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Journal of Neurologic Physical Therapy:
doi: 10.1097/NPT.0b013e3182a18460
Research Articles

Utility of Disease-Specific Measures and Clinical Balance Tests in Prediction of Falls in Persons With Multiple Sclerosis

Dibble, Leland E. PT, PhD; Lopez-Lennon, Cielita DPT; Lake, Warren DPT; Hoffmeister, Carrie DPT; Gappmaier, Eduard PT, PhD

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Author Information

University of Utah Department of Physical Therapy, Salt Lake City.

Correspondence: Lee Dibble, PT, PhD, University of Utah Department of Physical Therapy, 520 Wakara Way, Salt Lake City, UT 84124 ( Lee.Dibble@hsc.utah.edu).

The data presented in this manuscript have been presented in 2 different abstracts at the Consortium of Multiple Sclerosis Centers Annual Conference in 2008 and 2009.

No funding was received to support this project.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site ( www.jnpt.org).

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Abstract

Background and Purpose:

The sensory and motor deficits associated with multiple sclerosis (MS) contribute to falls with the majority of persons with MS falling at least once annually. To appropriately direct treatment, accurate fall prediction measures are needed. In this study of community-dwelling individuals with MS followed for 12 months, we sought to determine frequency of falls, utility of clinical balance tests to predict falls, and accuracy of participants' retrospective recall of fall events.

Methods:

Independently ambulatory persons with MS underwent 5 clinical balance tests including Activities-specific Balance Confidence, Berg Balance Scale, Functional Reach, Timed Up and Go, and Dynamic Gait Index, and one disease-specific measure of disability (Expanded Disability Status Scale) and then were followed for 1 year. Participants were queried monthly by phone to determine the number of fall events that had occurred. Accuracy of fall prediction was determined by receiver operating characteristic curve analysis and comparison of balance test performance between fallers and nonfallers.

Results:

Sixty-one percent of the 38 participants were classified as fallers at 12-month follow-up. Only the Berg Balance Scale, Dynamic Gait Index, and the Activities-specific Balance Confidence demonstrated clinically useful levels of accuracy. In addition, participants were generally poor in their accurate recall of fall events relative to their monthly fall reports.

Discussion and Conclusions:

The majority of participants fell during a 1-year prospective follow-up. Only 2 balance performance measures and 1 balance confidence measure accurately distinguished between fallers and nonfallers as well as possessed clinically useful levels of sensitivity and specificity. These results also emphasized the inaccuracy of retrospective fall history in an MS sample.

Video Abstract available (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A55) for more insights from the author.

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INTRODUCTION

Multiple sclerosis (MS) is a progressive, neurodegenerative disease of the central nervous system that affects more than 400 000 persons in the United States and affects 2.1 million persons worldwide.1,2 While the neurologic phenotype of MS is variable, the effects of MS on neurologic function consistently contribute to increasing disability over time.3,4 In particular, sensory and motor deficits may interact to increase fall risk and the frequency of falls relative to age-matched neurologically healthy individuals.5–7

Evidence for high fall rates in persons with MS has begun to accumulate. In samples of persons with MS of varied severity, studies have reported that greater than 50% of persons with MS fall greater than 2 times annually.8–11 Although the data are sparse, fall-related injuries appear to be common in persons with MS, with fractures being of particular concern.12,13 Given the adverse health and quality-of-life consequences of falls, researchers have begun to examine clinical balance tools that may accurately predict fall events in persons with MS.9,14 Unfortunately, few studies have utilized prospective fall data and the follow-up period has generally been less than 6 months.10,14,15

In this study of community-dwelling individuals with MS followed for 12 months, we sought to determine the frequency of falls, the utility of clinical balance tests to predict falls, and the accuracy of participants' retrospective recall of fall events. Our primary objective was to determine which demographic and disease-specific characteristics differed between fallers and nonfallers. We hypothesized that the majority of individuals in our sample would fall during the subsequent year. In addition, we hypothesized that those persons who fell would have greater neurologic deficits as measured by the Functional Status Score (FSS) than those who did not fall. Because of its limited consideration of postural control contributors to disability, we hypothesized that the fallers and nonfallers would not differ in overall Expanded Disability Status Scale (EDSS) scores. Our secondary objectives were to (1) compare the validity of common clinical balance tests in determining prospective fall risk and (2) compare the accuracy of monthly recall versus annual recall of fall events. On the basis of previous research, we hypothesized that all of the studied clinical tests would have greater fall prediction ability than random assignment to a fall category and that no one test would be superior to another.16–18 Finally, we hypothesized that persons with MS would be inaccurate in their annual recall of fall events relative to their monthly fall reports.

