Relationship of balance and mobility to fall incidence in people with chronic stroke.

Falls are the leading cause of injury-related deaths among elderly people in North America. (1) For elderly community-dwelling individuals, falls are common (2-4) and can be attributed to multiple factors such as impaired balance, (5-10) declining cognition, (3,10) and presence of neurological disease. (2,3,5,6,10,11) Stroke is considered one of the greatest risk factors for falls among elderly people. (12,13) The majority of individuals with stroke will have some degree of residual impairment, but will regain walking ability and will be discharged home following hospitalization. (14) Although residual impairment is common, most people with stroke will regain walking ability; however, poor balance and impaired gait can persist. (14)

Fall incidence rates between 23% and 50% have been reported in studies of people with chronic stroke (>6 months poststroke). (12,15-17) This rate is much higher than rates reported for older community-dwelling adults without stroke (11%-30%) (3,18,19) but lower than rates for people with subacute stroke (1-6 months poststroke) (25%). (20) Injury is a frequent consequence of falls in people with chronic stroke, with up to 28% reporting an injury. (15) Over half of all reported falls occurred indoors during walking activities. (12,16) Studies have shown that cognitive deficits, (13,20-24) functional impairment, (13,20,23,25,26) and impaired balance (20,22,23) are related to fall incidence in people with acute stroke (up to 1 month poststroke) and subacute stroke. However, in people with chronic stroke, factors that relate to falls and fall risk are not as clear. To date, only a few studies (12,15-17) have examined falls in people with chronic stroke. Measures of balance, (12,15,16) motor status, (12,15,16) cognition/ mood, (12,15-17) strength (defined as muscle force), (12,15) vision, (12,15) and activities of daily living (ADL) (12,15-17) have been used to predict fall risk in people with chronic stroke. These studies showed that only 3 of the factors--cognition/mood, (12,16) balance impairment, (15,17) and ADL (15-17)--increased fall risk in people with chronic stroke.

In studies that examined balance, the results were mixed, with Jorgensen et al (12) finding that balance was not a risk factor for falls, whereas Lamb and colleagues (15) and Hyndman and Ashburn (17) found that balance was a risk factor. Even in these 2 studies, (15,17) the question of balance as a risk factor was not clear. Lamb et al (15) used measures of self-reported balance difficulties (eg, while dressing) and performance measures (ie, people were asked to stand with feet side by side, semitandem, and tandem); only the self-reported measures predicted falls. Hyndman and Ashburn (17) used the Berg Balance Scale (BBS) and found a difference in scores between people who fell more than once and people who did not fall, but they found no difference in scores between people who did not fall and people who fell once.

Only 2 studies (15,16) examined mobility (eg, gait speed, ability to move in bed, transferring from one surface to another) as a potential risk factor for falls in people with chronic stroke. Lamb et al (15) found that gait speed and transfer ability were related to falls in a bivariate analysis but that only transfer ability was related to falls in a multivariate analysis. Hyndman et al (16) found that mobility was not a factor in fall events. One of the reasons that Lamb et al (15) found such a relationship but Hyndman et al (16) did not may have been the method in which mobility was measured. Lamb et al (15) used a measure of walking (ie, gait speed) along with specific one-item functional tests such as rising from a chair, transfers, and bed mobility, whereas Hyndman et al (16) used only one functional measure (Rivermead Mobility Index). Taken separately, these one-item measures are related to fall incidence, but once they are combined with other variables that may be related to falls (eg, depression, balance, ADL ability), their impact is diminished or negated. This may suggest the importance of comprehensive assessments that focus on the physical, cognitive/ psychological, and environmental factors.

Although cross-sectional studies have shown that stroke and balance impairment are risk factors for falls and fractures in older adults, (2,27) the evidence is not conclusive as to whether balance or mobility are risk factors for falls when the sample is limited to only individuals with chronic stroke. The purpose of this study was to quantify the relationship of balance (as measured by the BBS) and mobility (gait speed) to fall incidence in a community-dwelling sample of individuals with chronic stroke.

