Balance Score and a History of Falls in Hospital Predict Recurrent Falls in the 6 Months Following Stroke Rehabilitation

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ORIGINAL ARTICLE

Balance Score and a History of Falls in Hospital Predict Recurrent Falls in the 6 Months Following Stroke Rehabilitation Shylie F. Mackintosh, PhD, Keith D. Hill, PhD, Karen J. Dodd, PhD, Patricia A. Goldie, PhD, Elsie G. Culham, PhD ABSTRACT. Mackintosh SF, Hill KD, Dodd KJ, Goldie PA, Culham EG. Balance score and a history of falls in hospital predict recurrent falls in the 6 months following stroke rehabilitation. Arch Phys Med Rehabil 2006;87:1583-9. Objective: To investigate predictors of recurrent falls in adults who return to community dwelling after stroke rehabilitation. Design: Prospective observational study. Setting: Community. Participants: Fifty-five adults with stroke (mean age ⫾ standard deviation, 68.1⫾12.8y). Interventions: Not applicable. Main Outcome Measures: Baseline measures included balance, gait speed, muscle strength and tone, activity level, hemianopia, visual contrast sensitivity, hemineglect, medication use, fear of falling, and depression. Participants kept a 6-month prospective falls diary after discharge from rehabilitation. Results: Twenty-five (45%) participants reported falling, 12 had recurrent falls (ⱖ2 falls), and 13 fell once. Participants who fell recurrently had histories of falling during hospitalization or rehabilitation, poorer physical function measures, were taking more medications, and were more likely to have hemineglect than participants who fell once or did not fall (P⬍.05). A history of falling in the hospital or during rehabilitation, combined with poor balance (either Berg Balance Scale score ⬍49 or step test score ⬍7), predicted recurrent falls with sensitivity and specificity values greater than 80%. Conclusions: Falls are a common occurrence after stroke. The predictive model developed can be used to identify people who are likely to have recurrent falls in the 6 months after stroke rehabilitation. Key Words: Accidental falls; Cerebrovascular accident; Rehabilitation; Sensitivity and specificity. © 2006 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation

ETWEEN 50% AND 70% OF PEOPLE who return home after a stroke fall, especially during the first few months B of living at home. These falls may lead to depression, 1-4

1,5

1

restriction of activities,1 and minor injuries such as cuts, abrasions, and bruises.1,5,6 Falls can also lead to more serious consequences such as hip fractures,7 and may even result in a person moving to residential care.8 Therefore, it is important to establish predictors for falling in stroke patients so that falls and injury prevention programs target the people most at risk of falling. To date, there is little agreement about which factors increase the risk of falls in community-dwelling people with stroke. Potentially modifiable risk factors include: motor impairments,1,3,9,10 balance impairment,11 low levels of functional independence,1,2 falls in the hospital,1 depression,12 and motor and sensory impairments combined.3 Studies have suggested that as the number of risk factors increases, the likelihood of falling increases.12,13 Little attention has been given to the accuracy of falls predictors. One study14 investigated the “stops walking when talking” test to predict falls in community-dwelling stroke patients who could walk independently. For people who fell more than once in 6 months, the test had a positive predictive value of 42%, a negative predictive value of 89%, specificity of 69%, and sensitivity of 73%.14 Therefore, it was concluded that while this test is easy to administer, its use as a single predictor of recurrent falls in communitydwelling stroke patients is questionable. In an inpatient setting, lengthy fall risk assessment tools have been used to predict inpatient falls in stroke patients9,13; however, at this stage it is not clear how to predict accurately who will fall when they return home. Some studies have separated people who have 2 or more falls (recurrent falls) from people who only fall once or have never fallen.6,11 It may be particularly important to identify people who are likely to fall recurrently because they are at more risk of injury.15 People who fall more often are also more likely to lose confidence and to restrict their activity.16 Therefore, our purpose in this study was to identify the predictors of recurrent falls in adults who return home after stroke rehabilitation. METHODS

From La Trobe University, Victoria, Australia (Mackintosh, Goldie); School of Health Sciences, University of South Australia, Adelaide, SA, Australia (Mackintosh); National Aging Research Institute, Victoria, Australia (Hill); Melbourne Extended Care and Rehabilitation Service, Victoria, Australia (Hill); and Queen’s University, Kingston, ON, Canada (Culham). Supported by La Trobe University (faculty research grant). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated. Reprint requests to Shylie F. Mackintosh, PhD, School of Health Sciences, City East Campus, University of South Australia, North Terrace, Adelaide, South Australia 5000, e-mail: [email protected]. 0003-9993/06/8712-10738$32.00/0 doi:10.1016/j.apmr.2006.09.004

