Fatigue in multiple sclerosis

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ACADEMISCH PROEFSCHRIFT .... for reasons of comparability with other studies on fatigue in MS it was incorporated ...... Collega's en oud-collega's van het interdisciplinair MS behandelteam ... Vertrouwen, je eigen vaardigheden en beper-.

Fatigue in multiple sclerosis Measurement and management

Marc B. Rietberg

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The studies presented in this thesis were carried out at the Department of Rehabilitation Medicine of the VU University Medical Center Amsterdam, the Netherlands. The work was supported by a grant from the Stichting MS research, the Dutch MS research foundation (grant number 04553_MS). The printing of this thesis was financially supported by EFOX, the Scientific College Physical Therapy (WCF) of the Royal Dutch Society for Physical Therapy (KNGF) and the Dutch MS Research Foundation.

Cover

Marc Rietberg

Layout

Renate Siebes, Proefschrift.nu

Printed by

Ridderprint, Ridderkerk

ISBN

978-94-90791-38-4

© Copyright 2015 Marc B. Rietberg

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, photocopying, or otherwise, without the written permission of the author, or, when appropriate, of the publishers of the publications.

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vrije universiteit

Fatigue in multiple sclerosis Measurement and management

academisch proefschrift ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. F.A. van der Duyn Schouten, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op woensdag 11 november 2015 om 15.45 uur in de aula van de universiteit, De Boelelaan 1105 door

Martinus Berend Rietberg geboren te Groningen

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promotor: prof.dr. G. Kwakkel copromotor:

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dr. E.E.H. van Wegen

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Old friends pass away, new friends appear. It is just like the days. An old day passes, a new day arrives. The important thing is to make it meaningful: a meaningful friend – or a meaningful day. Dalai Lama

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Contents Chapter 1

General introduction

Chapter 2

Measuring fatigue in patients with multiple sclerosis:

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reproducibility, responsiveness and concurrent validity of three Dutch self-report questionnaires Chapter 3

Self-report fatigue questionnaires in multiple sclerosis,

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Parkinson’s disease and stroke: a systematic review of measurement properties Chapter 4

How reproducible is home-based 24-hour ambulatory

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monitoring of motor activity in patients with multiple sclerosis? Chapter 5

Do patients with MS show different daily physical activity

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patterns from healthy individuals? Chapter 6

The association between perceived fatigue and actual level of

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physical activity in multiple sclerosis Chapter 7

Effects of multidisciplinary rehabilitation on chronic fatigue in

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multiple sclerosis: a randomised controlled trial Chapter 8

TableOfContents_marc.indd 7

Summary and general discussion

157

Samenvatting (Summary in Dutch)

179

Dankwoord

187

About the author

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1 General introduction

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Chapter 1

Introduction Multiple sclerosis (MS) is primarily a chronic inflammatory disorder of the central nervous system (CNS) in which focal lymphocytic infiltration leads to damage of myelin and axons.1 Demyelisation results in slower conduction, or even a conduction block. Axonal lost results in sustained disabilities.2 In MS, lesions may occur in all parts of the CNS causing a wide variety of symptoms, including visual problems, altered sensation, muscle weakness, bladder- bowel or sexual problems, brainstem problems, and changes in cognitive function. In addition, patients with MS often experience severe fatigue.3 The incidence of MS is approximately 7 per 100,000 and the prevalence 80–120 per 100,000 in the Northern-American and Western-European countries,4 with almost 16,000 people affected in the Netherlands.5 MS is the most common neurological disorder affecting young adults, and with disease onset typically between the 20 and 40 years of age. Women are affected approximately two times more often than men. The exact etiology of the disease is unknown, but most likely MS is caused by a complex interplay between genetic, environmental and autoimmune factors.1 The clinical course of MS is highly variable and hard to predict.1 In approximately 85% of the patients, the disease starts with a relapsing-remitting (RR) course that is characterised by episodic acute periods of worsening clearly separated in time. A stable level of disability characterises the interval between the exacerbations. In RR-MS full recovery after an exacerbation is possible, disability evolves because recovery is incomplete. In 80% of the patients with MS the RR course is followed by the secondary progressive phase (SP-MS). This course is characterised by progressive deterioration, with or without exacerbations and remissions. In a relative small proportion, about 10%, of MS patients a continuously disease progression is seen from onset, without relapses, and referred to as primary progressive MS (PP-MS). PP-MS patients experience increasing disability without the presence of relapses.1,4,6,7 The most sporadic disease course is the progressive relapsing form of the disease, diagnosed in only 5% of the patients. The diagnosis MS is made on objective demonstration of dissemination in time (i.e. more than one disease event) and dissemination in space (i.e. involvement of more than one region in the CNS) on clinical grounds alone, or by careful and standardized integration of clinical and MRI findings or detection of oligoclonal bands in cerebrospinal fluid analysis.8

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knowledge on prognostic factors is derived from large group studies. However a recent systematic review has revealed several determinants indicative for long-term bad prognosis with an unfavourable disease course, such as: polysymptomatic onset with initial pyramidal, cerebellar, and sphincter involvement; an initially progressive disease course; higher age at

 

the time of diagnosis; a high number of early relapses; a short inter-relapse interval; and

General introduction

Determination of a detailed prognosis of MS at the individual level is difficult, while

early residual disability.9 A curative treatment for MS does not exist yet. Pharmacological treatment is focused on the pathophysiological mechanisms of MS, i.e. reducing the inflammatory response and with that the number of relapses.10,11 Besides treatment focussed on the neurodegenerative component of MS, multidisciplinary symptom management is critical in the care of MS patients for improving quality of life and the ability to work.10

Fatigue in MS To comprehend the full extent of fatigue in MS, the International Classification of Functioning, Disability and Health (ICF) can be used as a framework.12 Figure 1.1 shows the domains of the ICF and their mutual relationships. The domains are defined as body functions and structures (impairments), activities (limitations) and participation (restrictions). These

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three domains are related to health condition, in this context MS, and to two contextual factors, namely environmental and personal. In this context functioning denotes the positive aspects of the interaction between an individual (with a health condition) and that

Figure 1.1  The international classification of functioning, disability and health. A schematic representation of the relationships between the domains.

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Chapter 1

individual’s contextual factors (environmental and personal factors), and disability denotes the negative aspects of the interaction between an individual (with a health condition) and that individual’s contextual factors (environmental and personal factors). Next to describing health and disability in terms of functioning, the ICF framework can be used to classify outcome measures.12 Fatigue is considered to be one of the main causes of impaired daily activities and reduced quality of life in MS, reported by approximately 53%13 up to 92%14 of all MS patients. The poor understanding of the etiology underlying fatigue, the diverse consequences of fatigue, and the lack of adequate methods of measuring the impact of fatigue result in the current challenge of developing, testing, and prescribing effective interventions in patients with MS experiencing disabling fatigue.15,16 Different definitions of fatigue are proposed. A common used definition is the difficulty initiating or sustaining voluntary physical or mental activity.3,17-19 According to the ICF, fatigue is defined at the level of body functions and operationalised by code b1308: energy and drive functions, other specified [fatigue].12 Several pathophysiological mechanisms, such as dysregulation of the immune system, impaired nerve conduction, and neuro-endocrine and neurotransmitter changes have been suggested to explain fatigue in MS;3,20,21 however, the exact mechanisms remain unknown. Despite the poor understanding of the etiology, it is well accepted that fatigue is subjective and multidimensional in nature.22-23 The multidimensionality is believed to result from complex interactions between the underlying disease process,3,25,26 psychological22,27 and physical characteristics,26 as well as patients’ environmental factors.28,29 Based on assumed underlying pathophysiological mechanisms, the construct of fatigue in MS is often classified either into central and peripheral3 or into primary and secondary fatigue.21 Chaudhuri3 defined central fatigue as the failure to initiate and/or sustain attentional tasks (mental or cognitive fatigue) and physical activities (physical fatigue), and peripheral fatigue as muscle fatigability due to disorders of muscle and neuromuscular junctions. In the other classification, primary fatigue is considered to be a direct consequence of pathophysiological mechanisms of the MS disease process,3,21 while factors secondary to MS, such as pain and muscle spasms, and concomitant conditions such as viral infections, urinary infections, pregnancy, alcohol or substance abuse and depression may also contribute to feelings of fatigue.3,17,20,26 The Multiple Sclerosis Council for Clinical Practice Guidelines (MSCCPG)17 distinguishes chronic persistent fatigue from acute fatigue. Chronic persistent fatigue is

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weeks”, whereas acute fatigue is defined as “new or significant increase in feelings of fatigue in the previous six weeks”. Both chronic and acute fatigue can severely affect daily activities (i.e. activities and participation) and reduce quality of life. The present thesis is focussed on studying three corner stones in management of fatigue in

General introduction

defined as “being present for any amount of time on 50 percent of the days, for more than 6

patients with multiple sclerosis, namely: (1) measurement properties of outcomes to assess fatigue and daily physical activity; (2) the relationship between fatigue and daily physical activity; and (3) multidisciplinary treatment of fatigue.