Gait difficulties have been shown to be associated with falls in older adults. (2,5,6,8) Two of the studies involving individuals with chronic stroke (15,16) examined mobility as a potential risk factor for falls. Lamb et al (15) used a 3-point ordinal scale (1-3) to indicate walking ability and may have lost important information (ie, variability) by doing so. Only the rating of 1 ([less than or equal to] 0.25 m/s) was defined in the article. We assumed that the distribution was separated into thirds to indicate the ratings of 2 and 3. This study also was limited to female subjects only. Because only 2 studies have used mobility as a dependent variable, with mixed results, the role that mobility might play in falls remains unclear. Several authors (12,16,17) also contended that walking ability would be a factor in falls because the majority of falls reported occurred during walking. Gait speed is highly correlated with measurements of mobility (28) and has been shown to be a discriminate measure for studying locomotor recovery because it is sensitive enough to reflect physiological and functional changes. (28,29)

Method

Subjects and Design

One hundred eight people with stroke were recruited on a voluntary basis from the community. Flyers were posted in local senior centers and community centers, and advertisements were placed in the local newspapers. The inclusion criteria were: (1) over 50 years of age, (2) first stroke, (3) at least 1 year poststroke, and (4) reported ability to walk 8 m (with assistive device, if required). This information was gained during a screening interview conducted over the telephone. The diagnosis and other inclusion criteria were confirmed by a family physician and a therapist's observation of motor status. People with major musculoskeletal problems (eg, amputation or recent joint replacement surgery) or neurological disorders in addition to stroke were excluded from the study. Nine participants were excluded because their family physician confirmed additional neurological disorders (n=2), absence of stroke (n=2), or more than one occurrence of stroke (n=5), leaving 99 participants. Participant characteristics are described in Table 1.

Sample size was calculated to determine the sample size per group (no falls, 1 fall, more than 1 fall) required for the outcome measures of BBS and gait speed. We used data from a previous study, (30) which had a cohort of subjects with a group mean of 45.3 for the BBS (SD=5.3) and a mean gait speed of 0.68 m/s (SD=0.28). Using the sample size calculation of Portney and Watkins (31) an 80% power rate, an alpha of .05, and a group difference of 3 points for the BBS and a 10% difference for gait speed, a sample of 50 individuals per group was required. We were able to achieve an adequate sample size for people with no falls (n=50), but not for people with falls (n=49). Berg Balance Scale changes of at least 2 points have been shown to result in clinically meaningful change. (30)

The Functional Disability Scale of the American Heart Association Stroke Outcome Classification (AHASOC) (32) is a scale designed to measure residual impairment and disability of stroke in the areas of basic activities of daily living (BADL) and instrumental activities of daily living (IADL). The scale consists of 5 levels (1-5), with 1 indicating independence in both BADL and IADL and 5 indicating complete dependence. The median score for our sample was 2.0. Over half of our sample were classified as either independent in all BADL and IADL or independent in all BADL with partial assistance required for IADL. Self-report information on comorbidity was collected with a checklist of 21 conditions (eg, arthritis, congestive heart failure, depression). More than 50% of our sample indicated more than 2 comorbidities. The most frequently cited comorbidities were hypertension, a history of cardiac events, and a sedentary lifestyle.

In accordance with university and hospital policies, informed consent was obtained from all participants prior to their participation in the study. Ethics approval was obtained from the local university and hospital review boards. Participants took part in a 1-hour evaluation session that consisted of a semistructured interview to obtain a falls history and a clinical examination. All testing was conducted at the rehabilitation research laboratory located in a rehabilitation hospital. The examiner was an occupational therapist (JEH) who had 9 years of clinical experience in the area of neurology and who had used the BBS and the Mini-Mental Status Exam (MMSE) in clinical and research settings.

Examination

Interview. The following information was recorded for each participant from the interview: age, sex, time since stroke, side of paresis, number of falls recalled over the past 6 months, whether the individual sought medical attention for any of the falls, and whether an injury resulted from any of the falls. Data for fall history were reported by participant recall. Participants were informed that a fall was defined as coming to rest on the floor or another lower level but was not due to seizure, stroke or myocardial infarction, or an overwhelming displacing force (eg, earthquake). (16) Five individuals had someone else confirm their fall history. Fall information was classified into location of fall (indoor, outdoor) and activity (eg, walking, transferring) in which the individual was involved when he or she fell.