Participants Consecutive patients with stroke from 3 participating rehabilitation centers were invited to participate in the study if they were completing a stroke rehabilitation program and were returning to a community setting. Exclusion criteria were: a concurrent neurologic disorder (eg, Parkinson’s disease) or major orthopedic problem (eg, amputation) that might predispose them to falling, an Orientation-Memory-Concentration (OMC) test score above 10 (indicating dementia17) and no caregiver, or English-language skills insufficient to provide informed consent and complete the testing procedures. Arch Phys Med Rehabil Vol 87, December 2006

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Fig 1. Flow of participants in and out of the study.

In the 12-month recruitment period, 207 patients were discharged from the rehabilitation centers (fig 1). Ninety-one people did not meet the inclusion and exclusion criteria, 45 chose not to participate, and 3 were discharged before being invited to participate, leaving 68 participants in the study. The human research ethics committees of the relevant institutions approved the study. Initial Interview During Rehabilitation At an initial interview in the rehabilitation setting the OMC test,17 a simple and valid tool for screening cognitive function in stroke patients18 was administered to ensure the participants had sufficient recall to provide valid falls data about their falls. All participants who scored greater than 10, indicating significant cognitive decline,17 had a caregiver who was willing to help the subjects with their fall diaries and interviews. Data concerning age, sex, type of stroke, discharge FIM instrument scores,19 and documented falls in case records were also collected at this stage. Assessment in the Home Setting A trained research physical therapist made a home-based assessment of potential falls risk factors within 14 days of a subject’s discharge from rehabilitation. Participants (and/or caregivers) were asked about any falls during their acute hospital stay or during rehabilitation. Several sensory falls risk factors were assessed. Hemianopia was assessed using the confrontation visual field method, which has been shown to Arch Phys Med Rehabil Vol 87, December 2006

have a 90% sensitivity to detect hemianopia.20 Contrast sensitivity was assessed with the Melbourne edge test,21,22 using a light box to standardize the illumination. We assessed hemineglect with the star cancellation test23 and the baking tray task,24 which are valid, sensitive tests for identifying neglect in stroke patients. We calculated a star cancellation ratio to determine the presence of hemineglect.25,26 The baking tray task involved spreading 16 blocks evenly over an A4-sized board.24 The number of blocks on each half of the tray was recorded. An asymmetry ratio of more than 7:9 was interpreted to mean that the subject had hemineglect.24 Participants who tested positive in either neglect test were considered to have hemineglect because it has been reported that more than 1 test is preferable when attempting to diagnose hemineglect.26 Also, we assessed the following fall risk factors that may make it difficult to respond to a challenge to balance: reduced lower-limb muscle strength, decreased balance, slower gait speed, hypertonia, and the use of psychotropic medications that may slow reactions or cause dizziness. We used a Nicholas Manual Muscle Testera to test the maximal isometric muscle force of the hip abductors, quadriceps, and dorsiflexors of each leg using a standardized, reliable procedure established with stroke patients (test-retest; intraclass correlation coefficient [ICC] range, .87⫺.95).27,28 We calculated a mean score of 3 trials for each muscle group27 and then a measure of weakness was derived by describing the more affected side as a percentage of the less affected side.29 Balance was evaluated using the Berg Balance Scale (BBS)30 and the step test.31 The BBS uses a 5-point scale to evaluate 14 functional tasks and is a valid, highly reliable tool for use with stroke patients (test-retest, ICC⫽.98).30,32,33 The step test, also a reliable and valid test for stroke patients (test-retest; ICC range, .93⫺.94),31 involves stepping 1 foot on and off a 7.5cm step as quickly as possible for 15 seconds and recording the number of completed steps. Both legs were tested and the lowest score was used. Gait speed was timed over 5m at the participant’s fast pace.34 We used the Tone Assessment Scale35 to assess muscle tone: it is a reliable tool (interrater agreement; weighted ␬ range, .66⫺.94) that has 5 descriptors, ranging from “no increase in muscle tone” through to “affected part rigid,” to assess response to 6 combined passive movements in both the upper and lower limbs on the more affected side. Subjects were asked what prescriptions and over-the-counter medications they were currently using. Psychotropic medications were defined as any medications classified as neuroleptics, antipsychotics, tranquillizers (major and minor), anxiolytics, sedatives, hypnotics, or antidepressants.36 Behavioral factors that may increase the risk of falling include fear of falling, depression, and activity levels. Fear of falling was assessed with the Swedish modification of the Falls Efficacy Scale (FES-S), a valid, reliable tool with stroke patients.37 The scale asks people to rate how confident they feel while performing 13 common activities. Because not all participants had attempted all the items in the FES-S since their stroke, scores were converted to a percentage of the number of items they responded to (maximum score of 100 indicated no fear of falling). We assessed depression with the Geriatric Depression Scale (GDS), a 30-item scale specifically designed to measure depression in older people38 that has been validated in community-dwelling stroke patients.39 We used the adjusted activity score (AAS) of the Human Activity Profile (HAP)40 to investigate activity levels. After the home-based assessment, participants recorded falls data for 6 months in a diary. They returned a section of the

