Neuropsychological, Balance, and Mobility Risk Factors for Falls in People With Multiple : A Prospective Cohort Study
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Published Online: October 07, 2013
Abstract
Objectives
To determine whether impaired performance in a range of vision, proprioception, neuropsychological, balance, and mobility tests and pain and fatigue are associated with falls in people with multiple sclerosis (PwMS).
Design
Prospective cohort study with 6-month follow-up.
Setting
A multiple sclerosis (MS) physiotherapy clinic.
Participants
Community-dwelling people (N=210; age range, 21–74y) with MS (Disease Steps 0–5).
Interventions
Not applicable.
Main Outcome Measures
Incidence of falls during 6 months' follow-up.
Results
In the 6-month follow-up period, 83 participants (39.7%) experienced no falls, 57 (27.3%) fell once or twice, and 69 (33.0%) fell 3 or more times. Frequent falling (≥3) was associated with increased postural sway (eyes open and closed), poor leaning balance (as assessed with the coordinated stability task), slow choice stepping reaction time, reduced walking speed, reduced executive functioning (as assessed with the difference between Trail Making Test Part B and Trail Making Test Part A), reduced fine motor control (performance on the 9-Hole Peg Test [9-HPT]), and reported leg pain. Increased sway with the eyes closed, poor coordinated stability, and reduced performance in the 9-HPT were identified as variables that significantly and independently discriminated between frequent fallers and nonfrequent fallers (model χ23=30.1, P<.001). The area under the receiver operating characteristic curve for this model was .712 (95% confidence interval, .638–.785).
Conclusions
The study reveals important balance, coordination, and cognitive determinants of falls in PwMS. These should assist the development of effective strategies for prevention of falls in this high-risk group.
Keywords:
Accidental falls, Multiple sclerosis, Neuropsychological tests, Postural balance, Rehabilitation, Risk factorsList of abbreviations:
CI (confidence interval), MS (multiple sclerosis), 9-HPT (9-Hole Peg Test), PPA (Physiological Profile Assessment), PwMS(people with multiple sclerosis), ROC (receiver operating characteristic), TMT (Trail Making Test), TMT-A (Trail Making Test Part A), TMT-B (Trail Making Test Part B), TMT B-A (difference between TMT-B and TMT-A)
Multiple sclerosis (MS) is a chronic, lifelong, progressive neurologic disease with onset usually in early adulthood. MS lesions can influence strength, sensation, cognition, vision, and coordination, all of which may contribute to balance and fall risk.1, 2 People with MS (PwMS) are known to be at high risk for falls, as exemplified by epidemiologic studies that report that approximately 50% to 60% of PwMS fall 1 or more times over 3 to 12 months.3, 4, 5, 6, 7, 8, 9, 10
Several studies have assessed fall risk in PwMS. Identified risk factors for falls to date include advanced disease status,7,8, 9 balance or mobility impairments,3, 4, 5, 9, 11, 12 impaired forward limits of stability,8 visually dependent sway,8 use of walking aids,3, 5, 12 weakness in the lower limbs,5 fear of falling,4, 6 poor concentration or forgetfulness, urinary incontinence,4 and fatigue.10 Taken together, studies have highlighted the multifactorial nature of falls in this patient population. However, most studies were conducted in small samples (n<100) with falls as an outcome measure,7, 8, 11 and most relied on retrospective data on falls reported by participants.3, 4, 5, 6 Of those with prospective designs, the follow-up of participants has been short (3mo).7, 8, 9, 11
Previous studies have also used only a limited range of tests to assess potential risk factors for falls. We hypothesized that fall risk factors in MS are multifaceted. Therefore, concurrent examination of a broad set of neuropsychological, physical, and functional mobility measures is necessary to identify key explanatory and modifiable risk factors and to assess the relative importance of each factor in predisposing PwMS to falls. With this hypothesis, we aimed to extend previous work by administering a multidomain risk factor assessment to a sample of more than 200 community-living PwMS, and to determine prospectively the fall outcomes over 6 months. Such findings may elucidate why PwMS fall, and assist in the development of fall prevention strategies.