Balance. In the studies cited for older adults, balance was examined using clinical measures such as the Ramberg test, Tinetti Fall Efficacy Scale, and Tinetti Performance-Oriented Mobility Assessment. Similarly, in the acute and subacute stages of stroke recovery, impaired balance, assessed by clinical measures, has been identified as a risk factor for falls. The BBS was used in 2 of these studies. (20,23) All of the cited studies of patients with chronic stroke and falls used a clinical tool to measure balance impairment, except for the study by Hyndman et al, (16) who did not measure balance. Of the 3 studies that involved balance, the study by Lamb et al (15) showed that impaired balance was a predictor of falls in older women with stroke, and the study by Hyndman and Ashburn (17) showed that BBS scores of people who fell were lower than scores of those who did not fall. These findings suggest that clinical measures of balance, including the BBS, are appropriate and sensitive measures to use in studies of falls in people with stroke.

Balance was measured using the BBS. (33) The BBS is a 14-item test (56 points maximum) using a 5-point (0-4) scale to rate each item, with 0 indicating an inability or need for maximal assistance to complete the task or performs task with safety concerns and 4 indicating independent and safe ability to perform task. The BBS consists of tasks such as reaching, balancing on one limb, and transferring. Concurrent validity of data for the BBS has been examined in people with stroke. Correlations with data for the Barthel Index (r =.80), the Fugl-Meyer Motor Impairment Scale (r =.62-.94), measures of pastural sway (r=.55), (33,34) and gait speed (r=.60) (29) have been found. The BBS has been shown to yield data with high interrater and intrarater reliability in elderly people when using physical therapists as testers. Initial studies by Berg and colleagues (33,35) produced intraclass correlation coefficients (ICCs) of .98 for interrater reliability and .71 to .99 for intrarater reliability in elderly people.

Gait speed. Gait difficulties have been shown to be associated with falls in older adults. (2,5,6,8) Two of the studies involving individuals with chronic stroke (15,16) examined mobility as a potential risk factor for falls.

For the assessment of gait speed, participants were asked to walk at their "most comfortable speed" (ie, self-selected pace) using their usual assistive device along an 8-m distance 3 times and then to walk "as fast as possible but safely" (ie, maximum pace) 3 times. The mean of the 3 trials (in meters per second) was calculated. Participants walked in their own shoes and used an orthosis (n=8) (eg, ankle-foot orthosis) or an assistive device (cane [n=34], walker [n=10]) if required. Infrared emitting diodes (IREDs) were attached to the distal portion of the dorsal aspect of the participants' foot and the distal aspect of the Achilles tendon. An optoelectronic sensor * was used to track the IREDs. Gait speed was calculated using the distance covered by the IREDs and the corresponding elapsed time during each gait cycle. We have previously evaluated the test-retest reliability (separate days) for 22 individuals with stroke and found an ICC of .95 for self-paced gait speed. (36)

Cognition. The MMSE is a screening tool that can be used to detect cognitive deficits in orientation, learning, calculation, abstraction, memory, language, and spatial relationships. (37) Each item is given a score of 1 (able to fully complete the task) or 0 (unable to complete the task), with a total possible score of 30. A score of below 24 is typically used to describe people who could be experiencing cognitive deficits that would interfere with daily living. The MMSE is a tool that is widely used clinically and can be administered in 5 to 10 minutes. Dick et al (38) examined the MMSE for test-retest reliability in people with neurological conditions and reported an ICC of .95, and construct validity was determined with a correlation of r=.64 with the Wechsler Memory Scale. Scores for the MMSE have been associated with scores for cognitive subscale of the Functional Independence Measure (r=.67) and the Loewenstein Occupational Therapy Cognitive Assessment (r =.59). (39)

Data Analysis

Descriptive statistics were used to describe the sample and the content of the semistructured interview. Based on fall history, participants were categorized as having 1 fall, repeated falls ([greater than or equal to] 2 falls), or no falls.