PREDICTORS OF FALLS AFTER STROKE, Mackintosh

diary every 2 weeks and if the section was not returned within a week of the due date, subjects were reminded by a phone call. Falls that occurred between discharge from rehabilitation and the home assessment were recorded. A fall was defined as “an event that resulted in a person coming to rest inadvertently on the ground or other lower level and other than a consequence of sustaining a violent blow, loss of consciousness, sudden onset of paralysis such as stroke, or an epileptic seizure.”41(p4) During interviews after a fall was reported, the researchers determined whether a reported “fall” met our definition. Data Analysis Participants were categorized into 2 groups. People who reported 1 fall or no falls in the 6 months after discharge from rehabilitation were allocated to the single or no falls group, and people who had 2 or more falls were allocated to the recurrent falls group. Group differences were assessed using chi-square tests for categorical variables, t tests for continuous variables that met variance and normality assumptions, or Mann-Whitney U tests for ordinal variables or continuous variables that did not meet variance and normality assumptions. Cutoff scores were used for the balance and gait speed variables. For the BBS, a cutoff score of 49 was drawn from the findings of a previous study with older community-dwelling people.42 For the step test and gait speed, cutoff scores of 7 and 0.56m/s, respectively, were decided by examining the distribution of the data in each group and trying several cutoff scores to maximize prediction accuracy. An ␣ level of .05 was set for all tests. As proposed by Savitz and Olshan,43 ␣ levels were not adjusted for multiple independent variables. This was done to maintain statistical power, test specific associations of interest, and inform analysis for predictors of fallers. To examine the predictors of recurrent fallers, significant variables in the group analyses were entered in univariate logistic regression analyses. Sensitivity, specificity, and positive predictive and negative predictor values were also calculated for dichotomous variables. To determine if a combination of variables provided greater predictive ability, significant variables were considered for multivariate logistic regression analysis. Colinearity between independent variables was analyzed using a correlation matrix. Appropriate dependent variables were then entered into a forward stepwise logistic regression analysis. SPSS softwareb was used for all data management and analysis. RESULTS Fifty-four of the 68 participants completed all sections of the diaries. Of the 13 participants who withdrew (1 patient died), 3 moved into residential care, 4 experienced medical complications such as another stroke, and 4 reported that they were having difficulty coping at home and they thought the study added too much extra stress. Two participants gave no reason for withdrawing. Data from the subject who died were included in the analysis because that patient had reported 4 falls. Therefore, that person’s group membership was unequivocal. The 55 participants who were included in the analysis had a mean age ⫾ standard deviation (SD) of 68.1⫾12.8 years (age range, 28⫺88y). Thirty participants (55%) were women and 46 (84%) walked independently with or without aid. The type of stroke was most frequently an infarct (69%), with the mean time since stroke being 2.3⫾1.6 months at the time of the home-based assessment. Participants had a mean FIM score of 108.5⫾15.9 at discharge from rehabilitation and a mean OMC test score of 4.9⫾3.7. Three participants had an OMC test score above 10 and required the assistance of a caregiver to complete the diaries and interviews.