Methods
Design
This was a prospective cohort study with 6-month follow-up.
Participants
A total of 210 community-dwelling PwMS, aged 21 to 74 years, were recruited from patients who were referred for a physiotherapy assessment at an MS clinic site in Sydney, Australia. Participants were included if they were older than 18 years, had received a diagnosis of MS (any type), and were able to stand unsupported for 30 seconds and walk 10m with or without an aid (ie, at Disease Steps 0–5; appendix 113). The exclusion criterion was an inability to understand instructions relating to the physical assessment because of impaired cognitive function or insufficient English.
The study was approved by the Human Research Ethics Committee, University of New South Wales. Participation was voluntary, and informed consent was obtained from all participants before assessment.
Assessments
The test measures used in this study were selected to encompass the major sensorimotor, balance, and neuropsychological factors required for safe mobility. All were functional in nature, designed to assess impairments in performance. The tests were administered in 1 session lasting approximately 1 hour.
Sensorimotor function
Visual contrast sensitivity was assessed using the Melbourne Edge Test.14 Proprioception was measured with participants sitting using a lower limb–matching task.14 Errors were recorded using a protractor inscribed on a vertical clear acrylic sheet (60×60×1cm) placed between the legs. Maximal isometric quadriceps strength was measured in both legs while participants were seated on a high chair (so that feet did not touch the floor) with the hips and knees flexed to 90°. A strain gauge was fixed horizontally with straps on the lower shin, after which the participant was given a total of 3 attempts for each leg to push against the strap as forcefully as possible.14
Speed and coordination
Reaction time was assessed with a light as the stimulus and a finger press as the response.14 Participants had 10 practice trials and 10 experimental trials, with reaction time recorded in milliseconds. Fine motor control was examined with the timed 9-Hole Peg Test (9-HPT),15 in which seated participants pick up 9 pegs from a shallow container one at time as quickly as possible, place them in 9 holes, and then remove them again as quickly as possible one at a time, replacing them in the container. One practice trial with the dominant hand is followed by 1 timed trial each with the dominant and then the nondominant hand (with the average time taken as the test measure).
Standing and leaning balance
Postural sway (displacements of the body at the level of the waist) was assessed using a swaymeter with demonstrated high external validity and reliability.14, 16, 17 Testing was performed with participants standing on the floor and on a medium-density foam rubber mat (65×65×15cm thick) with eyes open and closed. Controlled leaning balance was measured using 2 tests with demonstrated validity and reliability: the maximal balance range and coordinated stability tests.17 In these tests, the swaymeter was attached anteriorly to the participant. In the maximal balance range test, participants were required to lean as far forward and as far back as possible without moving the feet or bending at the hips. Participants had 3 attempts at the test, with the highest anterior-posterior range taken as the test result. The coordinated stability test required each participant to adjust balance by leaning or rotating the body without moving the feet, so that the pen followed and remained within the borders of a 1.5-cm-wide convoluted track. A total error score was calculated by summing the number of occasions that the pen failed to stay within the path; 5 points were accrued for a cut corner and 1 for a crossed side. Participants performed 1 practice trial before the experimental trial.
Stepping and mobility
Stepping was assessed with the choice stepping reaction time test.18 For this test, participants stood on a nonslip black mat (0.8×1.2m) marked with 4 rectangular panels (32×13cm), 1 in front of each foot and 1 to the side of each foot. Participants were instructed to step onto specific rectangle panels in sequence as quickly as possible, using the left foot only for the 2 left panels (front and side) and the right foot only for the 2 right panels. Walking speed over 10m was assessed with and without a secondary cognitive task. To allow for acceleration and deceleration, 2m was provided at either end of a 10-m marked course. The secondary cognitive task was counting backward by threes starting at 100.