Fatigue assessment Fatigue can be assessed by using performance-based or self-report measures. Performance based assessment quantifies a change in performance or sustained mental or physical activity,18 and is not included in this thesis. Self-report assessment quantifies the perceived level of fatigue or perceived impact of fatigue. Most of the time fatigue in MS is assessed by means of self-report questionnaires. Fatigue according to the ICF is defined at the level of body functions, however most of the time it is assessed at the level of activity and participation. Assessment of fatigue is challenged

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by absence of a golden standard. Furthermore, a broadly accepted definition of fatigue is lacking,3 and with that, the determination of its many dimensions.3,30 The multidimensionality of MS related fatigue is illustrated by the different conceptual approaches for measuring fatigue, as each questionnaire is characterized by its own underlying concept. As a result, the different self-report questionnaires may measure different aspects or dimensions of fatigue.31 To address the multidimensionality of fatigue some studies use a combination of scales.26 In preparation for our randomized controlled trial on the effect of an individually tailored, multidisciplinary outpatient rehabilitation programme on chronic fatigue in MS presented in chapter 7, we have selected three questionnaires for outcome measurement. The selection of the Checklist Individual Strength (CIS20R), the Fatigue Severity Scale (FSS), and the Modified Fatigue Impact Scale (MFIS) was based on evidence and existing guidelines at that time. The CIS20R seems a promising questionnaire for use in fatigue evaluation. It recognises the multidimensional nature of fatigue in MS, but its use in MS research has been limited until now. The FSS is perhaps the most commonly used self-report questionnaire of fatigue severity in patients with MS, and

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Chapter 1

for reasons of comparability with other studies on fatigue in MS it was incorporated in the outcome set. The MFIS was recommended for assessment of fatigue in patients with MS by the Multiple Sclerosis Council for Clinical guidelines17 and more recently in a review on self-report assessment of fatigue in MS.32 However, the measurement properties of these three questionnaires have not been assessed in the same sample of patients. Therefore, in chapter 2 of this thesis the concurrent validity of CIS-20R, FSS and MFIS was studied. Next to concurrent validity, reliability and measurement error were studied in the same group of patients with MS. The multidimensionality of MS-related fatigue is also illustrated by the large number of self-report fatigue questionnaires used in MS samples.32 Kos and colleagues32 found in 2004 eighteen instruments, either fatigue specific or as part of quality of life instruments, that evaluate fatigue in MS. Guidelines17 and systematic reviews performed with the aim to help clinicians and researchers in choosing appropriate outcome measures by evaluating measurement properties of fatigue questionnaires, often show the limitation of not using uniform definitions and standards for the assessment of the methodological quality of the included studies. Chapter 3 of this thesis presents a systematic review of measurement properties of self-report questionnaires used in multiple sclerosis, Parkinson’s disease and stroke. A critical appraisal tool, the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN)33 was used, containing standards for systematically evaluating the methodological quality of studies on the measurement properties of health measurement instruments.

Daily physical activity in MS Amassing evidence34,35 indicates that patients with MS are less physically active than nondiseased people. However, they show quite similar activity levels compared to patients with, for example, chronic fatigue syndrome, chronic obstructive pulmonary disease or cerebral palsy.34 According to the ICF,12 daily physical activity is defined at the level of activity and partici­ pation. Activity concerns the ability to execute a task or action by an individual, and participation as involvement in a life situation. However, while activity and participation are separately defined within the ICF, they are treated as one category. According to the ICF, daily physical activity is operationalised by codes like code d410; changing basic body position, code d415; maintaining a body position, code d430; lifting and carrying objects, code d435; moving objects with lower extremities, code d440; fine hand use, code d445; hand and arm

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locations, code d510; washing oneself, code d530; toileting, code d540; dressing, code d550; eating, code d560; drinking.12 The inability to perform such a task is classified as an activity limitation. Participation concerns attainment of meaningful goals, such as performing work, visiting a supermarket or attending school or social activities. The inability of involvement in life situations is classified in the ICF as restrictions to participation.12

General introduction

use code d450; walking, code d455; moving around, code d460; moving around in different

The observed reduction in the daily activity is often held against underlying impairments such as, muscle weakness, spasticity, ataxia and fatigue.3,35,36 Next to fatigue, limitations in physical activity are acknowledged as a key problem, with up to 85% of patients reporting walking difficulties.37,38 Moreover, walking impairments have major impact on execution of tasks or actions (activities), and involvement in life situations (participation).

Daily physical activity measurement Key questions are how real world activities of daily living performed in patients own home environment such as lying, sitting, performing transfers and walking are related to perceived fatigue assessed with self-report questionnaires, and which strategies patients use to manage their 24 hour diurnal rhythm. Previously used methods to assess the level of physical activity are: (1) behavioural mapping by clinical observations;39 (2) using self-report

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questionnaires;40,41 (3) using patient diaries;42 and (4) applying semistructured interviews.43 However, these instruments measure at discrete moments in time and typically give a subjective indication of activity level. Advances in technology have fostered the development of objective methods allowing more continuous monitoring of daily physical activity using actigraphy44 and/or accelerometry45 in patients’ natural environment. Self-reported activities are only moderately related to objective registration and do not completely match actual activity because of possible recall bias.46 Following the ICF,12 Activity Monitoring (AM) measures can be classified at the body functions and structures (impairments), activities (limitations) or participation (restrictions) domain. Unfortunately, reproducibility of 24 hour AM has not been investigated in patients with MS. Therefore, in chapter 4 of this thesis, reliability and measurement error of 24-hour monitoring of mobility-related activities in patients with MS was studied. Studies investigating actual free-living physical activity behaviour of patients with MS are rare. From the literature, there is accumulating evidence that patients with MS have lower

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Chapter 1

physical activity levels than healthy age- and gender-matched subjects,34,35 but little is known about diurnal differences in distribution of these activities. From this perspective, AM allows to measure patients daily activity profiles in association with perceived fatigue. Better insight in these longitudinal associations may give clinicians information about: (1) patients preferred strategies to manage fatigue; and (2) patients ability to handle with the fluctuating energy levels on a day. In addition, the impact of related, sometimes confounding factors such as MS-type, EDSS severity, medication intake, age, and depression on the relationship between AM and fatigue can be investigated. Therefore, in chapter 5 a study is presented that investigated the amount, type and distribution of daily physical behaviour in patients with MS living in their own community setting. MS patients were compared with age and gender matched healthy controls.

Fatigue and the relationship with daily physical activity The assumed vicious circle of the impact of fatigue on decline in capacity to perform activities of daily living is poorly understood. Figure 1.2 shows the proposed determinants of motor performance: effort capacity and effort tolerance. In this relationship both factors seem to mutually influence each other.47 An impaired balance between, on the one hand, the capacity to produce effort (action) and, on the other hand, the tolerance to cope with increased effort (perception) is believed to worsen over time, acknowledging that increased physical activity may enhance feelings of fatigue, whereas increased feelings of fatigue may limit physical activity.47 Motor performance (Force and endurance)

Effort capacity

Effort tolerance

Generation / maintenance of energy production

Adaptation to / recovery from the stress of the effort

(Including peripheral and central mechanisms)

(Based on neurobiological stress system functioning)

Figure 1.2  Proposed determinants of motor performance: effort capacity and effort tolerance (according to the model of Van Houdenhove et al. 2007).