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During the study period, 25 (45%) participants reported falling. Twelve participants (48% of the fallers) reported falling 2 or more times, and 13 reported only 1 fall. Five subjects fell between discharge from rehabilitation and the home-based assessment, 4 of these participants reported no more falls in the 6-month diary period. As table 1 shows, there were no significant differences between the 2 groups in age, history of stroke, or history of falls before their stroke. There was a significant difference between the 2 falls groups for falls during hospitalization or rehabilitation. Ninety-two percent of the subjects who had recurrent falls after discharge from rehabilitation fell at least once while in the hospital or during rehabilitation. Of the 3 factors that may affect the ability to detect a hazard (hemineglect, hemianopia, reduced contrast sensitivity), only hemineglect was significantly more likely in participants who had recurrent falls (P⬍.05). Several physical function variables that may affect the ability to respond to a perturbation were significantly different between the groups (see table 1). Participants who had recurrent falls after discharge from rehabilitation had significantly lower quadriceps strength scores and lower BBS and step test scores, indicating poorer balance, than participants in the single or no falls group. Subjects who had recurrent falls also had a significantly slower median fast gait speed than people who had 1 or no falls. The number of medications taken differed significantly between the groups (see table 1), with the recurrent falls group taking significantly more medications than participants who had 1 or no falls. Although approximately one third of all participants were taking psychotropic medications, the proportion of recurrent fallers taking those medications was not significantly different from the proportion of the single or no falls group (P⬎.05) (see table 1). The only behavioral factor that differed significantly between groups was the AAS of the HAP at the time of the home-based assessment (see table 1). The AAS was significantly lower for participants who had recurrent falls than participants who had a single or no falls. Activity levels for both groups were low. Moderate levels of depression and mild to moderate levels of fear of falling were evident; however, scores did not differ significantly between the groups (P⬎.05) (see table 1). Predictors of Recurrent Falls Table 2 shows the results from univariate logistic regression performed on variables that demonstrated significant differences between the 2 groups. All variables except hemineglect, the number of medications taken, and the AAS of the HAP had significant odds ratios. Falls during hospitalization or rehabilitation were most strongly associated with recurrent falls. The sensitivity of this factor—the proportion of participants who had recurrent falls correctly predicted—was 92%. The specificity—the proportion of participants who did not have recurrent falls that were correctly predicted—was 72%. The positive predictive value, the likelihood that a participant who fell in the hospital or during rehabilitation would go on to have recurrent falls, was 48%, and the negative predictive value was 97% (see table 2). To ascertain if a combination of variables produced an improved prediction rate for recurrent falls, all variables that had significant odds ratios were considered for multivariate logistic regression. There were significant correlations between fast gait speed and balance measures. Therefore, the balance measures, fast gait speed, and quadriceps strength were entered separately with history of falls in the hospital Arch Phys Med Rehabil Vol 87, December 2006

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PREDICTORS OF FALLS AFTER STROKE, Mackintosh Table 1: Group Comparisons for Demographic Factors and Variables Likely to be Associated With Hazard Detection (Nⴝ55) Variable

Demographics Age Sex (women) Previous stroke Falls before stroke Falls in hospital or rehabilitation Factors reducing ability to detect a hazard Hemianopia Contrast sensitivity (dB) Hemineglect Factors increasing difficulty to respond to balance challenges Dorsiflexor strength Quadriceps strength Hip abductors strength BBS score BBS score ⬍49 Step test score (lowest score both legs) Step test score ⬍7 (lowest score both legs) Fast gait speed (ms) Fast gait speed ⬍.56m/s Tone No. of medications Use of psychotropic medication Behavioral factors Fear of falling (FES-S) Depression (GDS) AAS (HAP)

Recurrent Falls (n⫽12)

Single or No Falls (n⫽43)

Test

P

65.9⫾17.3 8 (67) 3 (25) 4 (33) 11 (92)

68.7⫾11.5 22 (51) 8 (19) 11 (26) 12 (28)

t ␹2 ␹2 ␹2 ␹2

.51 .34 .62 .59 .00

1 (8) 19.5 (16.5,21.0) 6 (50)

5 (11) 21.0 (19.0,24.0) 9 (21%)

␹2 U ␹2

.75 .06 .05

50 (1,84) 50 (7,67) 77 (10,97) 36.0 (23.3,46.3) 11 (92) 0.0 (0.0,3.3) 11 (92) 0.44 (0.27,0.71) 7 (58) 2.5 (0.0,5.5) 6.0 (4.3,7.8) 4 (33)