Neuropsychological assessment
Cognitive processing was assessed using the Trail Making Test (TMT),19 including Part A (TMT-A) that tests simple attention and Part B (TMT-B) that tests complex attention. In TMT-A, participants were asked to draw lines connecting mixed numbered circles in numerical order. TMT-B included a similar task, but the circles contained numbers and letters (eg, 1-A-2-B). Total time to complete each test was recorded. The difference between parts A and B (TMT B-A) was calculated to remove the motor speed element from the test evaluation, leaving an estimate of executive function.19
Pain and fatigue
Pain was assessed with a question asking whether participants currently had leg pain. Self-reported fatigue was assessed with the item from the Fatigue Severity Scale20 that asked whether fatigue affects physical performance. Participants who quite or very strongly agreed with the above statement were classified as having limiting fatigue.
Physiological Profile Assessment fall risk score
The Physiological Profile Assessment (PPA) fall risk index score is composed of weighted values from 5 of the sensorimotor and balance measures described above: visual contrast sensitivity, lower limb proprioception, knee extension strength, reaction time, and sway on the foam surface with eyes open. In studies of older people, PPA scores have been able to discriminate between multiple and nonmultiple fallers with accuracies up to 75%, with scores of less than 0 indicating a low risk of falling, 0 to 1 indicating a mild risk, 1 to 2 indicating a moderate risk, and scores of ≥2 indicating a high risk of falling.14
Falls follow-up
A fall was defined as “unintentionally coming to the ground or some lower level and other than as a consequence of sustaining a violent blow, loss of consciousness, sudden onset of paralysis as in stroke or an epileptic seizure.”21 After baseline assessment, the number of falls sustained by each person was monitored prospectively over 6 months by the use of monthly falls diaries, with monthly telephone follow-up if required.22 Previous studies14, 23 of older people have found that recurrent falls (≥2 falls within a year) are more likely to indicate physiological impairments and chronic conditions, and are therefore more clinically important. Because of the very high frequency of falls in PwMS,3, 4, 5, 6, 7, 8, 9,10 frequent fallers were defined as those who had ≥3 falls in the follow-up period.
Power analysis
A power analysis was based on a study of physiological risk factors for falls in a population of older people,24 which indicated that a sample size of 200 would be sufficient for detecting differences in outcome measures between frequent faller and nonfrequent faller groups. A sample of this size was also considered sufficient for not violating assumptions between the number of participants required for multivariate logistic regression modeling25—that is, we estimated that 60 (30%) of the 200 participants would have frequent (≥3) falls during the 6-month falls diary collection period, allowing for up to 6 predictors to be included in our models.
Statistical analysis
Continuously scaled data with right-skewed distributions were log-transformed before analysis. Bivariate correlations between variables were calculated using Pearson and Spearman correlation coefficients. Nonfrequent and frequent fallers were initially compared on the test measures using the relative risk statistic and independent t tests. Logistic regression analysis was then conducted to determine the best set of discriminators. To identify significant and independent fall risk factors across multiple domains, the variable with the strongest association with falls from each of the following domains—sensorimotor function, standing balance, leaning balance, functional mobility, executive function, and pain/fatigue—was entered into the multivariate model. No adjustments for MS disease severity or other medical conditions were made, since our underlying premise is a functional/physiological one. As such, we maintain that the effects of any medical conditions (diagnosed or not) would manifest in 1 or more of the physiological and neuropsychological measures assessed,14 and that the inclusion of medical conditions in the multivariate models may result in overadjusting and dilution of important explanatory findings.26 Marker variables such as previous falls that do not assist in explaining why falls occur were also not entered as possible predictors. A forward stepwise procedure was used in the logistic regression, and variables were retained in the final model if their significance was <.05. Receiver operating characteristic (ROC) analyses were then undertaken to investigate the efficacy of the classification function. The data were analyzed with SPSSa and STATA.b
Results
Demographic, health, and lifestyle characteristics of the sample
Table 1 shows the prevalence of medical conditions, mobility status, medication use, falls in past year, and injuries incurred from falls for the study sample.