The Mann-Whitney U test was used to detect mean differences between groups for the continuous variables of age, gait speed, MMSE score, and BBS score because these variable distributions were not normal. The chi-square test was used to detect proportional differences for the dichotomous variables of sex and side of paresis.

Bivariate correlations were produced using the Pearson product moment correlation to measure the strength of the association among continuous variables and to determine variable entry into the model; redundant (ie, highly correlated) variables were removed. Binary logistic regression was used to determine potential risk factors for falls, with 95% confidence intervals calculated for each of the independent variables entered into the model. This type of regression is considered appropriate for use when there is a combination of continuous and categorical predictor variables. (40) Based on the results of similar studies of people with chronic stroke (12,15-17) and studies involving elderly people, (2,5-10,11) the model was determined by blocked entry of all variables of interest (BBS score, self-selected gait speed, MMSE score), controlling for age and sex by first entry. Our statistics and model did not include the number of falls (continuous variable) per person because we had a small number of individuals with large numbers of falls (eg, 10), which would tend to overinflate the correlations. We used SPSS statistical software 11.0 for Windows ([dagger]) in the analysis. A value of P [less than or equal to] .05 was considered significant in all comparisons. All statistical testing was 2-sided. Variable entry for the regression was set at .05, and removal was set at .10.

Results

A total of 117 falls were recorded. Forty-nine participants (50%) experienced at least one fall over the past 6 months (Tab. 2). Falls that occurred indoors accounted for 56% of the 117 falls, with 62% of the indoor falls transpiring within the home. Falls that occurred outside totaled 39%. We were unable to classify location for 7 (5%) of the reported falls. The most frequent activity at the time of the fall was walking (51%). Further details regarding fall circumstance are shown in Table 2. Of the 49 participants who reported a fall, 20 (41%) reported an injury, with 17 (85%) of those seeking medical attention. Women appeared to be more likely than men to be injured by a fall ([chi square]=3.6, P=.06, likelihood ratio=3.5) in the group of subjects who fell once.

Age, BBS score, gait speed, and MMSE score did not differ between people who fell once and those who had not fallen (Tab. 1). Chi-square tests showed no group differences for sex or side of paresis between those who did not fall and those who fell once. Of the 9 individuals with MMSE scores below 20, 4 had fallen once and 5 had not fallen. These individuals were not more likely to fall compared with individuals with an MMSE score above 20.

There was no group difference on variables of age, BBS score, gait speed, and MMSE score between subjects who fell more than once and those who had not fallen (Tab. 1). Chi-square tests showed no group differences for sex or side of paresis between subjects who fell more than once and those who had not fallen.

Significant bivariate correlations were found between BBS score and MMSE score (r=.24, P<.05), BBS score and self-selected gait speed (r=.74, P<.01), BBS score and fast-paced gait speed (r=.70, P<.01), and self-selected gait speed and fast-paced gait speed (r=.90, P<.01). Due to the strong relationship between self-selected gait speed and fast-paced gait speed, only self-selected gait speed was included in the regression model. A scatterplot of BBS scores against number of falls illustrates the relationship between the 2 variables (Figure).

[FIGURE OMITTED]

When all 5 variables (age, sex, BBS score, gait speed, and MMSE score) were block entered into the regression, no significant model was produced for participants who fell once ([[chi square].sub.(5)]=5.20, P=.39) or those who fell more than once ([[chi square].sub.(5)]=1.90, P=.86) (Tab. 3).

Discussion and Conclusions

The purpose of this study was to determine whether balance and gait speed could explain falls in individuals with chronic stroke. The circumstances and characteristics of falls in this sample also were described. We found that falls are a common occurrence in older adults with chronic stroke. One half of our sample reported at least one fall in the past 6 months. In addition, fall-related injuries were common, although serious injury was less frequently reported in our sample. We found that more than one half of falls occurred in the home during walking activities.