86 (46,98) 68 (45,86) 87 (45,86) 53.0 (45.0,54.0) 15 (35) 8.0 (5.0,10.0) 16 (37) 0.84 (0.66,1.16) 8 (19) 2.6 (0.0,3.3) 4.0 (3.0,6.0) 12 (28)

U U U U ␹2 U ␹2 U ␹2 U U ␹2

.13 .04 .52 .00 .00 .00 .00 .01 .01 .13 .03 .71

73.7 (47.7,86.6) 14.0 (6.5,17.3) 12.5 (5.5,30.5)

83.1 (73.3,90.0) 8.0 (5.0,8.0) 32.0 (25.0,45.0)

U U U

.12 .14 .02

NOTE. For t test, mean ⫾ SD is reported; for Mann-Whitney U test, median (25th, 75th percentiles) are reported; and for ␹2 test, proportion (percentage) are reported. For strength, scores are a percentage of the more affected side compared with the less affected side.

or during rehabilitation into a series of forward stepwise logistic regression (Wald) analyses. Table 3 summarizes the results of the multivariate analysis for predictors of recurrent fallers. The combination of an impaired balance measure (either BBS score ⬍49 or step test score ⬍7) and a history of a fall in hospital or during rehabilitation provided improved predictive power for recurrent falls with sensitivity and specificity values greater than 80%, and positive predictive and negative predictive values above 60%. When fast gait speed less than .56m/s and quadriceps strength were entered individually with falls in hospital, neither of these variables were selected within the logistic regression analysis and so did not add to the prediction model for recurrent falls.

The 5 participants who fell between the time of discharge from rehabilitation and the home-based assessment may have confounded the results, and so the data were reanalyzed with those subjects removed. A combination of a history of falls in the hospital or during rehabilitation and a BBS score less than 49 still had high predictive rates (sensitivity, specificity, positive and negative predictive ⱖ75%), as did a combination of a history of falls in hospital or rehabilitation and a step test score less than 7 (sensitivity, specificity, positive and negative predictive ⱖ64%). In summary, a history of falling in the hospital and/or during rehabilitation and an impaired balance measure, either a BBS score of less than 49 or a step test score less than 7, were independent predictors of falling 2 or more times in the 6

Table 2: Univariate Logistic Regression Analysis for Predictors of Recurrent Falls (Nⴝ55) Variable

Odds Ratio (95% CI)

Sensitivity (%)

Specificity (%)

Positive Predictive

Negative Predictive

Falls in hospital or rehabilitation (yes) BBS score ⬍49 (yes) Step test score ⬍ 7 (yes) Fast gait speed ⬍.56m/s (yes) Quads strength % more affected of less affected (per unit increase) Hemineglect (yes) No. of medications (per unit increase) AAS HAP (per unit increase)

28.4 (3.3⫺244.6) 20.5 (2.4⫺174.7) 19.8 (2.3⫺168.7) 6.1 (1.5⫺24.4) 0.1 (0.0⫺0.9)

92 92 92 58 NA

72 65 64 81 NA

48 42 42 47 NA

97 97 97 88 NA

3.8 (1.0–14.6) 1.3 (1.0⫺1.7) 0.9 (0.9–1.0)

50 NA NA

79 NA NA

40 NA NA

85 NA NA

NOTE. Sensitivity, specificity, and positive and negative prediction values only calculated for dichotomous variables. For the step test, the lowest score when both legs were tested was used. Abbreviations: CI, confidence interval; NA, not applicable.

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PREDICTORS OF FALLS AFTER STROKE, Mackintosh Table 3: Multivariate Logistic Regression for Predictors of Recurrent Falls (Nⴝ55) Variables Entered

Adjusted Odds Ratio (95% CI)

Sensitivity (%)

Specificity (%)

Positive Predictive

Negative Predictive

BBS score ⬍49 (yes) Fall in hospital or rehabilitation (yes) Step test score ⬍7 (yes) Fall in hospital or rehabilitation (yes)

7.5 (1.4⫺40.6) 20.5 (2.2⫺190.6) 9.7 (1.0⫺93.3) 17.2 (1.9⫺145.2)