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a vicious circle in which impaired fitness due to a reduction in physical activity in turn may result in increased feelings of fatigue.48 However, these relations are rather speculative and unexplored in people with MS is unclear. In addition, several studies in patients with MS also found significant associations of

General introduction

As a consequence, it has been hypothesized that being less active due to fatigue can lead to

fatigue with variables such as age,13 physical disability,13,49,50 disease sub-type,13 anxiety,51 depression,49,50 and health-related quality of life.49 Motl and colleagues35 found a small but significant association between fatigue and depression with self-reported physical inactivity (r=0.42) even when corrected for disease severity (Expanded Disability Status Scale [EDSS] score) or MS-disease course. In a recent qualitative study, Kayes et al.52 reported that fatigue is seen by MS patients as a barrier to taking part in physical activity related to the previously mentioned vicious cycle. However, Vercoulen et al.53 found no significant correlation for patients with MS between physical activity (assessed with an actometer), and perceived fatigue assessed with the Checklist Individual Strength subscale ‘Subjective Fatigue’ over a period of 12 days. Therefore, a potential found relationship of physical activity with fatigue might be specific to the type of fatigue questionnaire used, as questionnaires typically evaluate different underlying constructs of fatigue.31,54 In addition, the method used to measure physical activity by using self-report scales46 or activity monitoring46,55,56 may also

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affect found relationships between physical activity and fatigue. Chapter 6 presents a study that aimed to determine the relationship between the actual amount of physical activity performed over a 24-hour period in MS patients’ own community setting, and self-reported perceived fatigue as assessed by three common used self-report questionnaires (i.e. FSS, MFIS and CIS-20R). Additionally, we investigated whether the associations between physical activity and perceived fatigue were confounded by factors such as age, disability status, disease duration, disease sub-type, depression and anxiety, as previous studies have not addressed this.

Fatigue management The pathophysiological basis of fatigue remains unclear and consequently effective treatment is limited. A number of clinical trials have tested a variety of pharmacological and nonpharmacological interventions for MS-related fatigue. Several drugs such as amantadine, pemoline, modafinil, and aminopyridine have been advocated with respect to their effect on fatigue in MS. The evidence for effects of pharmacological treatment is not established, with

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exception of amantadine, which might be of benefit to some MS patients.57-62 Moreover, for most above-mentioned pharmacological agents adverse effects have been reported.58,63,64 In addition, several non-pharmacological treatment interventions, such as aerobic training,65-69 cognitive behaviour therapy61,70 and energy management strategies71,72 aim to reduce the impact of fatigue on patients with MS and are not associated with adverse effects. There is some evidence from RCTs61,65,67-72 that these interventions might be beneficial to MS patients. However, rigorous evidence underpinning them is also lacking. In view of the multidimensional character, it is suggested that fatigue in MS should be managed in a tailored, multidisciplinary way. Acknowledging that there is limited evidence that some MS patients benefit from particular interventions, gaps remain in the current evidence base. Moreover, to date, few randomized clinical trials have evaluated the effects of a combination of these treatment interventions on chronic fatigue in MS as the main focus of intervention, with subjective fatigue as the primary measure of outcome.73 Chapter 7 describes a single-blinded, randomized controlled trial (RCT) in which the effects of a multidisciplinary outpatient rehabilitation programme on chronic fatigue was investigated and compared to monodisciplinary consultation by an MS nurse. For this Treatment of Fatigue (ToF) trial, an individually tailored, multidisciplinary outpatient rehabilitation programme was developed in the MS Center of the VU University medical center (VUmc). In this programme we focussed on treatment of chronic fatigue, acknowledging the recommendation of the MSCCPG17 that chronic fatigue should be distinguished from acute fatigue, and requires different management approaches. In our trial we used descriptive modifiers postulated by the MSCCPG17 to differentiate between acute and chronic fatigue. In the general discussion in chapter 8, the main findings, methodological issues, clinical implications, and future directions following from this thesis are provided.

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1.

Compston A, Coles A. Multiple sclerosis. Lancet. 2008 Oct 25; 372(9648): 1502-17.

2.

Petzold A, Eikelenboom MJ, Keir G, Grant D, Lazeron RH, Polman CH, Uitdehaag BM, Thompson EJ, Giovannoni G. Axonal damage accumulates in the progressive phase of multiple sclerosis: three year follow up study. J Neurol Neurosurg Psychiatry. 2005; 76: 206-11.

3.

Chaudhuri A, Behan PO. Fatigue in neurological disorders. Lancet. 2004 Mar 20; 363 (9413): 978-88.

4.

Compston A, Coles A. Multiple sclerosis. Lancet. 2002 Apr 6; 359(9313): 1221-31. Review. Erratum in: Lancet 2002 Aug 24; 360(9333): 648.

5.

Stichting MS Research; www.ms research.nl.

6.

Lublin FD, Reingold SC. Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology. 1996 Apr; 46(4): 907-11.

7.

Koch-Henriksen N, Sørensen PS. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol. 2010 May; 9(5): 520-32.

8.

Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O’Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011 Feb; 69(2): 292-302.

9.

Bergamaschi R. Prognostic factors in multiple sclerosis. Int Rev Neurobiol. 2007; 79: 423-47.

10.

Kamm CP, Uitdehaag BM, Polman CH. Multiple sclerosis: current knowledge and future outlook. Eur Neurol. 2014; 72(3-4): 132-41.

11.

CBO werkgroep. Richtlijn Multiple Sclerose, Bohn Stafleu van Loghum, 2012, Houten.

12.

International Classification of Functioning, Disability and Health. Geneva, Switserland: World Health Organisation, 2001.

13.

Colosimo C, Millefiorini E, Grasso MG, Vinci F, Fiorelli M, Koudriavtseva T, Pozzilli C. Fatigue in MS is associated with specific clinical features. Acta Neurol Scand. 1995 Nov; 92(5): 353-5.

14.

Fisk JD, Ritvo PG, Ross L, Haase DA, Marrie TJ, Schlech WF. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis. 1994 Jan; 18 Suppl 1: S79-83.

15.

Möller F, Poettgen J, Broemel F, Neuhaus A, Daumer M, Heesen C. HAGIL (Hamburg Vigil Study): a randomized placebo-controlled double-blind study with modafinil for treatment of fatigue in patients with multiple sclerosis. Mult Scler. 2011; 17 (8): 1002-9.

16.

Asano M, Finlayson ML. Meta-analysis of three different types of fatigue management interventions for people with multiple sclerosis: exercise, education, and medication. Mult Scler Int. 2014; 2014: 798285.

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General introduction

References

1

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20

Chapter 1

17.

Multiple Sclerosis Council for Clinical Guidelines. Fatigue and multiple sclerosis: evidence based management strategies for fatigue in multiple sclerosis. Washington, DC: Paralyzed Veterans of America; 1998.

18.

Schwid SR, Covington M, Segal BM, Goodman AD. Fatigue in multiple sclerosis: current understanding and future directions. J Rehabil Res Dev. 2002 Mar-Apr; 39(2): 211-24.

19.

Friedman JH, Brown RG, Comella C, Garber CE, Krupp LB, Lou JS, Marsh L, Nail L, Shulman L, Taylor CB; Working Group on Fatigue in Parkinson’s Disease. Fatigue in Parkinson’s disease: a review. MovDisord. 2007; 22: 297-308.

20.

Vucic S, Burke D, Kiernan MC. Fatigue in multiple sclerosis: mechanisms and management. Clin Neurophysiol. 2010; 121(6): 809-17.

21.

MacAllister WS, Krupp LB. Multiple sclerosis-related fatigue. Phys Med Rehabil Clin N Am. 2005; 16(2): 483-502.

22.

Vercoulen JH, Hommes OR, Swanink CM, Jongen PJ, Fennis JF, Galama JM, van der Meer JW, Bleijenberg G. The measurement of fatigue in patients with multiple sclerosis. A multidimensional comparison with patients with chronic fatigue syndrome and healthy subjects. Arch Neurol. 1996 Jul; 53(7): 642-9.

23.

Flachenecker P, Kümpfel T, Kallmann B, Gottschalk M, Grauer O, Rieckmann P, Trenkwalder C, Toyka KV. Fatigue in multiple sclerosis: a comparison of different rating scales and correlation to clinical parameters. Mult Scler. 2002 Dec; 8(6): 523-6.