In comparison with older adults (age [greater than or equal to] 65 years) living in the community, our sample had a much higher rate of fall incidence. Studies of older adults (3,18,19) have shown 6-month fall rates of 20% to 35%, whereas our sample showed a 6-month fall rate of 50%. These studies (3,18,19) examined falls in older adults, and individuals with stroke accounted for less than 15% of the samples. However, in studies where history of stroke was included in the analyses, stroke was identified as a significant correlate of fall incidence. (2,3,5,6,10,11) In addition, investigators in many studies of falls in community-dwelling older adults cited impaired balance (5-10) and cognition (3,10) as factors in fall incidence and risk; these factors can be residual impairments of stroke. These factors may explain why our participants' fall incidence rate was higher than in general samples of older adults. The percentage of our participants who fell and sought medical attention (88%) also was higher than in Blake's study (41) of the epidemiology of falls in community-living older adults, whose rate of fall incidence was 5%.

Our measures of balance and gait speed were not able to explain falls in this population or to discriminate between those who had not fallen, had fallen once, or had fallen more than once. Our inability to find differences between participants who had fallen and those who had not fallen may have been due to insufficient power. We calculated needing a sample size of 50 people in each group, and although we were able to achieve an adequate sample size for participants who had not fallen, we did not achieve an adequate sample size for those who had fallen (n=49). Studies involving elderly people without stroke have shown that age, (6,9,11) sex, (5,11) balance, (6-10) ability to perform ADL, (2,5,6) and cognitive scores (3,10) are predictors of fall risk. Several predictors of falls also are known for individuals in the acute and subacute phases of stroke recovery, but determining fall risk in the chronic stage remains difficult. Stroke can affect different functions (motor, sensory, cognitive) within an individual independently or in combination, leaving people with different severity levels of residual impairment and varying compensatory strategies. With this level of outcome diversity, an individualized approach to fall risk may account for impairment variability and be more effective in fall prevention than group prediction models.

We hypothesized that BBS scores would be associated with fall incidence and be a risk factor for falls in people with chronic stroke. Our results did not support this hypothesis. Given the surprising results from our planned analyses, we examined our data further to determine possible reasons for these findings. We noted that all of the subjects who had a low BBS score (cutoff score of [less than or equal to] 45 suggested by the developers of the scale (34) for fall risk) and either no falls or one fall were participants who used a wheelchair (n=9). All of our subjects were ambulatory, but these 9 individuals described using a wheelchair for outdoor mobility or for long distances, and walking mainly in the home or for short distances. A post hoc analysis revealed that when the data for these 9 subjects were excluded from the data set, a low relationship between the BBS scores and falls was apparent (r=.37, P<.01). Further analysis showed that subjects who used a 4-wheel walker (n=11) also had low fall incidence ([less than or equal to] 1); however, of those who used a cane, 13 (37%) had [greater than or equal to] 2 falls, with some having as many as 10 falls. Subjects who used a cane and had [greater than or equal to] 2 falls scored [less than or equal to] 45 on the BBS. Thus, those individuals who had a low BBS score (indicating risk for falling of [less than or equal to] 45) but who used either a walker or a wheelchair did not appear to be at risk for falling. We believe that it would be useful in the future to assess the impact of mobility aids on fall risk because our post hoc observations suggest that the prescription of mobility aids, especially 4-wheel walkers, may reduce fall risk in individuals with a BBS score of [less than or equal to] 45.

Clinical measures of function as predictors of falls in people with chronic stroke remain elusive. Factors such as vestibular function, sensation, perception, and home environment have not been assessed in this population and may add important information for fall risk. Berg et al (33) defined 3 components of balance: static (maintenance of posture), dynamic (adjustment to voluntary movement), and reactive (reaction to external forces).

The majority of falls were reported during walking, an activity that requires dynamic and reactive balance. The measure we used to evaluate balance tests static balance (eg, standing, sitting unsupported), with some activities requiring dynamic balance (eg, turning 360[degrees], transferring, placing alternate feet on stool) and to a lesser degree reactive balance. It may be that the BBS does not test the domains of balance required to prevent or successfully recover from a fall, which may indicate that a more sensitive measure of balance, including large components of reactive and dynamic balance, is necessary. Researchers may need to test situations in which people are required to elicit reactive balance in response to externally imposed perturbations such as recovering from a push to the body, platform perturbation, or a tripping paradigm (a tripping paradigm is constructed using obstacle placement or the sudden introduction of an obstacle that could produce tripping if the person is unable to negotiate the obstacle). Furthermore, the interaction of people within their environment, such as approaching a curb and stepping up over it, may be critical.