83

91

71

95

83

86

63

95

NOTE. For the step test, we used the lowest score when both legs were tested. For the sensitivity, specificity, and positive and negative predictive analyses, a yes to an impaired balance score (BBS score ⬍49, step test score ⬍7) and a yes to a fall in hospital or rehabilitation were combined to indicate a “positive test” for predicting recurrent falls.

months after discharge from stroke rehabilitation, and a “yes” to both factors combined to achieve the strongest prediction accuracy. DISCUSSION The combination of a history of falls during hospitalization or rehabilitation and poor performance on 1 of the simple clinical balance measures was highly accurate in predicting the risk of recurrent falls in people returning home after stroke. Sensitivity, specificity, and positive prediction and negative prediction values were all above 60%, suggesting that it is possible to predict with considerable accuracy those stroke patients who will have multiple falls, and those who will not. This is considerably higher than what has been reported in previous studies. For example, 1 study11 reported that only 43% of recurrent fallers were correctly predicted (sensitivity) by self-reported difficulties in balance while dressing and residual balance problems after stroke, while in another study14 a positive “stops walking when talking” test only predicted recurrent fallers in 42% of cases (positive predictive value). Other studies have not reported the accuracy of their predictions of fallers. While we acknowledge that our prediction model requires further validation, a combination of a history of falling in a hospital or during rehabilitation, and a poor balance score (either a BBS score less than 49 or a step test score less than 7) shows potential as a tool to identify people with stroke who have a high risk of recurrent falls after completing rehabilitation. Recalling a fall during a hospital stay or rehabilitation by participants or caregivers was shown in our study to be an independent risk factor for recurrent falls in the 6 months after rehabilitation. This finding is supported by another study.1 In our study, this variable alone had high sensitivity and specificity scores; however, the positive predictive value was low, with only 48% of participants who fell in the hospital or during rehabilitation actually having recurrent falls in the next 6 months. The combination of this variable with a balance measure, in particular the BBS less than 49, improved the positive predictive value to 71%, suggesting that the combined variables form a better screening test for recurrent falls risk. In our study, both balance score cutoffs (BBS score ⬍49, step test score ⬍7), had significant odds ratios. One study44 reported no association between a falls history and BBS scores in chronic stroke, however, that study did not investigate predicting future falls. Other performance measures of balance such as body sway,12 motor club assessment,1 and a combination of timed side by side, semi-tandem, and tandem stand rated on a scale of 0 to 4,11 were not predictive of falls in stroke patients. The results of our study suggest that the BBS or the step test may be useful balance measures in predicting falls risk in a community setting.

Study Limitations This study has provided useful information about the prediction of recurrent falls in stroke patients who return home after rehabilitation. It is acknowledged, however, that 45 potential participants declined to participate in the study. It was not possible to make comparisons between participants and nonparticipants because demographic data could not be collected from those who chose not to participate. Despite this, the participants had an age and sex profile similar to another study45 that included all patients in a stroke rehabilitation unit over 1 year and our participants also had discharge FIM scores similar to another study46 with a 100% participation rate. This indicates that our subject sample was similar to those of other studies that involved all stroke subjects in a rehabilitation setting, suggesting that our results may be generalized to other people who return home after stroke. Sample size may have limited our analysis of variables with smaller effect sizes. For example, contrast sensitivity is a visual function measure that has been repeatedly associated with falls in older people.22,47,48 With our small effect size of .48, we had only approximately 40% power to detect a significant difference, if one existed, in this variable. Further studies with larger sample sizes are needed. Another issue that may have affected our results was that by the time the home-based assessments were made, several participants had already experienced falls in the hospital, during rehabilitation, or at home. In fact, 92% of participants who had recurrent falls reported a fall in the hospital or during rehabilitation. It is therefore possible that some factors, such as fear of falling or depression, were consequences of a falls history rather than factors leading to future falls. There was little difference in the predictive ability of the combined risk factors of a fall in a hospital or during rehabilitation and an impaired balance score when predictors for recurrent falls were reanalyzed without the data of patients who fell at home after discharge from rehabilitation, but before the home-based assessment. CONCLUSIONS This study found that people who had recurrent falls had distinct characteristics. They had poorer balance, walked more slowly, and were less active. They also took more medications. These factors may have reflected a general frailty,49 or could be adverse reactions to, or interactions between, medications affecting balance and reaction time or causing postural hypotension or dizziness.49 These results have implications for stroke clinicians in the assessment and treatment of people with stroke. There is a need for targeted falls prevention and injury minimization programs for stroke patients when they return home. While in rehabilitation, those subjects may benefit from education about the risk of falls when they return home. Medications need to be careArch Phys Med Rehabil Vol 87, December 2006