NOTE. Values are mean ± SD (minimum–maximum) or n (%).
Associations among the assessment measures, age, and disease severity
Table 2 shows the correlations between the test measures and age and disease severity. Impaired performances in most tests were significantly associated with increased age and disease severity.
∗N=209.
†P<.01.
‡Path length in mm
§P<.05.
Incidence of falls
Of the 210 participants, 209 had complete incidence information for falls at the 6-month follow-up. Of these, 83 participants (39.7%) reported no falls, 57 (27.3%) reported 1 or 2 falls only, 45 (21.5%) reported 3 to 9 falls, and 24 (11.5%) reported 10 or more falls (range, 10–210) (fig 1). A total of 1397 falls occurred. One hundred twenty-one participants (57.9%) sustained 1 or more fall-related injuries: 78 bruises (37.3%), 50 cuts/grazes (23.9%), 21 sprains/strains (10.0%), 5 dislocations (2.4%), and 6 (2.9%) fractures. Frequent fallers had higher average ± SD MS Disease Step levels than nonfrequent fallers (3.8±0.8 and 3.1±1.2, respectively; t1,200=4.36, P<.001) but were of similar age ± SD (52.1±10.5y and 50.1±11.4y, respectively; t1,207=1.26, P=.21).
Fig 1
Number of falls reported by participants during 6-month follow-up. Among frequent (≥3) fallers, 21.5% reported 3 to 9 falls, while 11.5% reported 10 falls or more.
Associations among the assessment measure and frequent falls
Table 3 shows the mean scores plus SDs for the continuously scored test measures for the nonfrequent and frequent fallers. Frequent falling was associated with impaired balance, as indicated by 4 sway tests and 2 leaning balance tests (maximal balance range and coordinated stability), slow stepping and walking (with and without a dual task), slow psychomotor speed (TMT-A scores), reduced executive functioning (TMT-B and TMT B-A scores), reduced fine motor control (9-HPT times), and high PPA fall risk scores. Frequent falling was not significantly associated with reduced visual contrast sensitivity, poor proprioception, reduced lower limb strength, or slow reaction time.
NOTE. Values are mean ± SD or as otherwise indicated. Impaired performance is indicated by low scores in the visual contrast sensitivity, quadriceps strength tests, and maximal balance, and high scores in all other tests.
Abbreviation: NS, not significant.
∗P values were derived using independent group t tests.
A significantly higher proportion of frequent fallers (56.1%) reported lower limb pain than nonfrequent fallers (40.1%) (relative risk, 1.54; 95% confidence interval [CI], 1.03–2.30). There was also a trend indicating that more frequent fallers (59.1%) than nonfrequent fallers (45.3%) reported fatigue affecting their physical performance (relative risk, 1.46; 95% CI, .97–2.19).
Logistic regression identified sway with eyes closed, poor coordinated stability, and reduced fine motor control as the variables that significantly and independently discriminated between participants who did and did not experience frequent falls (model χ23=30.1, P<.001). The adjusted odds ratios for SD decrements in performance for these tests were 1.59 (95% CI, 1.11–2.23) for sway, 1.41 (95% CI, 1.01–1.97) for coordinated stability, and 1.45 (95% CI, 1.02–2.05) for fine motor control. These variables all remained significant when age was also included in the multivariate model. The area under the ROC curve for the 3-variable model was .712 (95% CI, .638–.785). In comparison, the area under the ROC curve for the PPA fall risk score derived in studies of older people was .681 (95% CI, .605–.756).