Our participants were a volunteer, community-based sample, which could bias results because they may be more mobile and cognitively intact than people living in an institution; however, a wide range of balance and mobility impairment was evident from the data. We also relied on participant recall for fall history. Some authors (42,43) have suggested that this method of information gathering can produce recall bias and negatively affect results. We attempted to control for recall bias by having a caregiver or spouse present, if necessary, for confirmation.

The clinical information (balance, mobility, and cognitive status) collected at the time of the study may have been different than at the time of the fall. During the 6-month time frame used for fall history (March-August), participants could have experienced an illness (eg, flu, cold) or an exacerbation or worsening of an existing condition (eg, arthritis, dementia that could have negatively influenced their functional status at the time of the fall(s). However, at the time of examination, these issues may have been controlled or resolved. In contrast, participants might have been in worse physical or mental condition at the time of the examination than at the time of the fall(s). Activity level also may have fluctuated during the 6-month period, because some individuals might be more prone to indoor activities during the winter months and more active outside during the summer months.

We did not use a measure of activity level in our study. Our sample may have been sedentary given the residual effects of stroke. However, our measure of functional status showed that participants were independent in BADL and most IADL and were ambulatory. We believe that future studies should use a measure of activity level, as well as measures of vision, sensation, and medication use.

Clinical Implications

The results of our study suggest that people with chronic stroke are a subgroup of older adults who are at risk for falls. Because neither the BBS score nor gait speed were able to explain falls, clinicians may be left wondering how to assess fall risk. The majority of falls in our study and in other studies (12,16) of stroke were reported during walking, an activity that requires dynamic and reactive balance. We recommend that clinicians examine individuals in situations requiring reactive balance, such as recovering from a push to the body, a tripping paradigm, or negotiating a curb. Examining individuals in their community and home environments also may provide valuable insight into potential hazards or difficulties and may provide physical therapists with information about the need for mobility aids in different environments (eg, home, long distances, shopping, negotiation of different terrain). In addition, clinicians should not rely on only one assessment but rather a battery of assessments that include physical, mental, and environmental factors in attempting to examine which individuals may fall and to prevent future falls.

The issue of mobility aids may be of particular importance in people with chronic stroke. Our observations suggest that the prescription of 4-wheel walkers for people with a low BBS score ([less than or equal to] 45) may reduce the risk of falls. Of particular interest were individuals who used a cane and fell at high rates. It is possible that these individuals were assessed at discharge for a cane but have since deteriorated and might benefit from a re-evaluation of mobility status. Further research regarding mobility aids and their association with falls and fall risk would be of importance to clinicians in the area of stroke rehabilitation.

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* Northern Digital, 103 Randall Dr, Waterloo, Ontario, Canada N2V 1C3.

([dagger]) SPSS Inc, 233 Wacker Dr, Chicago, IL 60606.

JE Harris, OT, MSc, is a graduate student in the School of Rehabilitation Sciences, University of British Columbia, Vancouver, British Columbia, Canada, and in the Rehabilitation Research Laboratory, GF Strong Rehab Centre, Vancouver, British Columbia, Canada.

JJ Eng, PT/OT, PhD, is Associate Professor, School of Rehabilitation Sciences, University of British Columbia, T325-2211 Wesbrook Mall, Vancouver, British Columbia, Canada V6T 2B5 ([e-mail]), and Scientist, Rehabilitation Research Laboratory, GF Strong Rehab Centre. Address all correspondence to Dr Eng.

DS Marigold is a graduate student in the Department of Neuroscience, University of British Columbia, and in the Rehabilitation Research Laboratory, GF Strong Rehab Centre.

CD Tokuno, MSc, is Research Coordinator, Rehabilitation Research Laboratory, GF Strong Rehab Centre.

