.014 2.485 1.201 5.143
Upper quartile 0.001 4.180 1.813 9.635
Anxiety 0.000
Possible 0.000 2.343 1.471 3.732
Probable 0.000 3.959 2.253 6.966
Depression 0.001
Possible 0.000 2.507 1.505 4.177
Probable 0.028 2.646 1.108 6.318

4. Discussion

This is believed to be the first study, which provides a detailed description of the relationships between spasticity and overall QOL, taking into account other health status variables in a large cohort with MS. It has found that spasticity retains a significant direct relationship to QOL in the presence of other variables such as fatigue, anxiety and activity limitations. The multivariate model also reinforces the importance of the broader bio-psychosocial model in understanding the everyday lived experience of those with MS. It suggests a framework for more complex analyses to investigate the model further, and to determine the stability of the model over time, as will be the case with the longitudinal TONiC study (

The present study found that increasing severity of spasticity was associated with a significant worsening of overall QOL, as measured by the LMSQOL, a disease specific measure of QOL for MS. In line with previous research, strong associations were found between spasticity and physical domains. The psychosocial effects of spasticity in MS have only been previously explored in qualitative studies (Nicolson and Anderson, 2001, Morley et al, 2013, and Bhimani et al, 2012). Those studies found that spasticity can have deleterious effects on patients' psychological health and social roles. Patients reported that spasticity affected their relationships, employment and future planning. Anxiety, depression, low self-esteem and locus of control were all reported to be affected by spasticity. However, as the magnitude of these effects could not be estimated by previous qualitative studies, the present study provides quantitative evidence of these associations.

Although it is possible that spasticity worsens depression, fatigue and anxiety, hence adversely affecting QOL, no causal relationships can be modelled due to the cross-sectional nature of the first phase (baseline) of this study. Other than spasticity, the strongest determinants of QOL were fatigue, activity limitations, depression and anxiety, which supports the findings of previous studies investigating factors influential to QOL in MS (Lobentanz et al, 2004, Amato et al, 2001, and Janardhan and Bakshi, 2002).

Spasticity was found to affect about 85% of our sample, confirming the high prevalence of spasticity in MS. The prevalence and severity found for spasticity in this study are similar to previous population surveys assessing MS-related spasticity (Rizzo et al, 2004, Arroyo et al, 2013, and Svensson et al, 2014). As expected, higher EDSS was associated with worse spasticity. However, it is important to acknowledge that almost a fifth (18.8%) of patients with severe spasticity (upper quartile) had not yet reached EDSS >6.5, with the majority of patients experiencing moderate spasticity (inter-quartile range) at lower levels of disability.

The efficacy of current treatments for spasticity in MS has been examined in several reviews (Paisley et al, 2002 and Shakespeare et al, 2003). A significant proportion (29.2%) of MS patients have severe spasticity, for which management can be particularly problematic. Arroyo et al. reported that patients with intractable severe spasticity progress to being wheelchair-bound in a third of cases and only 20% are still in employment (Arroyo et al., 2011). The findings of our study also show a sharp decline in employment as the severity of spasticity increases (60.4% in employment for those in the lower quartile of spasticity vs. 6% employment of those in the upper quartile (Chi-Square 54.1; p <0.001)). Reduced capacity to work, together with caregiver burden, have been found to be the biggest contributors to the societal costs related to MS-associated spasticity (Svensson et al., 2014). Hence, maximising mobility and providing adequate treatment are imperative to prevent the loss of productivity of MS patients, and our data suggest that these interventions may be needed early as well as late in the disease course. However, in a review on spasticity management practices in Europe, Berger emphasised that only 3% of overall healthcare costs in MS are related to rehabilitation services and antispasticity agents (Berger, 2013).

4.1. Study limitations and future directions

This study has several limitations. Firstly, the participants were recruited by NHS teams with an interest in MS, both hospital and community-based, so the sample may not be representative of MS patients receiving care solely by self-referral to their GP. However, demographic characteristics, EDSS scores and spasticity severity and prevalence are comparable to other population-based studies in MS (Rizzo et al, 2004 and Flachenecker et al, 2014). Secondly, the cross-sectional design of the study does not allow formulation of any assumptions regarding causality. Future studies of longitudinal and interventional designs will enable more detailed examination of these relationships.

Although patient-rated measures for spasticity such as the MSSS-88, have been shown to be robust, they are not without limitations. It is possible that patients may attribute other forms of stiffness (dystonia, and rigidity) to spasticity, which could confound the assessment (Skold, 2000 and Skold et al, 1999). Inclusion of data on antispasticity therapies would also enhance the findings. Finally, while a number of factors were accounted for in the regression models, it is likely that many other variables (e.g. coping, hope, personality traits, social support, self-efficacy etc.) could have important contributions to QOL. Sleep problems are known to impact QOL in MS and have not been accounted for in the present analysis (Veauthier et al, 2015 and Veauthier and Paul, 2014). Inclusion of additional factors would provide a more complete understanding of the determinants of QOL in MS.

5. Conclusions

The present study found that spasticity affects the majority of patients with MS and may be severe even in the early stages of disease. A strong association between spasticity and worse overall QOL was found. Since high correlations were found between spasticity and depression, fatigue, pain and bladder dysfunction, it could be hypothesised that spasticity may also affect QOL indirectly through these impairments. Further study is needed to examine these relationships in more detail.

Conflicts of interest

None declared.

Funding acknowledgements

The TONiC study received support from Biogen, United Kingdom; MNDA, United Kingdom; (grant number 922-794) NIHR, United Kingdom; Novartis, United Kingdom; Roche United Kingdom; Teva United Kingdom; and Walton Neuroscience Fund.


The authors would like to thank all the patients who took part in the study, research and clinical staff for recruitment and the TONiC team.


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a The Walton Centre Foundation Trust, Liverpool, United Kingdom

b University of Liverpool, United Kingdom

c Swiss Paraplegic Research, Nottwil, Switzerland

Correspondence to: Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool L9 7LJ, United Kingdom.

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About the Editors

  • Prof Timothy Vartanian

    Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College, Cornell...
  • Dr Claire S. Riley

    Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia University,...
  • Dr Rebecca Farber

    Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in New...

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