Discussion
This study aimed to identify fall risk factors in a large sample of community-dwelling PwMS to achieve a greater understanding of important contributors to fall risk in this population. A range of neuropsychological, physical, and functional mobility measures was significantly associated with prospectively measured fall rates, with impaired standing and leaning balance and reduced fine motor control being significantly and independently associated with falls in a multivariate model.
Reduced balance has previously been identified as a significant factor contributing to falls in MS.3, 4, 5, 8, 11, 12 In the present study, frequent fallers performed significantly worse than nonfrequent fallers in all balance tests including the 4 postural sway tests on the floor and the foam with eyes open or closed. Further, in the multivariate logistic regression analysis, 2 complementary balance tests—postural sway with eyes closed on the floor and coordinated stability—were significant and independent predictors of frequent falls. Increased sway assesses quiet standing postural control, is associated with reduced peripheral sensation and quadriceps weakness,27 and is a surrogate measure of sensorimotor performance.14, 28 In contrast, coordinated stability is a voluntary, dynamic measure involving control of the center of mass while leaning to achieve postural control near the limits of the base of support. Both measures contributed to the final model, suggesting that each is an important and independent contributor to fall risk. Importantly, these 2 balance tests are “low tech,” have good test-retest reliability,14 and can be easily used in clinical settings.
Reduced fine motor control, as assessed by poor performance on the 9-HPT, was the third measure included in the multivariate model for frequent falls. The 9-HPT provides a quantitative measure of arm and hand function and is increasingly used in MS clinical trials. It has been chosen as 1 of 3 components of the Multiple Sclerosis Functional Composite.29 Poor performance on the 9-HPT may result from a range of pathophysiological changes, including impaired upper limb strength, sensation, and coordination. The 9-HPT may also serve as a marker of the extent of disease affecting complex upper extremity function. Slow performance on the 9-HPT is associated with abnormalities in the corpus callosum in PwMS30 and with changes in the interhemispheric white matter pathway connecting bilateral supplementary motor areas and other areas involved in planning and movement control.31
Several additional measures (slow choice stepping reaction time, gait speed, reduced executive functioning, the presence of pain) were significantly associated with falls in univariate analyses. These variables were not included in the multiple regression model partly because of their associations with the included balance and motor control measures. However, as these variables reflect specific abilities directly required for safe mobility, they also warrant attention in considering strategies for fall prevention in PwMS. In contrast, some measures that discriminate between elderly fallers and nonfallers, including vision (as assessed with a test of contrast sensitivity), lower limb proprioception, knee extension strength, and simple reaction time,14 appear to be less important in discriminating between frequent and nonfrequent fallers in this sample of PwMS. For visual contrast sensitivity and knee extension strength, this may be due to adequate performance in both frequent and nonfrequent fallers in this group of PwMS, although leg flexor-extensor weakness has been identified as a predictor for recurrent fallers in women with MS.8 However, for lower limb proprioception and simple reaction time, the converse was evident, in that performances for both frequent and nonfrequent fallers above that found in older people at increased risk of falls.18, 32 It may be that PwMS are able to adapt to these deficiencies and avoid falling, provided they do not have concomitant balance and coordination impairments.
The PPA fall risk measure also discriminated significantly between the frequent and nonfrequent fallers in PwMS, with the area under the ROC curve not far below that of the MS-specific model derived here. These findings complement those of Gunn et al,9 in which the authors found a model including the PPA, Ashworth Scale for spasticity had an area under the ROC curve of .73.
Study limitations
Strengths of this study include the broad range of sensorimotor and balance measures and the rigorous prospective falls surveillance. However, the study had a number of limitations. First, we measured the strength of only 1 muscle group: the knee extensors. Weakness in other lower limb muscle groups is common in MS and may be associated with a higher fall risk in this group. Second, we used only single questionnaire items for the measures of pain and fatigue. More rigorous assessment of these measures may have uncovered stronger associations with fall risk. Finally, the inclusion of other signs and symptoms in the present study, such as joint contractures and spasticity, may have further improved our explanatory model of fall risk in PwMS. Further research aimed at identifying cut points for strength and other measures, as well as comparing performance scores obtained in PwMS with normative data, may also provide useful additional information regarding fall risk in this population.
