Background
Stroke can result in a range of impairments which predispose an individual to falling. The incidence of community-based falls within the first six months following stroke is 37–73% [
1‐
3], and the rate of falls in chronic stroke is approximately double that of healthy controls [
4,
5]. People who have had a stroke are at a high risk of falls-related fractures [
6]. Further adverse consequences may include fear of falling and subsequent reduced activity, deconditioning, and greater falls risk [
7]. A recent systematic review identified mobility and balance variables (i.e., gait speed, timed up and go (TUG) and Berg Balance Scale) as the strongest predictors of falls after stroke [
8]. Other significant factors included medications, mood, cognition and prior history of falls.
Gait speed (e.g., 10-m walk) is a common assessment for examining falls risk following stroke [
9,
10], but it is possible that measures of movement quality during walking may assist risk assessment. Indeed, research has found that single limb support time asymmetry was an independent and strong predictor of falls after inpatient rehabilitation discharge [
11]. Measures of step variability and “smoothness” during gait were also shown to be more strongly predictive of falls post stroke than other commonly-used clinical measures [
12]. However, these studies were limited to a six-month follow-up period or were comprised of a small (
n = 40) chronic stroke cohort [
12]. Larger mediolateral pelvic displacement during walking has been demonstrated in people following stroke compared with healthy controls [
13,
14]. While this variable has not previously been investigated in relation to falls risk after stroke, smaller mediolateral trunk displacement has been found in older adults with no falls history [
15] or of the pelvis in those with worse balance [
16]. Conversely, larger pelvic displacement was found to be predictive of falls in Parkinson’s disease [
17].
There is currently no single balance test shown to be a superior predictor of falls following stroke. The Berg Balance Scale is a frequently used assessment for identifying post-stroke falls risk [
1,
9]. Nonetheless, this test includes multiple items examining different aspects of balance performance and does not reveal which of these factors are more strongly reflective of risk [
18]. Indeed, the Berg Balance Scale has also demonstrated poor falls prediction after stroke and is recommended for use in combination with other measures [
19]. The TUG has shown to be predictive of falls after stroke [
9], while the dual-task TUG has demonstrated superior predictive ability to the standard test in Parkinson’s disease [
20]. Research in elderly cohorts has demonstrated variable findings, with a significant difference between fallers and non-fallers only for the standard TUG [
21], equivalence between the two tests [
22], and significance for falls prediction only for the cognitive dual-task TUG [
23]. Prior research has also supported the use of the step test to predict post-stroke falls following inpatient discharge [
1]. Static standing postural sway using a force platform has shown to differentiate post-stroke fallers [
11,
24] or predict falls [
25,
26], with findings tending to favour mediolateral variables [
11,
26]. However, prior research has typically not included a comparison between a range of clinical and instrumented measures of balance and mobility to compare their relative strength for falls prediction. For example, studies have included only two clinical balance tests [
1,
9] or only instrumented variables [
26].
The current study aimed to comprehensively examine multiple aspects of gait and balance in relation to prospective falls after stroke over a 12-month period following inpatient rehabilitation discharge. Specifically, we aimed to identify which aspects of gait and balance were strongly associated with falls and whether instrumented variables, derived from using relatively accessible technologies, could add value to the standard clinical tests.
Results
Of 96 individuals recruited, 81 (
n = 25 Australia,
n = 56 Singapore) completed baseline testing and 12 months prospective falls follow up. The reasons for loss of follow-up were voluntary withdrawal (
n = 7), unable to contact or move overseas (
n = 5), and death or further serious medical event (
n = 3). There were no significant differences between those lost to follow-up and those retained in terms of baseline characteristics or clinical tests (6mWT, TUG and step test;
P > 0.05). Participant characteristics are presented in Table
1. Over 12 months post discharge, 23/81 (28%) individuals fell at least once and 13/81 (16%) fell more than once. Significant differences between fallers and non-fallers were observed for the Modified Rankin Scale, Short Falls Efficacy Scale – International, prior stroke and falls in the 12 months preceding stroke (
P = 0.008–0.046).