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METHODS

Participants

Approval was received from the Health Sciences Institutional Review Board of University of Utah prior to implementation of the study. An accessible sample of 49 individuals with MS who were referred to outpatient neurology clinics were recruited for the study. The following inclusion criteria were used to determine eligibility: a medically confirmed diagnosis of definite MS, independent ambulation 25 m without rest (with or without an assistive device), willingness and ability to participate in clinical balance examinations, and willingness to accurately report fall characteristics (incidence and situation in which the falls occurred, and consequences of the fall or falls) in a monthly phone interview for 12 months. Potential participants were excluded if they were not ambulatory, had other medical conditions (orthopedic, neurologic, and cognitive) that prevented their participation in the procedures of the study, or refused to be contacted for phone call follow-up regarding fall events.

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Outcomes Measures

The EDSS was used because it is the most common clinical test used by referring medical providers as an indicator of disability in persons with MS. In the context of the EDSS examination, disability is operationally defined as the presence and severity of neurologic deficits.19 The EDSS has been found to be a valid and reliable indicator of disability.20 The EDSS rates disability on a score ranging from 0 to 10 with individuals who score at or less than 4.5 considered to be fully ambulatory without aid. Persons with MS who score greater than 4.5 but less than 8.0 are considered to be ambulatory with limitations, while individuals who score more than 8.0 are considered nonambulatory.

The Activities-specific Balance Confidence (ABC) was utilized to assess participants' confidence not to fall during balance challenges. The ABC is a standardized self-report measure that asks participants to rate their balance confidence when completing specific daily life balance challenges.21 Higher percentages (closer to 100%) indicate greater balance confidence. Among subjects with MS, the reliability of the ABC Scale has been shown to be high (interrater reliability interclass correlation coefficient [ICC] = 0.96, test-retest reliability ICC = 0.92).22 In addition, previous research has reported that the ABC was able to show differences between fallers and nonfallers in persons with MS.23,24

The Berg Balance Scale (BBS) was used to examine participants' performance on static and dynamic balance tasks.25 The scale rates participants' performance on 14 different balance tasks and rates performance on each task on a 5-point ordinal scale (0 = unable to perform; 4 = able to perform without difficulty). The scores for each item are summed with a range of possible scores from 0 to 56 (higher scores indicative of more stable performance on each of the balance tasks). The reliability of the BBS has also been demonstrated in persons with MS with high test-retest (ICC = 0.96) and interrater reliability (ICC = 0.96) in persons with MS.22,23

The Functional Reach was used as a test of anticipatory balance control performed while maintaining a static standing position. This test was performed following previously published guidelines.26 Participants performed 2 reach trials and the mean of distance reached in centimeters was used as the score. High test-retest reliability has been reported in persons with MS (ICC = 0.89).27

The Dynamic Gait Index (DGI) was utilized to assess gait-related mobility and dynamic balance.28,29 The DGI has 8 gait-related items that are each scored 0 (poor or unable to perform) to 3 (performed well). The maximum score attainable is 24; lower scores indicate a decrease in balance ability. Test-retest reliability was reported to be between 0.76 and 0.99 and interrater reliability was 0.98, using a study population of ambulatory subjects with MS.30

The Timed Up and Go (TUG) Test was used as a composite measure of mobility and dynamic balance in which participants are asked to stand up from a seated position, walk out 3 m, turn around and walk back to their starting point, and return to the seated position. Participants were timed and their score was the time taken to perform the task. Each participant performed 3 trials of the TUG Test according to previously published guidelines.31 The mean of the scores was calculated and used as the dependent variable. Previous research has demonstrated the sensitivity and specificity of the TUG Test in determining fall risk in community-dwelling older adults.32 High test-retest reliability has been demonstrated in persons with MS (ICC = 0.91).33