24.

Kos D, Kerckhofs E, Carrea I, Verza R, Ramos M, Jansa J. Evaluation of the Modified Fatigue Impact Scale in four different European countries. Mult Scler. 2005; 11: 76-80.

25.

Bakshi R. Fatigue associated with multiple sclerosis: diagnosis, impact and management. Mult Scler. 2003 Jun; 9(3): 219-27.

26.

Kos D, Kerckhofs E, Nagels G, D’hooghe MB, Ilsbroukx S. Origin of fatigue in multiple sclerosis: review of the literature. Neurorehabil Neural Repair. 2008 Jan-Feb; 22(1): 91-100.

27.

Bol Y, Duits AA, Hupperts RM, Vlaeyen JW, Verhey FR. The psychology of fatigue in patients with multiple sclerosis: a review. J Psychosom Res. 2009 Jan; 66(1): 3-11. Epub 2008 Sep 24.

28.

Baker DG. Multiple sclerosis and thermoregulatory dysfunction. J Appl Physiol. 2002 May; 92(5): 1779-80.

29.

Johansson S, Ytterberg C, Hillert J, Widén Holmqvist L, von Koch L. A longitudinal study of variations in and predictors of fatigue in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2008 Apr; 79(4): 454-7.

30.

Debouverie M, Pittion-Vouyovitch S, Louis S, Guillemin F. Validity of a French version of the fatigue impact scale in multiple sclerosis. Mult Scler. 2007; 13(8): 1026-32.

31.

Dittner A, Wessely S, Brown R. The assessment of fatigue: A practical guide for clinicians and researchers. Journal of Psychosomatic Research. 2004; 56(2): 157-70.

32.

Kos D, Kerckhofs E, Ketelaer P, Duportail M, Nagels G, D’Hooghe M, Nuyens G. Self-report assessment of fatigue in multiple sclerosis: A critical evaluation. Occup Ther Health Care. 2004; 17(3-4): 45-62.

Chapter_1_Marc.indd 20

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21 Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, Bouter LM, de Vet HC. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: An international Delphi study. Qual Life Res. 2010; 19(4): 539-49.

34.

Motl RW, McAuley E, Snook EM. Physical activity and multiple sclerosis: a meta-analysis. Mult Scler. 2005; 11: 459-63.

35.

Motl RW, Arnett PA, Smith MM, Barwick FH, Ahlstrom B, Stover EJ. Worsening of symptoms is associated with lower physical activity levels in individuals with multiple sclerosis. Mult Scler. 2008; 14: 140-2.

36.

Snook EM, Motl RW. Effect of exercise training on walking mobility in multiple sclerosis: a metaanalysis. Neurorehabil Neural Repair. 2009; 23: 108-16.

37.

White LJ, Dressendorfer RH. Exercise and multiple sclerosis. Sports Med. 2004; 34: 1077-100.

38.

Morris ME, Vowels L, Dodd K. Changes in gait and fatigue from morning to afternoon in people with multiple sclerosis. J Neurol Neurosurg Psychiatry. 2002; 72: 361-5.

39.

Veitch J, Salmon J, Ball K. Children’s active free play in local neighborhoods: a behavioral mapping study. Health Educ Res. 2008; 23: 870-9.

40.

Patterson SM, Krantz DS, Montgomery LC, Deuster PA, Hedges SM, Nebel LE. Automated physical activity monitoring: validation and comparison with physiological and self-report measures. Psychophysiology. 1993; 30: 296-305.

41.

Washburn RA, Montoye HJ. The assessment of physical activity by questionnaire. Am J Epidemiol. 1986; 123: 563-76.

42.

Ghione S, Rosa C, Salerno L, Ceccanti G, Volterrani D. Assessment of physical and mental activity using a standardized computer-assisted diary during ambulatory blood pressure monitoring. J Hypertens (Suppl). 1993; 11: S284-5.

43.

Melanson EL Jr, Freedson PS. Physical activity assessment: a review of methods. Crit Rev Food Sci Nutr. 1996; 36: 385-96.

44.

Littner M, Kushida CA, Anderson WM, Bailey D, Berry RB, Davila DG, Hirshkowitz M, Kapen S, Kramer M, Loube D, Wise M, Johnson SF. Standards of Practice Committee of the American Academy of Sleep Medicine. Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep. 2003; 26: 337-41.

45.

Bussmann JB, Martens WL, Tulen JH, Schasfoort FC, van den Berg-Emons HJ, Stam HJ. Measuring daily behavior using ambulatory accelerometry: the activity monitor. Behav Res Methods Instrum Comput. 2001; 33: 349-56.

46.

Gosney JL, Scott JA, Snook EM, Motl RW. Physical activity and multiple sclerosis: validity of self-report and objective measures. Fam Community Health. 2007; 30: 144-50.

47.

Van Houdenhove B, Verheyen L, Pardaens K, Luyten P, Van Wambeke P. Rehabilitation of decreased motor performance in patients with chronic fatigue syndrome: should we treat low effort capacity or reduced effort tolerance? Clin Rehabil. 2007; 21: 1121-42.

Chapter_1_Marc.indd 21

General introduction

33.

1

29-7-2015 10:40:07

22

Chapter 1

48.

Beckerman H, de Groot V, Scholten MA, Kempen JC, Lankhorst GJ. Physical activity behavior of people with multiple sclerosis: understanding how they can become more physically active. Phys Ther. 2010; 90: 1001-13.

49.

Pittion-Vouyovitch S, Debouverie M, Guillemin F, Vandenberghe N, Anxionnat R, Vespignani H. Fatigue in multiple sclerosis is related to disability, depression and quality of life. J Neurol Sci. 2006; 243(1-2): 39-45.

50.

Kroencke DC, Lynch SG, Denney DR. Fatigue in multiple sclerosis: relationship to depression, disability, and disease pattern. Mult Scler. 2000; 6: 131-6.

51.

Ford H, Trigwell P, Johnson M. The nature of fatigue in multiple sclerosis. J Psychosom Res. 1998; 45(1 Spec. No.): 33-38.

52.

Kayes NM, McPherson KM, Taylor D, Schlüter PJ, Kolt GS. Facilitators and barriers to engagement in physical activity for people with multiple sclerosis: a qualitative investigation. Disabil Rehabil. 2011; 33(8): 625-42.

53.

Vercoulen JH, Bazelmans E, Swanink CM, Fennis JF, Galama JM, Jongen PJ, Hommes O, Van der Meer JW, Bleijenberg G. Physical activity in chronic fatigue syndrome: assessment and its role in fatigue. J Psychiatr Res. 1997; 31: 661-73.

54.

Kos D, Kerckhofs E, Nagels G, D’Hooghe BD, Duquet W, Duportail M, Ketelaer P. Assessing fatigue in multiple sclerosis: Dutch modified fatigue impact scale. Acta Neurol Belg. 2003; 103(4): 185-91.

55.

Motl RW, McAuley E, Snook EM, Scott JA. Validity of physical activity measures in ambulatory individuals with multiple sclerosis. Disabil Rehabil. 2006; 28: 1151-6.

56.

Kos D, Nagels G, D’Hooghe MB, Duquet W, Ilsbroukx S, Delbeke S, Kerckhofs E. Measuring activity patterns using actigraphy in multiple sclerosis. Chronobiol Int. 2007; 24: 345-56.

57.

Weinshenker BG, Penman M, Bass B, Ebers GC, Rice GP. A double blind, randomized, crossover trial of pemoline in fatigue associated with multiple sclerosis. Neurology. 1992; 42: 1468-71.

58.

Brañas P, Jordan R, Fry-Smith A, Burls A, Hyde C. Treatments for fatigue in multiple sclerosis: a rapid and systematic review. Health Technol Assess. 2000; 4(27): 1-61.

59.

Rammohan KW, Rosenberg JH, Lynn DJ, Blumenfeld AM, Pollak CP, Nagaraja HN. Efficacy and safety of modafinil (Provigil) for the treatment of fatigue in multiple sclerosis: a two centre phase 2 study. J Neurol Neurosurg Psychiatry. 2002; 72: 179-83.