Conclusions
The study findings elucidate important balance, coordination, and cognitive determinants of falls in PwMS. Multifaceted fall prevention strategies have been found to be effective in the general older community, but to date, only preliminary fall prevention interventions have been undertaken in PwMS.33, 34, 35 Future studies that assess intervention strategies targeting falls should consider fall risk factors that are potentially amenable to intervention (eg, impaired coordination, balance, stepping, and gait), as well as factors that need to be considered in delivery of an intervention (eg, executive function impairments). The findings of this large prospective study suggest that assessment of physical and cognitive fall risk factors is required in clinical practice to tailor intervention strategies to the fall risk profile of individuals with MS.
Suppliers
- a.SPSS Inc, 233 S Wacker Dr, 11th Fl, Chicago, IL 60606.
- b.StataCorp LP, 4905 Lakeway Dr, College Station, TX 77845.
Acknowledgments
We thank Multiple Sclerosis Limited and physiotherapists of Multiple Sclerosis Limited for assisting us in recruitment and assessment of participants, and Ms. Connie Severino for her enormous efforts and time for data entry.
Appendix 1. Disease Steps
Methods: For Disease Steps, classification of a patient is determined by history and neurologic examination as well as course of MS. The scale consists of the following categories:
- ❏0 = Normal: functionally normal with no limitations on activity or lifestyle. Patients may have minor abnormality on examination, such as nystagmus or an extensor plantar. The course is relapsing-remitting with a return to baseline with or without treatment. These patients are not treated with any ongoing symptomatic therapy for MS.
- ❏1 = Mild disability: mild symptoms or signs. These patients have mild but definite findings such as sensory abnormalities, mild bladder impairment, minor incoordination, weakness, or fatigue. There is no visible abnormality of gait. The pattern of disease is relapsing-remitting, but patients may not have a full return to baseline after attacks. These patients may use ongoing symptomatic therapy such as amantadine, baclofen, or oxybutynin.
- ❏2 = Moderate disability: the main feature is a visibly abnormal gait, but patients do not require ambulation aids. The pattern of disease is relapsing-remitting or progressive.
- ❏3 = Early cane: intermittent use of cane (or other forms of unilateral support including splint, brace, or crutch). These patients use unilateral support primarily for longer distances but are able to walk at least 25ft without it. The pattern of disease is relapsing-remitting or progressive.
- ❏4 = Late cane: these patients are dependent on a cane or other forms of unilateral support and cannot walk 25ft without such support (eg, these patients may hang on to furniture inside their homes or touch the wall when walking in clinic). Patients may use a scooter for greater distances (eg, malls). The pattern of disease is relapsing-remitting or progressive.
- ❏5 = Bilateral support: patients require bilateral support to walk 25ft (eg, 2 canes or 2 crutches or a walker). They may use a scooter for greater distances. The pattern of disease is relapsing-remitting or progressive.
- ❏6 = Confined to wheelchair: patients are essentially confined to a wheelchair or scooter. They may be able to take a few steps but are unable to ambulate 25ft, even with bilateral support. They may show further progression including worsening hand function or inability to transfer independently.
- ❏U = Unclassifiable: this category is used for patients who do not fit the above classification (eg, significant cognitive or visual impairment, overwhelming fatigue, or significant bowel or bladder impairment in an otherwise minimally impaired patient).
Reprinted by Permission of SAGE from: Hohol MJ, Orav EJ, Weiner HL. Disease steps in multiple sclerosis: a longitudinal study comparing disease steps and EDSS to evaluate disease progression. Mult Scler 1999;5:349-54. Copyright © 1999 by SAGE Publications.
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