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Procedures

Each participant signed a consent form that had been approved by the institutional review board. Age, duration of disease, and fall history were gathered via personal interview. Data regarding participants' EDSS and FSS were established by their medical providers and acquired from their medical records. For this study, a fall was operationally defined as an event in which the individual unintentionally came to rest on the ground or a lower surface, not as the result of a major intrinsic event (such as a stroke or heart attack) or an overwhelming extrinsic event (such as ice). This definition of a fall is consistent with previous studies of clinical balance measures.34,35

Immediately following completion of the self-report items and clinical interview, each individual underwent the following clinical balance tests (in order): BBS, FRT, DGI, TUG Test. Participants were tested wearing their normal shoes. The assessment was carried out in one session. Rest breaks were given when requested by the participants, both within and between measures, with approximately 5 minutes given between each test. All subjects were informed that they could request rest breaks. The overall examination took 1 to 1.5 hours.

After completion of the clinical balance tests, participants were reminded of the fall definition and that they should record their fall events in a fall diary. Their contact information was confirmed prior to completion of the testing session. Participants were contacted monthly, via the telephone, for 12 consecutive months. One evaluator conducted all the monthly phone interviews utilizing a standardized series of questions. During the first week of a given month, participants were contacted and asked, “How many times did you fall to the ground this month?” Answers were given in whole numbers (0, 1, 2,...). If a particular fall was questionable to a participant, the definition of a fall would be repeated to the participant, and the participant would again be asked to report the number of falls for that given month. Participants unavailable for contact were recalled one additional time during the same month. If it was not possible to contact a participant during the given month after 2 failed attempts, then they would be asked to report the last 2 consecutive months' fall history during the next month's report. After completing the 12th and final telephone interview, participants were asked to retrospectively estimate their total number of falls for the last 12-month period.

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Statistical Analysis

Descriptive statistics were calculated for demographic variables. To examine our primary objective, we calculated the percentage of the overall sample that met our operational definition as a faller (2 or more falls in the year). Consistent with previous studies that have classified fallers and nonfallers,34,35 those persons who had experienced more than 2 falls during their prospective follow-up period were classified as fallers at 2 or more falls while those persons who reported less than 2 falls were classified as nonfallers. Descriptive statistics (means, standard deviations, and 95% confidence intervals) for age, body mass index (BMI), and time since diagnosis were calculated and the interval estimators of the demographic variables were compared.

Because of concerns for the normality of data distributions and the level of measurement, between-groups comparisons of fallers and nonfallers for our primary and secondary outcomes were conducted using separate nonparametric tests for differences on all outcome measures (Mann-Whitney U tests). To complete analysis of our secondary objective, based on prospective fall occurrence, we generated receiver operating characteristic (ROC) curves for each of the clinical balance tests and the EDSS. Separate ROC curves were generated by plotting the sensitivity (true positive rate) along the y-axis and 1 − specificity (false positive rate) on the x-axis for all values of each clinical balance measure. The sensitivity and specificity values were calculated using the participant's prospective fall history as the gold standard for diagnosis as a faller or nonfaller. Tests that have ROC curves closer to the upper left corner of the ROC plot maximize the signal-to-noise ratio and may have more clinical utility.36 Finally, the area under the curve (AUC) for each ROC curve and the confidence intervals about the AUC were determined as described previously.37,38 The AUC was interpreted as the probability of correctly identifying the participant with MS who was a faller from a randomly selected pair of participants and ranged from 0.50 (no identification ability, equivalent to random assignment) to 1.00 (perfect identification ability).

Finally, to examine our secondary objective regarding the accuracy of fall recall, we sought to determine the accuracy of 6- and 12-month retrospective recalls relative to our monthly follow-ups. Given the need for only 4 weeks of recall between phone calls, we considered the monthly reports provided by participants to be the number of actual falls. To determine the accuracy of 6- and 12-month retrospective recall, we calculated the ratio of the sum of the actual falls to the estimates provided at the 6- and 12-month follow-ups. Ratios greater than 1 meant that participants overestimated their actual falls, while ratios less than 1 meant that participants underestimated their actual falls. This analysis included all participants as we were interested in the accuracy of recall, regardless of fall group assignment.