60.

Stankoff B, Waubant E, Confavreux C, Edan G, Debouverie M, Rumbach L, Moreau T, Pelletier J, Lubetzki C, Clanet M; French Modafinil Study Group. Modafinil for fatigue in MS: a randomized placebo-controlled double-blind study. Neurology. 2005 Apr 12; 64(7): 1139-43.

61.

Van Kessel K, Moss-Morris R, Willoughby E, Chalder T, Johnson MH, Robinson E. A. A Randomized Controlled Trial of Cognitive Behavior Therapy for Multiple Sclerosis Fatigue. Psychosom Med. 2008; 70: 205-13.

62.

Brown JN, Howard CA, Kemp DW. Modafinil for the treatment of multiple sclerosis-related fatigue. Ann Pharmacother. 2010 Jun; 44(6): 1098-103.

63.

Noseworthy JH. Clinical trials in multiple sclerosis. Curr Opin Neurol Neurosurg. 1993; 6: 209-15.

Chapter_1_Marc.indd 22

29-7-2015 10:40:07

23 Van Oosten BW, Truyen L, Barkhof F, Polman CH. Choosing drug therapy for multiple sclerosis. An update. Drugs. 1998; 56: 555-69.

65.

Petajan JH, Gappmaier E, White AT. Impact of aerobic training on fitness and quality of life in multiple sclerosis. Ann Neurol. 1996; 39: 432-41.

66.

Rietberg MB, Brooks D, Uitdehaag BM, Kwakkel G. Exercise therapy for multiple sclerosis. Cochrane Database Syst Rev. 2005; 1:CD003980.

67.

Cakt BD, Nacir B, Genç H, Saraçoğlu M, Karagöz A, Erdem HR, Ergün U. Cycling progressive resistance training for people with multiple sclerosis: a randomized controlled study. Am J Phys Med Rehabil. 2010 Jun; 89(6): 446-57.

68.

Dalgas U, Stenager E, Jakobsen J, Petersen T, Hansen HJ, Knudsen C, Overgaard K, IngemannHansen T. Fatigue, mood and quality of life improve in MS patients after progressive resistance training. Mult Scler. 2010 Apr; 16(4): 480-90.

69.

Oken BS, Kishiyama S, Zajdel D, Bourdette D, Carlsen J, Haas M, Hugos C, Kraemer DF, Lawrence J, Mass M. Randomized controlled trial of yoga and exercise in multiple sclerosis. Neurology. 2004 Jun 8; 62(11): 2058-64.

70.

Mohr DC, Hart S, Vella L. Reduction in disability in a randomized controlled trial of telephoneadministered cognitive-behavioral therapy. Health Psychol. 2007 Sep; 26(5): 554-63.

71.

Mathiowetz VG, Finlayson ML, Matuska KM, Chen HY, Luo P. Randomized controlled trial of an energy conservation course for persons with multiple sclerosis. Mult Scler. 2005 Oct; 11(5): 592-601.

72.

Mathiowetz VG, Matuska KM, Finlayson ML, Luo P, Chen HY. One-year follow-up to a randomized controlled trial of an energy conservation course for persons with multipele sclerosis. International Journal of Rehabilitation Research. 2007; 30(4): 305-13.

73.

Neill J, Belan I, Ried K. Effectiveness of non-pharmacological interventions for fatigue in adults with multiple sclerosis, rheumatoid arthritis, or systemic lupus erythematosus: a systematic review. J Adv Nurs. 2006; 56(6):617-35. Erratum in J Adv Nurs. 2007 Jan; 57(2): 225.

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2 Measuring fatigue in patients with multiple sclerosis: reproducibility, responsiveness and concurrent validity of three Dutch self-report questionnaires M.B. Rietberg E.E.H. van Wegen G. Kwakkel

Disabil Rehabil. 2010; 32(22): 1870-6.

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Abstract Purpose:  To determine the reproducibility, responsiveness and concurrent validity of Dutch versions of the Fatigue Severity Scale (FSS), Modified Fatigue Impact Scale (MFIS), and Checklist Individual Strength (CIS20R) in patients with Multiple Sclerosis (MS). Method:  Forthy-three ambulatory patients with MS (mean age 48.7 years; SD 7 years; 30 women; median EDSS score 3.5) completed the questionnaires twice within one week. The Intraclass Correlation Coefficients (ICCs), Bland and Altman analysis, the smallest detectable change (SDC) and the Minimal detectable change (MDC) were calculated. Concurrent validity was determined by Pearson’s correlation coefficients. Results:  ICCs ranged from 0.76 (FSS), to 0.85 (MFIS) to 0.81 (CIS20R). Bland and Altman analysis showed no significant systematic differences between assessments. MDCs were 20.7% (FSS), 19.23% (MFIS), and 17.7% (CIS20R). Pearson correlation coefficients were r=0.66 (FSS-MFIS), r=0.54 (MFIS- CIS20R) and r=0.42 (CIS20R-FSS). Conclusion:  Despite good test-retest reliability of FSS, MFIS and the CIS20R, the present study shows that fatigue questionnaires are not very responsive for change in patients with MS. This finding suggests that future trials should monitor profiles of fatigue by repeated measurements rather than pre-post assessments alone. The moderate associations suggest that the three questionnaires largely measure different aspects of perceived fatigue.

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Numerous studies report fatigue as the most common symptom in multiple sclerosis (MS).1-6 Fatigue is reported by 65 to 95% of all MS patients, and between 15 and 60% of the patients report fatigue as the most disabling problem, severely limiting daily activities and having a major impact on quality of life.1-4,7-10 Although the exact etiology of fatigue in MS is unclear and consensus on defining fatigue is still lacking, proposed definitions11,12 support the general clinical notion that MS-related fatigue is subjective and multidimensional in nature. The multidimensionality is believed to result from a complex interplay between underlying disease process,12-14 psychological15,16 and physical

Measuring fatigue in patients with MS

Introduction

characteristics14 as well as patients’ environmental factors.17,18 The multidimensionality of MS related fatigue is illustrated by the large number of questionnaires used in MS samples,14 such as the Fatigue severity Scale (FSS),19 the Fatigue Assessment Instrument (FAI),20 the Fatigue Impact Scale (FIS),21 the Checklist Individual Strength (CIS20R),15 the Modified Fatigue Impact Scale (MFIS),11 and the Fatigue Descriptive Scale (FDS).22 The multidimensionality of MS related fatigue is also manifested by the different conceptual approaches of measuring fatigue. For example, the FSS19 assesses the severity of fatigue symptoms and its impact on an individual’s daily functioning during the past week, whereas the MFIS assesses the perceived impact of fatigue on the domains physical, cognitive and psychosocial functioning during the past four weeks. The CIS20R15 assesses four dimensions

2

related to fatigue: subjective experience of fatigue; reduction in motivation; reduction in activity and reduction in concentration over the last two weeks. Most approaches to fatigue assessment can be classified as either self-report scales or performance-based measures of motor or cognitive output.6 The most commonly used method, and perchance the best way to quantify fatigue, in clinical practice and in research is the use of self-report questionnaires.23,24 Of the above listed self-report instruments for assessing fatigue, the FSS is perhaps one of the most commonly used measures of fatigue severity in patients with MS.23 The psychometric properties of the FSS have been evaluated in MS patients,19 the FSS is easy to administer and has a high degree of validity and sensitivity to clinical changes.19 The MFIS is recommended for clinical practice and research by the Multiple Sclerosis Council for Clinical guidelines.11 Psychometrics have been evaluated in a Dutch version of the MFIS.25 That study indicates that the Dutch version of the MFIS is a reliable, valid and responsive tool to assess the impact of MS-related fatigue on daily life.

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The Checklist Individual Strength (CIS20R)15 recognises the multidimensionality of fatigue in MS, but its use in MS research is limited until now. While norm scores for severe fatigue are available,26 psychometric properties of the CIS20R, like reproducibility and concurrent validity with other commonly used scales in the MS population are lacking. The aim of the present study was to determine the reproducibility, responsiveness and concurrent validity of the Dutch versions of the Fatigue Severity Scale (FSS), Modified Fatigue Impact Scale (MFIS) and Checklist Individual Strength (CIS20R) in patients with MS.

