Fatigue, Physical Activity, Quality of Life, and Fall Risk in People with Multiple Sclerosis

Vister, Eva; Tijsma, Mylou; Hoang, Phu; Lord, Stephen R.

doi:10.7224/1537-2073.2015-077

Cited by 37 publications

(18 citation statements)

References 38 publications

(34 reference statements)

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“…Figure 1 shows the flow of studies through the review selection process. Of the 33 studies eligible for inclusion, 9 were prospective cohort studies, 17–25 19 were cross-sectional studies 6,8,26–42 and 5 were randomised controlled trials with appropriate cross-sectional data. 43–47 In total, 12 authors were contacted for additional data, 10 of whom returned data in relation to 12 different studies.…”

Section: Resultsmentioning

confidence: 99%

The ability of clinical balance measures to identify falls risk in multiple sclerosis: a systematic review and meta-analysis

et al. 2017

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Section: Resultsmentioning

confidence: 99%

The ability of clinical balance measures to identify falls risk in multiple sclerosis: a systematic review and meta-analysis

et al. 2017

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“…39 This behaviour might be explained as a protective mechanism. However, this protective response may result in some people with MS curtailing their activities inappropriately, resulting in further deconditioning and adversely affecting physical function and independence s. 40 This adverse response should be avoided especially due to the growing body of evidence highlighting the benefits of regular physical activity for MS. 41 Most concerning is that studies have consistently found that fallers have lower quality of life than non-fallers 14,42 Lower quality of life, in turn, has higher associated socio-economic costs, and contributes significantly to the intangible costs and burden for the person with MS related to issues around self-care, pain, anxiety and depression. 32 Nilsagard et al 43 qualitatively explored the context and impact of accidental falls in pwMS, with participants describing falls as limiting, restrictive and embarrassing.…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

“…Interventions addressing personal and environmental factors associated with falls are lacking. For example, fear of falling and falls risk is not only associated with falls, but is an independent falls risk factor 84,42,85…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

Falls in People with Multiple Sclerosis

Coote¹,

Comber²,

Quinn³

et al. 2020

International Journal of MS Care

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Falls are highly prevalent for people with Multiple Sclerosis (MS) and result in a range of negative consequences such as injury, activity curtailment, reduced quality of life, increased need for care and time off work. This narrative review aims to summarise key literature and to discuss future work needed in the area of falls prevention for people with MS. The incidence of falls is estimated to be over 50%; similar to the rate of falls in adults over 80 years. The consequences of falls are considerable as rate of injury is high, and fear of falling and low self-efficacy are significant problems that lead to activity curtailment. A wide range of physiological, personal and environmental factors have been highlighted as potential risk factors and predictors of falls. Falls are individual and multifactorial, and hence approaches to interventions will likely need to adopt a multi-factorial approach. However, the literature to date has largely focused on exercise-based interventions, with newer more comprehensive interventions using both education and exercise showing promising results. Several gaps remain in the topic of falls in MS and in particular the lack of standardisation of definitions and outcome measures to enable data pooling and comparison. In future, the involvement of people with MS in the design and evaluation of programmes is essential, as are approaches to intervention development that consider implementation from the outset.

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“…Thus, finding the relationship between fatigue and gait patterns can help the therapist to develop suitable rehabilitation strategies for reducing the impact of fatigue on MS patients [ 14 ]. Furthermore, fatigue was found to strongly affect fall risk, balance performance, and fear of falling [ 15 ]. Hence, interventions to reduce fatigue can contribute to decreasing fall risk and fall-related injuries and improving overall quality of life.…”

Section: Introductionmentioning

confidence: 99%

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Ibrahim

Küderle

Gaßner

et al. 2020

J NeuroEngineering Rehabil

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Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.

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Fatigue, Physical Activity, Quality of Life, and Fall Risk in People with Multiple Sclerosis

Abstract: Background: Fatigue, inactivity, and falls are major health issues for people with multiple sclerosis (MS). We examined the extent to which fatigue and low walking activity are associated with quality of life and increased fall risk in people with MS.

Cited by 37 publications

References 38 publications

The ability of clinical balance measures to identify falls risk in multiple sclerosis: a systematic review and meta-analysis

The ability of clinical balance measures to identify falls risk in multiple sclerosis: a systematic review and meta-analysis

Falls in People with Multiple Sclerosis

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

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