For all variables, we calculated mean and standard deviation as well as 95% confidence intervals. The a priori level of significance was set at P < 0.05 and the Statistical Package for the Social Sciences (SPSS version 19.0 for Macintosh IBM Corporation, Armonk, NY) was used for all calculations and comparisons.

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RESULTS

From the accessible sample of 49 participants, 11 total participants were excluded; 9 of the 11 individuals were unwilling to accept phone calls, while 1 was excluded for personal reasons and 1 due to unrelated trauma. Therefore, 38 participants (mean age: 53.8 ± 10.6 years; 35 women and 13 men) underwent the initial testing and remained in the study for the 12-month prospective monitoring period. Based on their reports of falls during the prospective monitoring period, 23 of the 38 participants (61%) were classified as fallers while the remaining 15 participants were classified as nonfallers (Table 1). The 25 participants classified as fallers reported a total of 343 falls over the consecutive 12-month prospective monitoring period.

Table 1.
Table 1.
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Of the clinical balance tests studied, the ABC, the BBS, and the DGI were found to be significantly different between the faller and nonfaller groups while the Functional Reach, the EDSS, and TUG did not discriminate between these 2 groups. Scores on the BBS for both groups were higher than the fall risk cutoff score of 45 reported in the literature,39 while the scores on the DGI for both groups were lower than the fall risk cutoff score of 19 reported in the literature.29 Scores for the ABC in the faller group were less than the cutoff of 67%, suggested by Lajoie and Gallagher24 (Table 2).

Table 2.
Table 2.
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In terms of relative accuracy of predicting falls, the EDSS and all of the clinical balance tests had AUC values greater than those associated with random assignment (50%); however, the EDSS possessed the lowest AUC (0.55). While the BBS had the highest AUC point estimator (0.71), the interval estimators for the AUCs of all of the tests substantially overlapped (Table 2).

For comparisons of actual falls with recalled falls, data were available only for 30 of the 38 participants. At the 6-month follow-up, 5 of the 30 participants (17%) accurately recalled their actual number of falls, 6 of 30 (20%) overestimated, while the remaining 63% of the sample underestimated their actual falls. At the 12-month follow-up, 7 of the 30 participants (23%) accurately recalled their actual number of falls, 10 of 30 (33%) overestimated, while the remaining 47% of the sample underestimated their actual falls (Figure 1).

Figure 1.
Figure 1.
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DISCUSSION

In this study of 38 participants with MS followed for 12 months, we sought to determine the frequency of falls, the utility of clinical balance tests to predict falls, and the accuracy of participants' retrospective recall of fall events. As hypothesized and in agreement with recent reports of fall frequency in persons with MS,7,11,18,40 the majority of individuals in our sample (61%) fell more than twice during the prospective monitoring period. Three of the clinical balance tests used (ABC, BBS, and DGI) were significantly different between fallers and nonfallers and also had predictive accuracy measures (AUC) that exceeded a random guess. Finally, participants in our sample were generally inaccurate in their long-term recall of fall events relative to their monthly fall reports.

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EDSS and the Accumulation of Neurologic Signs May Have Limited Utility for Fall Prediction

Our a priori hypothesis was that, because of its cursory consideration of postural control factors, the EDSS as a measure of disability would not be different between faller and nonfaller groups and have limited accuracy in terms of fall prediction. As hypothesized, there was no significant difference found in EDSS scores between the faller and nonfaller groups. In addition, the accuracy of the EDSS in terms of fall prediction (AUC = 0.57) was only slightly higher than that associated with random assignment. Such a finding suggests that the disability and the presence and severity of neurologic deficits as measured by the EDSS cannot be relied upon to provide accurate predictions of fall events.

In contrast to our expectations regarding the EDSS, our a priori hypothesis was that the FSS of the EDSS would differ between faller and nonfaller groups. We based this hypothesis on the observation that the inclusion of weakness, somatosensation, visual, and cognitive items would appear to be important in postural performance. The lack of significant differences between the fallers and nonfallers on FSS summed scores led us to examine the FSS more closely. In-depth examination of individual FSS values revealed that the majority of participants' scores represented minimal deficits in most systems. The scoring of individual FSS items within the minimally impaired range (0–2) does not appear to be able to document specific body and structure function deficits relevant to falls and fall risks. For example, the 0–2 score items of Brainstem Functions category asks the examiner to rate the presence or absence of nystagmus or extraocular weakness. It does not specify the examination of vestibuloocular or vestibulospinal function during dynamic head or body movements that may be relevant to gaze stability or balance function.41,42