Methods Subject selection Patients suffering from MS were recruited from the MS center of the VU University Medical Center (VUmc), the Netherlands. Patients met the following inclusion criteria: (1) older than 18 years; (2) a definite diagnosis of MS;27 (3) an Expanded Disability Status Scale (EDSS)28 score below 6.5; (4) no co-morbidity that could influence fatigue; (5) written informed consent. All participants gave informed consent, in accordance with the ethical standards of the declaration of Helsinki. The local medical ethics committee approved the present study.

Fatigue questionnaires Three questionnaires, the FSS, the MFIS, and the CIS20R, were used to assess fatigue. The Fatigue Severity Scale (FSS)19 is a nine-item self-report questionnaire to assess the severity of fatigue and its impact on an individual’s daily functioning. Participants rate their agreement with a statement ranging from one point, reflecting ‘strongly disagree’ to seven points representing ‘strongly agree’, depending on how appropriate they feel the statement applies to them. A total sum score is calculated. Translation and back translation was performed by two independent linguists and evaluated by a panel of three clinical experts. The Modified Fatigue Impact Scale (MFIS) is a shortened Dutch version of the 40–item Fatigue Impact Scale3 and assesses the perceived impact of fatigue on the subscales physical, cognitive and psychosocial functioning during the past four weeks. Participants rate on a five point Likert scale, with 0 = ‘Never’ to 4 = ‘Almost always’, their agreement with 21

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the three subscales. The CIS20R15 assesses fatigue during the past two weeks and consists of four dimensions: subjective experience of fatigue; reduction in motivation; reduction in activity and reduction in concentration. The CIS20R consists of twenty statements for which the participant has to indicate on a seven point scale ranging from ‘Yes, that is true’ to ‘No, that is not true’ to what extent the particular statement applies to him or her.30 Subscores for the domains as well a total score are calculated.

Measuring fatigue in patients with MS

statements. The items can be aggregated into a total MFIS score, as well as into a score for 29

Design Participants filled in the questionnaires twice with an interval of one week, during visits from an assessor in patients’ own home. To prevent carry-over effects, three different questionnaire orders (MFIS/FSS/CIS20R, CIS20R/MFIS/FSS, and FSS/CIS20R/MFIS) were composed and given in a random order. In random order, half of the participants had both the test and retest assessments in the morning, the other half of the participants in the afternoon to control for influences of diurnal fluctuations in perception of fatigue. The participants were verbally instructed to read each statement carefully, and then circle the one number that best indicates their agreement. In case participants had difficulty with selecting an answer, they were told to choose the answer that comes closest to describing their perceived symptoms

2

of fatigue. If the participant needed help in understanding words or phrases, or marking their responses, the assessor assisted.

Statistical analysis All data were analyzed with SPSS statistical package (version 15.0). First, descriptive statistics were used to determine mean age, gender, duration of disease and, type of MS. Next, associations of these characteristics with fatigue following the three questionnaires were explored, using Pearson correlation coefficients. Reproducibility concerns the degree to which repeated measurements provide similar results.30 Reproducibility was determined by calculating Intraclass Correlation Coefficients (ICCs) for test-retest reliability and by applying the Bland and Altman method for agreement between the two measurements.

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Test-retest reliability Reliability was defined as how well the scores of the participants can be distinguished from each other on a fatigue questionnaire, despite existing measurement errors.31 For the ICCs, a two-way random effects model was used assuming included patients and assessors are a random selection of both populations.

Agreement In addition we were interested in the absolute agreement between two consecutive assessments and therefore used the absolute agreement definition in the calculation of ICC.32 In the present study, an ICC beyond 0.70 was defined as good reliability, an ICC between 0.40 and 0.70 as moderate reliability and an ICC below 0.40 as poor reliability. Agreement concerns the measurement error, and assesses how close derived scores on the fatigue questionnaires produces exactly the same outcome.31 For this purpose, the Bland and Altman method was used by plotting the mean difference (Mean Δ) between the two consecutive measurements against the standard deviation (SD) of this difference. 33 The ‘limits of agreement’ were calculated as the mean difference ± 1.96 times the standard deviation of the differences.

Responsiveness Bland and Altman analyses indicated no large systematic differences with regard to the limits of agreement for the FSS, MFIS, and CIS20R, therefore we choose to calculate the Smallest Detectable Change (SDC) on the basis of the limits of agreement, which is based on the Standard Error of Measurement (SEM) consistency. Responsiveness is the ability of an instrument to measure real or important change over time, in the concept being measured.34 A distribution-based method was used to estimate the percentage change between the two assessments which should be exceeded to exclude measurement error, by determining the SDC.35,36 The SDC was calculated by 1.96 x √2 x SEM to indicate 95% confidence for real change between the two assessments scores.37 The SEM was calculated by SD x √(1-R), with R=ICC and SD=√(total variance).31 In order to allow comparison between the three questionnaires, the Minimal Detectable Change (MDC) was calculated by expressing the SDC as a percentage of the maximal feasible score for each questionnaire.36

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31

Since visual inspection of histograms of FSS, MFIS, and CIS20R scores for MS patients showed a normal distribution, concurrent validity was determined using Pearson’s correlation coefficients. Strong association was defined if coefficients were beyond 0.70, whereas coefficients between 0.30 and 0.70 were classified as moderate to substantial and correlation coefficients less than 0.30 as a weak association.38

Results

Measuring fatigue in patients with MS

Concurrent validity

Patient characteristics Table 2.1 shows characteristics of the participants. Forty-three patients (mean age 48.7 years, median EDSS score 3.5) completed the three fatigue questionnaires. Of the 43 participants 13 (30%) patients were male. Participants had median scores of 52 on the FSS, 41 on the MFIS and 78.5 on the CIS20R. Age, gender, type of MS, duration of the disease, and EDSS

Table 2.1  Participants characteristics (N=43) Variable

Mean (SD)

Min–Max

Age (years) Gender; male/female Disease duration (years) Type MS; RR/SP/PP

48.7 (7.0) 13/30 14.3 (9.2) 26/10/7

38–64

Median (IQR)

Min–Max

3.5 52 (6) 41 (18) 21.5 (6) 17 (8) 4 (2.5) 78.5 (19) 32.5 (13) 20.5 (7) 13.5 (8) 12 (6)

1–6.5 15–63 1–74.5 1–32 0–35.5 0–8 31.5–121.5 9.5–56 5–31.5 4–25 3–20.5

Variable EDSS median FSS MFIS Physical subscale Cognitive subscale Psychosocial subscale CIS20R Subjective feeling Concentration Motivation Physical activity

2

2–51

MS, Multiple Sclerosis; RR, Relapse Remitting; SP, Secondary Progressive; PP, Primary Progressive; EDSS, Expanded Disability Status Scale; FSS, Fatigue Severity Scale; MFIS, Modified Fatigue Impact Scale; CIS20R, Checklist Individual Strength; IQR, Inter Quartile Range; SD, Standard Deviation; Min, minimum; Max, maximum.

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score were not significantly correlated with the FSS, MFIS, and CIS20R. All assessments were applied with a mean measurement interval of 7 days, according to the measurement protocol.

Test-retest reliability Table 2.2 shows the test-retest reliability of the FSS, MFIS and CIS20R for MS patients. Briefly, the ICCs for the FSS, MFIS and CIS20R were good (0.76, 0.85 and 0.81 respectively). ICCs for the MFIS domains were good, ranging from 0.73 to 0.88 and the ICCs for the CIS20R domains were also good, ranging from 0.77 to 0.84.

Agreement Figure 2.1 displays Bland and Altman plots for the total scores of the three fatigue questionnaires of the patients with MS. No systematic differences were observed between the first and second assessments of the various questionnaires.

Responsiveness Table 2.2 shows the SDCs and MDCs for the fatigue questionnaires in patients with MS. Responsiveness expressed by the SDC was 13.1 for the FSS, 16.2 for the MFIS and 24.8 for the CIS20R, resulting in a MDC of 20.7% for the FSS, 19.2% for the MFIS and 17.7% for the CIS20R, respectively.