CL Louis is a student in the School of Rehabilitation Sciences, University of British Columbia, and a research assistant in the Rehabilitation Research Laboratory, GF Strong Rehab Centre.

Ms Harris, Dr Eng, and Mr Marigold provided concept/idea/research design and writing. Mr Tokuno and Ms Louis provided data collection, and Ms Harris, Mr Tokuno, and Ms Louis provided data analysis. Dr Eng provided project management.

This research was presented at the School of Rehabilitation Sciences, University of British Columbia, Research Symposium, May 11, 2004.

This study was supported by an operating grant (MOP-57862) from the Canadian Institute of Health Research to Dr Eng and by a career scientist award to Dr Eng from the Canadian Institute of Health Research and the Michael Smith Foundation for Health Research.

Table 1.

Participant Characteristics (N=99) and Mann-Whitney Test Comparisons
Between Those Who (1) Did Not Fall and Fell Once and (2) Did Not Fall
and Fell More Than Once (a)

Variable                   n         [bar.X]   SD     Range

Sex (M/F)                  67/32
Age (y)                              69.0      9.5      50-93
Affected side (L/R/N)      49/47/3
Time since stroke (y)                 4.0      4.3       1-24
MMSE                                 27.0      4.0      16-30
BBS                                  44.0      7.7      20-56
Gait self-selected (m/s)              0.63     0.29   0.06-1.3
Gait fast paced (m/s)                 0.89     0.37   0.12-1.7

                                                       More Than
                           No Falls   One Fall         One Fall
Variable                   (n=49)     (n=21)     P     (n=29)      P

Sex (M/F)
Age (y)                    67.6       72.4       .06   68.0        .86
Affected side (L/R/N)
Time since stroke (y)       5.6        4.3       .20    5.0        .74
MMSE                       25.5       25.3       .83   25.7        .80
BBS                        44.9       43.4       .46   43.2        .34
Gait self-selected (m/s)    0.67       0.60      .35    0.60       .26
Gait fast paced (m/s)       0.91       0.84      .53    0.80       .17

(a) M/F=male/female, L/R/N=left/right/unable to detect a weaker side,
BBS=Berg Balance Scale (0-56), MMSE=Mini-Mental Status Exam (0-30).

Table 2.

Fall Incidence, Location, Circumstance, and Injury by Faller
Classification

                                             More Than
                             One Fall        One Fall

                             N     %         N     %

Fall incidence and injury    21    43 (a)    28    57 (a)
  Fracture                    0               3
  Soft tissue                 3               1
  Cuts                        1               1
  Bruising                    5               6
  No injury reported         12              17
Fall circumstances           21              96
  Location
    Indoor                   13    62 (b)    52    54 (c)
    Outdoor                   5    24        40    42
    Unknown                   3    14         4     4
  Activity
    Walking                  10    48        58    60
    Standing (turning)        4    19        14    15
    Transferring              5    24        14    15
    Stairs                    2    10         3     3
    Unknown                                   7     7

(a) Percentage derived by total number of participants who fell (n=49).

(b) Percentage derived by total number of falls (n=21) by participants
who fell once.

(c) Percentage derived by total number of falls (n=96) by participants
who fell more than once.

Table 3.

Logistical Regression: Risk Factors for Participants Who (1) Fell Once
and (2) Fell More Than Once (a)

                      One Fall

Risk Factor           P      Odds Ratio    95% CI

Age                   .05    1.1            1.0-1.1
Sex                   .32    2.1           0.50-8.4
BBS score             .74    1.1           0.91-1.1
Gait self-selected    .41    0.3           0.02-5.2
MMSE score            .81    1.0           0.89-1.2

                      More Than One Fall

Risk Factor           P      Odds Ratio    95% CI

Age                   .88    0.99          0.95-1.1
Sex                   .39    0.63          0.22-1.8
BBS score             .77    1.0           0.92-1.1
Gait self-selected    .39    0.34          0.03-6.4
MMSE score            .99    1.0           0.87-1.2

(a) CI=confidence interval, BBS=Berg Balance Scale, MMSE=Mini-Mental
Status Exam.