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Predictive Validity of Common Clinical Balance Tests

In contrast to the Body Structure and Function focus of the EDSS and the FSS, clinical balance tests examine the activity domain of the World Health Organization's International Classification of Function, Disability, and Health.43 Because they examine posturally challenging tasks that may be performed in daily life, these tests may be more relevant to clinicians examining fall risk in persons with MS. However, to have evidence for their utility, balance tests should demonstrate predictive validity. For the purposes of this study, we considered a test to have appropriate predictive validity only if it was found to be significantly different between faller and nonfaller groups and if it had an AUC for ROC analysis that exceeded random assignment. Only the BBS, the DGI, and the ABC meet these criteria. Such findings agree with the previous research18,23 and provide additional support for the potential use of the BBS, the DGI, and the ABC in persons with MS.

Our ROC analysis methods considered the predictive validity of each clinical balance test in isolation. However, the AUC values reported here for individual tests emphasize that none of the tests demonstrated sufficiently high levels of fall prediction accuracy (no tests had AUC > 0.75) to warrant their use in isolation. In our opinion, the heterogeneity of MS neurologic signs and symptoms precludes the ability of one test to accurately capture all persons with MS that will fall. The heterogeneous neurologic deficits present in MS suggest that the composite measures or collective interpretation of multiple clinical balance tests may overcome the shortcomings of any individual clinical balance test.9,44

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THE LIMITATIONS OF RETROSPECTIVE RECALL OF FALL EVENTS

Although retrospective fall reporting is a commonly used practice in the falls literature, our data found that it was profoundly inaccurate. While fall diaries may improve accuracy,34,35 data from persons with PD suggest that the longer the recall time required, the worse the accuracy may be.45 In our sample, the majority of participants with MS were inaccurate at both 6- and 12-month retrospective recall. Such findings suggest that more accurate methods than delayed recall must be implemented if fall events are to be considered as an outcome measure in fall-prevention trials in persons with MS.46

In this study, we assumed that the monthly phone calls provided an accurate count of actual falls. However, the frequency of exposure to postural challenges and monitoring of instability episodes that do not result in a fall are critical missing pieces of the accurate fall prediction puzzle. In future studies, an emphasis should be placed on community-based monitoring of ambulatory activity, stumbles, and near-fall events. This will allow validation of the patient's recall ability.

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LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH

Although a range of balance and disease severity is represented in the data, these volunteers were pooled from tertiary care medical center–based neurology and rehabilitation clinics. As such, the participant group may have been biased toward higher physical and cognitive functioning and self-motivation. We did not provide standard rest breaks for all participants and this may have affected the results. Future studies should use larger samples from broader accessible populations and may consider proposing cutoff scores for the balance tests utilized here. In addition to the clinical balance tests supported by our data, a variety of other factors are relevant and must be captured by other components of the patient examination. Previous research has highlighted the relevance of age, previous fall history, assistive device use, spasticity, and lack of proprioception as factors related to falls and fall risk in persons with MS.7,11,17,45,47 Additional factors that may influence balance function and were not examined in this study were types of medications, relapses, fatigue, and the presence of comorbidities. These factors should be included in future studies.

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CONCLUSION

The majority of persons with MS in this sample fell more than twice during 12-month follow-up period. Of the clinical balance tests examined, only the BBS, the DGI, and the ABC accurately distinguished between fallers and nonfallers, as well as possessed clinically useful levels of sensitivity and specificity. These results also emphasized the inaccuracy of retrospective fall history reporting in a MS sample. Such data provide important preliminary guidance for the use of these measures in the clinic and during controlled trials of the efficacy of fall risk-reduction and fall-prevention interventions in persons with MS.

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REFERENCES

1. Hassan-Smith G, Douglas MR. Epidemiology and diagnosis of multiple sclerosis. Br J Hosp Med (Lond). 2011; 72:(10):M146–M151.

2. Zindler E, Zipp F. Neuronal injury in chronic CNS inflammation. Best Pract Res Clin Anaesthesiol. 2010; 24:(4):551–562.