Table 2.2  Test retest reliability and responsiveness

FSS MFIS Physical subscale Cognitive subscale Psychosocial subscale CIS20R Subjective feeling Concentration Motivation Physical activity

ICC (95% CI)

SDC

MDC (%)

0.76 (.60 to .86) 0.85 (.74 to .92) 0.73 (.55 to .84) 0.88 (.79 to .93) 0.81 (.68 to .89) 0.81 (.67 to .89) 0.84 (.72 to .91) 0.77 (.62 to .87) 0.81 (.67 to .89) 0.84 (.54 to .84)

13.1 16.2 8.9 8.0 2.3 24.8 11.8 9.7 6.6 6.9

20.8 19.3 24.7 20 28.8 17.7 21.1 27.7 23.6 32.9

FSS, Fatigue Severity Scale; MFIS, Modified Fatigue Impact Scale; CIS20R, Checklist Individual Strength; ICC, Intraclass Correlation Coefficients; SDC, Smallest Detectable Change; MDC%, Minimal Detectable Change; All ICCs p10s).

Design Six research assistants were trained to apply the AM (see details in the Activity Monitor section) before the study. The AM was applied and removed in the participants’ own home.

Activity monitoring in MS

periods longer than 5 seconds” (walking >5s), and “number of walking periods longer than

Twenty-four-hour monitoring was executed twice, with an interval of exactly 1 week, assuming that activity patterns remain approximately the same between similar weekdays. Research assistants visited the participants on day 1 (application AM) and 2 (pick up AM) and a week later on days 8 (application AM) and 9 (pick up AM). To avoid bias caused by diurnal fluctuations, all participants carried the AM for 24 hours. All participants were instructed to continue their usual daily activities performed in their own environment (i.e., home and community) and use their walking devices or orthoses as they would normally do, but they were asked to refrain from showering and swimming. Participants were told that the system measures body movement, but no elaborate explanation of the purpose of the study and the function of the AM was given until the end of the final measurement session.

Statistical analysis Reproducibility concerns the degree to which repeated measurements provide similar results.23 Reproducibility can be assessed in terms of reliability and agreement parameters.

4

Reliability Reliability was defined as how well the scores of the participants can be distinguished from each other with an AM application despite existing measurement errors.23 For all variables, the test-retest reliability was calculated by using a ICC 2-way random-effects model with an absolute agreement definition.24 For the present study, we defined an ICC greater than 0.70 as good reliability and an ICC between 0.40 and 0.70 as moderate reliability. An ICC below 0.40 was defined as poor reliability.

Agreement Agreement concerns the measurement error and assesses how close the scores on the AM application are for the 2 measurements.23 For this purpose, the Bland-Altman method

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was used for assessing agreement between the 2 measurements by calculating the mean difference (mean Δ) between the 2 consecutive measurements and the SD of this difference.25 The LoAs were calculated as the mean difference ± 1.96 times the SD of the differences. The Bland-Altman plot provides a visual interpretation of possible systematic variation in differences over the range of measurement and outliers that are not revealed by regular correlation analyses.

Measurement error The SDC was calculated as an indication of measurement error, by 1.96 x √2 x SEM (standard error of measurement). Changes larger than the SDC are considered to be real changes, i.e. changes beyond measurement error.26 The SEM was calculated by SD x √(1 – R), with R equal to ICC and SD equal to √(total variance).

Results Patient characteristics Forty-three patients with MS (mean age, 48.7y; median EDSS score, 3.5) were recruited from the outpatient clinic of the MS center of excellence at the VU University Medical Center. Fifty potential participants were contacted by telephone, of which 7 declined the invitation because of unavailability or unspecified reasons. No obvious differences were noted between participants and nonparticipants. Table 4.1 shows characteristics of the participants. Figure 4.1 displays the frequencies of EDSS scores of the participants.

Table 4.1  Participants characteristics (N=43) Variable Age (years) Gender: men; women Type MS: RR; SP; PP  Disease duration (years) Variable EDSS

Frequencies

Mean (SD)

Range

NA 13; 30 26; 10; 7 NA NA NA

48.7 (7.0) NA NA 14.3 (9.2) Median (IQR) 3.5 (2.5)

38–64 NA NA 2–51 Range 1–6.0

SD, standard deviation; EDSS, Expanded Disability Status Scale; RR, relapse remitting; SP, secondary progressive; PP, primary progressive; IQR, inter quartile range; NA, not applicable.

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  91 Activity monitoring in MS

6

5

Count

4

3

2

1

0 1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

5,00

5,50

6,00

EDSS score

Figure 4.1  Frequencies of Expanded Disability Status Scale (EDSS) scores.

None of the participants used a wheelchair. All monitoring data were normally distributed as determined by visual inspection. The mean monitoring duration was 23.6 hours (SD, 0.61). For all assessments, it took 10 to 15 minutes to apply the AM. The AM data of 2 MS patients (4.4% of total assessments) could not be used for analysis because of a malfunctioning sensor. One participant had an interval of 6 days because the first measurement had to be repeated as a result of system failure. Another participant had an interval of 14 days because

4

of a delayed second measurement as a result of unavailability of the research assistant due to illness. All other subjects had a measurement interval of 7 days according to the measurement protocol. In general, the AM was well tolerated by the subjects even though they could not shower or bathe for 24 hours. No problems were reported.

Reproducibility On average patients showed 2.1 hours of dynamic activity during the 24-hour registration, of which 1.2 hours was defined as walking activity (Table 4.2).

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Table 4.2  Reproducibility of measurement

Dynamic activity (hours) Transitions (N) Walking (hours) Walking periods >5 seconds (N) Walking periods >10 seconds (N) Static activity (hours) Sitting (hours) Standing (hours) Lying (hours)

Mean

SD

ICC

Meandif

SDdif

LoA (low/up)

SDC

2.09 116 1.20 222 133 21.49 7.97 3.48 9.70

0.83 48 0.73 118 75 1.01 2.48 1.01 2.51

0.72 0.74 0.77 0.80 0.76 0.71 0.67 0.62 0.55

0.01 -8 -0.01 -5 -1 -0.07 -0.15 -0.24 0.42

0.63 34 0.50 75 53 0.77 2.02 1.71 2.39

-1.23/1.24 -75/58 -0.99/0.98 -153/143 -104/102 -1.59/1.44 -4.10/3.81 -3.58/3.10 -4.27/5.11

1.23 66 0.99 148 103 1.52 3.95 3.34 4.68

Mean; pooled mean of the two assessments, SD; pooled standard deviation of the two assessments; ICC, intraclass correlation coefficients; Meandif, mean difference between the two assessments; SDdif, Standard deviation of the mean difference; LoA, limits of agreement; low, lower bound; up, upper bound; SDC, smallest detectable change; All ICCs p5s, walking >10s, number of sit-to-stand transitions).

Study limitations The present study has some limitations. First, only ambulatory MS patients were included (i.e., EDDS score below 6.5). This limits the generalization of the present findings to ambulatory

4

MS patients. Second, the application of the AM was restricted to the legs and the trunk, whereas a more elaborate AM setup with sensors on the arms would also allow one to measure wheelchair propulsion activity. Further studies are needed to include nonambulatory MS patients who are bound to a wheelchair. Third, the distribution-based method we have used to gain insight into the percentage change between the 2 assessments, which should be exceeded in order to exclude measurement error, is not necessarily informative as to the extent these data are clinically meaningful. To determine clinically meaningful information regarding the relationship between the observed change and its clinical importance, a patient’s or clinician’s perspective is needed. A distribution-based method informs about observed change in the sample, and anchor-based methods estimate minimal important change directly.23

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Finally, the dynamic activity of AM showed a relatively high SDC of 1.23 hours. Future studies investigating treatment effects could decrease the within-subject variability, and thus the measurement error, by using longer registration periods or more frequent assessments over predefined time intervals. In addition, further research is needed to compare and interpret the SDCs for the separate variables of walking and the sit-to-stand transitions.

Conclusions The present study shows that 24-hour ambulatory monitoring of motor activity is a feasible and reproducible method to measure physical activity in ambulatory patients with MS. The highest test-retest reliability was found for walking-related activities, whereas the lowest value was found for “lying”. The results confirm the practical utility of AM because our study design included 6 different research assistants who applied the AM and measurements took place in patients’ own home setting. These findings support the robustness and feasibility of the AM for use in clinical studies. However, future studies should confirm the validity of the AM for proper classification into the different activity categories, implementing new advances in hardware and software.