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fully reviewed. Functional mobility training, especially balance training, must be maximized and must be responsive to the complex demands of returning home successfully, including dual task activities and adapting to different environments. It is not possible for all stroke patients to return home with good physical function, therefore, they must be taught how to mobilize safely within their limitations; and harm minimization strategies such as use of hip protectors may need to be recommended. In conclusion, it is possible to accurately predict whether stroke patients who have completed rehabilitation are at risk of recurrent falls. Tailored falls and injury prevention strategies are recommended for these people when they return home. Acknowledgments: The project would not have been possible without the physiotherapists at the Melbourne Extended Care and Rehabilitation Service, St Margaret’s Rehabilitation Hospital, and Griffith Rehabilitation Hospital. Two research physiotherapists, Catherine Culhane and Prue Plummer, made important contributions to the recruitment of participants and data collection. References 1. Forster A, Young J. Incidence and consequences of falls due to stroke: a systematic inquiry. BMJ 1995;311:83-6. 2. Ugur C, Gücüyener D, Uzuner N, Ozkan S, Özdemir G. Characteristics of falling in patients with stroke. J Neurol Neurosurg Psychiatry 2000;69:649-51. 3. Yates JS, Lai SM, Duncan PW, Studenski S. Falls in communitydwelling stroke survivors: an accumulated impairments model. J Rehabil Res Dev 2002;39:385-94. 4. Mackintosh SF, Hill K, Dodd KJ, Goldie P, Culham E. Falls and injury prevention should be part of every stroke rehabilitation plan. Clin Rehabil 2005;19:441-51. 5. Gücüyener D, Ugur C, Uzuner N, Özdemir G. The importance of falls in stroke patients. Ann Saudi Med 2000;20:322-3. 6. Hyndman D, Ashburn A, Stack E. Fall events among people with stroke living in the community: circumstances of falls and characteristics of fallers. Arch Phys Med Rehabil 2002;83:165-70. 7. Ramnemark A, Nilsson M, Borssen B, Gustafson Y. Stroke, a major and increasing risk factor for femoral neck fracture. Stroke 2000;31:1572-7. 8. Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med 1997;337:1279-84. 9. Rapport LJ, Webster JS, Flemming KL, et al. Predictors of falls among right-hemisphere stroke patients in the rehabilitation setting. Arch Phys Med Rehabil 1993;74:621-6. 10. Nyberg L, Gustafson Y. Patient falls in stroke rehabilitation. A challenge to rehabilitation strategies. Stroke 1995;26:838-42. 11. Lamb SE, Ferrucci L, Volapto S, Fried LP, Guralnik JM. Risk factors for falling in home-dwelling older women with stroke: the Women’s Health and Aging Study. Stroke 2003;34:494-500. 12. Jørgensen L, Engstad T, Jacobsen BK. Higher incidence of falls in long-term stroke survivors than in population controls: depressive symptoms predict falls after stroke. Stroke 2002;33:542-7. 13. Nyberg L, Gustafson Y. Fall prediction index for patients in stroke rehabilitation. Stroke 1997;28:716-21. 14. Hyndman D, Ashburn A. Stops walking when talking as a predictor of falls in people with stroke living in the community. J Neurol Neurosurg Psychiatry 2004;75:994-7. 15. Nevitt MC, Cummings SR. Risk factors for injurious falls: a prospective study. J Gerontol 1991;46:M164-70. 16. Vellas BJ, Wayne SJ, Romero LJ, Baumgartner RN, Garry PJ. Fear of falling and restriction of mobility in elderly fallers. Age Ageing 1997;26:189-93. 17. Katzman R, Brown T, Fuld P. Validation of a short orientationmemory-concentration test of cognitive impairment. Am J Psychiatry 1983;140:734-9. Arch Phys Med Rehabil Vol 87, December 2006

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Suppliers a. LaFayette Instruments, 3700 Sagamore Parkway N, LaFayette, IN 47904. b. Version 11.0; SPSS Inc, 233 S Wacker Dr, 11th Fl, Chicago, IL 60606.

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