3. Ciccarelli O, Brex PA, Thompson AJ, Miller DH. Disability and lesion load in MS: a reassessment with MS functional composite score and 3D fast FLAIR. J Neurol. 2002; 249:(1):18–24.

4. Brex PA, Ciccarelli O, O'Riordan JI, Sailer M, Thompson AJ, Miller DH. A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N Engl J Med. 2002; 346:(3):158–164.

5. Zeigelboim BS, Arruda WO, Mangabeira-Albernaz PL, et al. Vestibular findings in relapsing, remitting multiple sclerosis: a study of thirty patients. Int Tinnitus J. 2008; 14:(2):139–145.

6. Prosperini L, Kouleridou A, Petsas N, et al. The relationship between infratentorial lesions, balance deficit and accidental falls in multiple sclerosis. J Neurol Sci. 2011; 304:(1/2):55–60.

7. Sosnoff JJ, Socie MJ, Boes MK, et al. Mobility, balance and falls in persons with multiple sclerosis. PLoS One. 2011; 6:(11):e28021

8. Matsuda PN, Shumway-Cook A, Ciol MA, Bombardier CH, Kartin DA. Understanding falls in multiple sclerosis: association of mobility status, concerns about falling, and accumulated impairments. Phys Ther. 2012; 92:(3):407–415.

9. Cattaneo D, De Nuzzo C, Fascia T, Macalli M, Pisoni I, Cardini R. Risks of falls in subjects with multiple sclerosis. Arch Phys Med Rehabil. 2002; 83:(6):864–867.

10. Peterson EW, Cho CC, von Koch L, Finlayson ML. Injurious falls among middle aged and older adults with multiple sclerosis. Arch Phys Med Rehabil. 2008; 89:(6):1031–1037.

11. Fjeldstad C, Pardo G, Bemben D, Bemben M. Decreased postural balance in multiple sclerosis patients with low disability. Int J Rehabil Res. 2011; 34:(1):53–58.

12. Tremlett H, Lucas R. The risks for falls and fractures in multiple sclerosis. Neurology. 2012; 78:(24):1902–1903.

13. Moen SM, Celius EG, Nordsletten L, Holmoy T. Fractures and falls in patients with newly diagnosed clinically isolated syndrome and multiple sclerosis. Acta Neurol Scand Suppl. 2011; (191):79–82.

14. Finlayson ML, Peterson EW, Cho CC. Risk factors for falling among people aged 45 to 90 years with multiple sclerosis. Arch Phys Med Rehabil. 2006; 87:(9):1274–1279; quiz 1287.

15. Nilsagard Y, Lundholm C, Denison E, Gunnarsson LG. Predicting accidental falls in people with multiple sclerosis—a longitudinal study. Clin Rehabil. 2009; 23:(3):259–269.

16. Foreman KB, Addison O, Kim HS, Dibble LE. Testing balance and fall risk in persons with Parkinson disease, an argument for ecologically valid testing. Parkinsonism Relat Disord. 2011; 17:(3):166–171.

17. Dibble LE, Christensen J, Ballard DJ, Foreman KB. Diagnosis of fall risk in Parkinson disease: an analysis of individual and collective clinical balance test interpretation. Phys Ther. 2008; 88:(3):323–332.

18. Landers MR, Backlund A, Davenport J, Fortune J, Schuerman S, Altenburger P. Postural instability in idiopathic Parkinson's disease: discriminating fallers from nonfallers based on standardized clinical measures. J Neurol Phys Ther. 2008; 32:(2):56–61.

19. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology. 1983; 33:(11):1444–1452.

20. Goodkin DE, Cookfair D, Wende K, et al. Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS). Multiple Sclerosis Collaborative Research Group. Neurology. 1992; 42:(4):859–863.

21. Powell LE, Myers AM. The Activities-specific Balance Confidence (ABC) Scale. J Gerontol A Biol Sci Med Sci. 1995; 50A:(1):M28–M34.

22. Cattaneo D, Jonsdottir J, Repetti S. Reliability of four scales on balance disorders in persons with multiple sclerosis. Disabil Rehabil. 2007; 29:(24):1920–1925.

23. Cattaneo D, Regola A, Meotti M. Validity of six balance disorders scales in persons with multiple sclerosis. Disabil Rehabil. 2006; 28:(12):789–795.