Acknowledgements We thank all participants and L.E.G. Wevers, PT, M.S. Blok, PT, T. Rikhof, PT, W. Abers, PT, V.M. Boelaart, PT, and J. Jansen, PT, for their assistance in data collection.

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1.

Rietberg MB, Brooks D, Uitdehaag BM, Kwakkel G. Exercise therapy for multiple sclerosis. Cochrane Database Syst Rev. 2005 Jan 25; (1): CD003980.

2.

White LJ, Dressendorfer RH. Exercise and multiple sclerosis. Sports Med. 2004; 34: 1077-1100.

3.

Morris ME, Vowels L, Dodd K. Changes in gait and fatigue from morning to afternoon in people with multiple sclerosis. J Neurol Neurosurg Psychiatry. 2002; 72: 361-65.

4.

Veitch J, Salmon J, Ball K. Children’s active free play in local neighborhoods: a behavioral mapping study. Health Educ Res. 2008 Oct; 23(5): 870-9.

5.

Patterson SM, Krantz DS, Montgomery LC, Deuster PA, Hedges SM, Nebel LE. Automated physical activity monitoring: validation and comparison with physiological and self-report measures. Psychophysiology. 1993 May; 30(3): 296-305.

6.

Washburn RA, Montoye HJ. The assessment of physical activity by questionnaire. Am J Epidemiol. 1986 Apr; 123(4): 563-76.

7.

Ghione S, Rosa C, Salerno L, Ceccanti G, Volterrani D. Assessment of physical and mental activity using a standardized computer-assisted diary during ambulatory blood pressure monitoring. J Hypertens Suppl. 1993 Dec; 11(5): S284-5.

8.

Melanson EL Jr, Freedson PS. Physical activity assessment: a review of methods. Crit Rev Food Sci Nutr. 1996 May; 36(5): 385-96. Review.

9.

Gosney JL, Scott JA, Snook EM, Motl RW. Physical activity and multiple sclerosis: validity of self-report and objective measures. Fam Community Health. 2007 Apr-Jun; 30(2): 144-50.

10.

Littner M, Kushida CA, Anderson WM, Bailey D, Berry RB, Davila DG, Hirshkowitz M, Kapen S, Kramer M, Loube D, Wise M, Johnson SF; Standards of Practice Committee of the American Academy of Sleep Medicine. Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep. 2003 May 1; 26(3): 337-41.

11.

Bussmann JB, Martens WL, Tulen JH, Schasfoort FC, van den Berg-Emons HJ, Stam HJ. Measuring daily behavior using ambulatory accelerometry: the Activity Monitor. Behav Res Methods Instrum Comput. 2001 Aug; 33(3): 349-56.

12.

Busse ME, Pearson OR, Van Deursen R, Wiles CM. Quantified measurement of activity provides insight into motor function and recovery in neurological disease. J Neurol Neurosurg Psychiatry. 2004 Jun; 75(6): 884-8.

13.

Motl WM, Mc Auley E, Snook M Scott JA. Accuracy of two electronic pedometers for measuring steps taken under controlled conditions among ambulatory individuals with multiple sclerosis. Mult Scler. 2005; 11: 343-5.

14.

Bussmann JB, Stam HJ. Techniques for measurement and assessment of mobility in rehabilitation: a theoretical approach. Clin Rehabil. 1998 Dec; 12(6): 455-64.

15.

White DK, Wagenaar RC, Ellis T. Monitoring activity in individuals with Parkinson disease: a validity study. J Neurol Phys Ther. 2006 Mar; 30(1): 12-21.

Chapter_4_Marc.indd 97

Activity monitoring in MS

References

4

29-7-2015 10:42:25

98

Chapter 4

16.

Hoff JI, van der Meer V, van Hilten JJ. Accuracy of objective ambulatory accelerometry in detecting motor complications in patients with Parkinson disease. Clin Neuropharmacol. 2004 Mar-Apr; 27(2): 53-7.

17.

Lim I, van Wegen E, Rochester L, Nieuwboer A, Willems A-M, Jones D, Baker K, Hetherington V, Kwakkel G. Does cueing training improve physical activity in patients with parkinson’s deasease? Neurorehabil Neural Repair. 2010 Jun; 24(5): 469-77.

18.

Bussmann HBJ, Reuvekamp PJ, Veltink PH, Martens WLJ, Stam HJ. Validity and reliability of measurements obtained with an “Activity monitor” in people with and without a transtibial amputation. Phys Ther. 1998; 78: 989-98.

19.

Van den Berg-Emons RJG, Bussmann JBJ, Balk AHMM, Stam H. Validity of ambulatory accelerometry to quantify physical activity in heart failure. Scand J Rehabil Med. 2000 Dec; 32(4): 187-92.

20.

Bussmann JBJ, Van de Laar YM, Neeleman MP, Stam HJ. Ambulatory accelerometry to quantify motor behaviour in patients after failed back surgery. Pain. 1998; 74: 153-61.

21.

McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, McFarland HF, Paty DW, Polman CH, Reingold SC, Sandberg-Wollheim M, Sibley W, Thompson A, van den Noort S, Weinshenker BY, Wolinsky JS. Recommended Diagnostic Criteria for Multiple Sclerosis: Guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Ann Neurol. 2001; 50: 121-7.

22.

Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983; 33: 1444-52.

23.

De Vet HCW, Terwee CB, Knol DL, Bouter LM. When to use agreement versus reliability measures. J Clin Epidemiol. 2006; 59: 1033-9.

24.

Rankin G, Stokes M. Reliability of assessment tools in rehabilitation: an illustration of appropriate statistical analyses. Clin Rehabil. 1998; 12(3): 187-99.

25.

Bland JM and Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476): 307-10.

26.

Roebroeck ME, Harlaar J, Lankhorst GJ. The application of generalizability theory to reliability assessment: an illustration using isometric force measurements. Phys Ther. 1993; 73: 386-95.

27.

Motl RW, Zhu W, Park Y, McAuley E, Scott JA, Snook EM. Reliability of scores from physical activity monitors in adults with multiple sclerosis. Adapt Phys Activ Q. 2007 Jul; 24(3): 245-53.

Suppliers a.

Step Watch, Oklahoma City Corporate Office: 840 Research Prkwy, Ste 200, Oklahoma City, OK 73104.

b.

TEMEC Instruments, Spekhofstraat 2, 6466 LZ Kerkrade, telephone 003145 5428888 Kerkrade, The Netherlands.

c.

Smith & Nephew, Postbus 525, 2130 AM Hoofddorp, The Netherlands.

d.

Microsoft B.V. Evert van de Beekstraat 354, 1118 CZ Schiphol, The Netherlands.

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5 Do patients with MS show different daily physical activity patterns from healthy individuals? M.B. Rietberg E.E.H. van Wegen B.J. Kollen G. Kwakkel

Neurorehabil Neural Repair. 2014 Feb 10; 28(6): 516-23.

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Abstract Background:  Reduced physical activity is an important consequence of Multiple Sclerosis (MS). However, little is known about the real quantity and type of daily activities that patients with MS perform in their own home environment. Objective:  To gain insight in differences in amount and pattern of physical activities performed over a 24-hour period in the own community environment of patients with MS and healthy individuals. Methods:  A total of 43 ambulatory patients with MS and 26 age- and gender-matched healthy individuals participated. Physical activity recorded with an ambulatory activity monitor was classified into postures and motions. Multilevel analyses were conducted to investigate whether the pattern of physical activities across daily periods (morning, afternoon, and evening) was dependent on the group (MS vs healthy individuals). Results:  Results showed a significant overall lower amount of dynamic activity as compared with a group of healthy controls (p5 seconds Walking >10 seconds Transitions Static activity Lying Sitting Standing

Beta

95% CI

p

-0.27 -0.18 -24.6 -18.1 -0.16 0.18 0.28 0.15 -0.17

-0.39, -0.15 -0.26, -0.10 -37.5, -11.7 -26.2, -9.94 -5.24, 4.92 0.05, 0.32 -0.19, 0.76 -0.18, 0.47 -0.33, -0.01

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