24. Lajoie Y, Gallagher SP. Predicting falls within the elderly community: comparison of postural sway, reaction time, the Berg Balance Scale and the Activities-specific Balance Confidence (ABC) scale for comparing fallers and non-fallers. Arch Gerontol Geriatr. 2004; 38:(1):11–26.

25. Berg KO, Wood-Dauphinee SL, Williams JI, Gayton D. Measuring balance in the elderly: Preliminary development of an instrument. Physiother Can. 1989; 41:304–311.

26. Duncan PW, Weiner DK, Chandler J, Studenski S. Functional Reach: a new clinical measure of balance. J Gerontol. 1990; 45:(6):M192–M197.

27. Frzovic D, Morris ME, Vowels L. Clinical tests of standing balance: performance of persons with multiple sclerosis. Arch Phys Med Rehabil. 2000; 81:(2):215–221.

28. Whitney SL, Marchetti GF, Schade A, Wrisley DM. The sensitivity and specificity of the Timed “Up & Go” and the Dynamic Gait Index for self-reported falls in persons with vestibular disorders. J Vestib Res. 2004; 14:(5):397–409.

29. Whitney S, Wrisley D, Furman J. Concurrent validity of the Berg Balance Scale and the Dynamic Gait Index in people with vestibular dysfunction. Physiother Res Int. 2003; 8:(4):178–186.

30. McConvey J, Bennett SE. Reliability of the Dynamic Gait Index in individuals with multiple sclerosis. Arch Phys Med Rehabil. 2005; 86:(1):130–133.

31. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991; 39:(2):142–148.

32. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther. 2000; 80:(9):896–903.

33. Nilsagard Y, Lundholm C, Gunnarsson LG, Dcnison E. Clinical relevance using timed walk tests and ‘Timed Up and Go’ testing in persons with multiple sclerosis. Physiother Res Int. 2007; 12:(2):105–114.

34. Hauer K, Lamb SE, Jorstad EC, Todd C, Becker C. Systematic review of definitions and methods of measuring falls in randomised controlled fall prevention trials. Age Ageing. 2006; 35:(1):5–10.

35. Lamb SE, Jorstad-Stein EC, Hauer K, Becker C. Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc. 2005; 53:(9):1618–1622.

36. Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007; 96:(5):644–647.

37. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143:(1):29–36.

38. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating graph. J Math Psychol. 1975; 12:387–415.

39. Shumway-Cook A, Baldwin M, Polissar NL, Gruber W. Predicting the probability for falls in community-dwelling older adults. Phys Ther. 1997; 77:(8):812–819.

40. Matsuda PN, Shumway-Cook A, Bamer AM, Johnson SL, Amtmann D, Kraft GH. Falls in multiple sclerosis. PM R. 2011; 3:(7):624–632.

41. Colebatch JG. Vestibular evoked myogenic potentials in multiple sclerosis. Clin Neurophysiol. 2012;. Clinical Neurophysiology 123:( 2012) 1693–1694.

42. Gazioglu S, Boz C. Ocular and cervical vestibular evoked myogenic potentials in multiple sclerosis patients. Clin Neurophysiol. 2012. 2012 Sep; 123:(9):1872–9.

43. World Health Organization. International Classification of Functioning, Disability and Health: ICF. Geneva, Switzerland: World Health Organization; 2001; .

44. Jacobs JV, Horak FB, Tran VK, Nutt JG. Multiple balance tests improve the assessment of postural stability in subjects with Parkinson's disease. J Neurol Neurosurg Psychiatry. 2006; 77:(3):322–326.

45. Duncan RP, Leddy AL, Cavanaugh JT, et al. Accuracy of fall prediction in Parkinson disease: six-month and 12-month prospective analyses. Parkinsons Dis. 2012; 2012:237673

46. Cameron MH, Lord S. Postural control in multiple sclerosis: implications for fall prevention. Curr Neurol Neurosci Rep. 2010; 10:(5):407–412.

47. Nilsagard Y, Denison E, Gunnarsson LG, Bostrom K. Factors perceived as being related to accidental falls by persons with multiple sclerosis. Disabil Rehabil. 2009; 31:(16):1301–1310.

Keywords:

accuracy; balance; falls; multiple sclerosis

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