Table 1
Participant baseline characteristics and between-group differences
Demographics |
Country (Singapore) | 56 (69.1%) | 42 (72.4%) | 14:9 (60.9%) | 3 | 0.310 |
Age | 62.99 ± 13.22 | 62.83 ± 12.29 | 63.39 ± 15.63 | 1 | 0.864 |
Sex (male) | 43 (53.1%) | 31 (53.4%) | 12 (52.2%) | 3 | 0.917 |
Stroke details |
Time since stroke (days) | 24.0 (20.0–34.5) | 23.0 (20.0–33.3) | 24.0(22.0–36.0) | 2 | 0.515 |
Side of stroke lesion (right) | 39 (48.1%) | 27 (46.6%) | 12 (52.2%) | 3 | 0.771 |
Type of stroke (infarct) | 64 (79.0%) | 46 (79.3%) | 18 (78.3%) | 3 | 0.917 |
Functional measures, comorbidities and falls history |
Modified Rankin Scale (0–6) | 2.83 ± 0.89 | 2.67 ± 0.91 | 3.22 ± 0.74 | 2 | 0.013* |
FIM (18–126) | 105.0 (93.0–116.5) | 105.5 (94.0–118.0) | 103.0 (92.0–115.0) | 2 | 0.753 |
MoCA (0–30) (n = 78) | 25.0 (23.0–28.0) | 25.0 (23.0–27.0) | 26.0 (20.8–28.0) | 2 | 0.902 |
Inattention, star cancellation test < 44 (yes) (n = 80) | 15 (18.8%) | 11 (19.3%) | 4 (17.4%) | 3 | 0.843 |
HADS (0–21) (n = 77) | 7.0 (3.0–12.0) | 7.0 (3.0–12.5) | 7.0 (3.0–11.5) | 2 | 0.977 |
Short FES-I (7–28) | 10.0 (8.0–15.0) | 9.5 (7.0–14.0) | 13.0 (10.0–19.0) | 2 | 0.013* |
Gait aid use (yes) | 19 (23.5%) | 11 (19.0%) | 8 (34.8%) | 3 | 0.130 |
Assistance for gait (yes) | 16 (19.8%) | 10 (20.8%) | 6 (26.1%) | 3 | 0.367 |
FCI (0–18) | 1.60 ± 0.74 | 1.52 ± 0.71 | 1.83 ± 0.78 | 2 | 0.064 |
Prior stroke | 5 (6.2%) | 1 (1.7%) | 4 (17.4%) | 3 | 0.008* |
Inpatient falls (yes) | 15 (18.5%) | 8 (13.8%) | 7 (30.4%) | 3 | 0.082 |
≥1 fall in 12 months preceding stroke (yes) | 19 (23.5%) | 10 (17.2%) | 9 (39.1%) | 3 | 0.046* |
Falls details
Two participants each reported over 30 falls; one always when standing up from a chair and holding on to a walking frame, and the other always during community walking or climbing up or down stairs. No medical attention was sought for these falls. Apart from these two participants and missing details for 11/45 falls, 47% of falls occurred inside the home, 32% were related to going up or down stairs, and 24% occurred during walking. Six participants (7/42 falls) sought medical attention post fall but only one resulted in hospital admission for a shoulder fracture.
Gait variables
Neither comfortable (stopwatch-derived) or fast (Kinect-derived) gait speed was significantly different between the faller and non-faller groups (Table
2). The faller group demonstrated significantly smaller stride length, gait speed variability, and mediolateral and vertical pelvic displacement. After adjusting for country, prior falls and assistance, significant predictors of falls were mediolateral pelvic displacement (IQR-OR = 7.85), stride length (IQR-OR = 4.23) and step length asymmetry (IQR-OR = 1.37). In regression models that also included the 6mWT as a covariate, only mediolateral pelvic displacement remained significant, with one IQR reduction in displacement (i.e., 5.38 cm versus 7.22 cm) indicating 6.75 times greater odds of falling.
Table 2
Between-group differences and adjusted regression analyses
Gait variables
|
6mWT-comfortable, m/s (n = 57:23) | 0.78 ± 0.36 | 0.61 ± 0.32 | 0.075 | 0.45–0.98 | 2.10 (0.90–4.89)b | 0.086 | N/A | N/A |
Kinect-fast walk, m/s (n = 56:23) | 0.93 ± 0.38 | 0.76 ± 0.37 | 0.106 | 0.57–1.16 | 1.89 (0.78–4.58)b | 0.157 | N/A | N/A |
Stride length, m (n = 54:20) | 1.03 ± 0.26 | 0.87 ± 0.31 | 0.043* | 0.76-1.22 | 4.23 (1.14–15.66)b | 0.031* | 1.97 (0.26–14.79)b | 0.511 |
Cadence, steps/min (n = 54:20) | 107.84 ± 23.50 | 95.48 ± 31.61 | 0.114 | 91.41–122.08 | 2.48 (0.96–6.41)b | 0.060 | 1.46 (0.45–4.81)b | 0.530 |
Step width, m (n = 54:21) | 0.15 ± 0.04 | 0.15 ± 0.04 | 0.943 | 0.12–0.17 | 1.19 (0.56–2.52)b | 0.649 | 1.43 (0.65–3.13)b | 0.376 |
Step length asymmetry, ratio (n = 53:20)c | 1.15 ± 0.16 | 1.54 ± 1.10 | 0.458 | 1.06–1.16 | 1.37 (1.01–1.85) | 0.040* | 1.28 (0.96–1.69) | 0.090 |
Gait speed variability, m/s (n = 56:23)c | 0.17 ± 0.09 | 0.13 ± 0.04 | 0.048* | 0.11-0.18 | 1.82 (0.78–4.26)b | 0.169 | 1.24 (0.46–3.37)b | 0.670 |
ML pelvic displacement, cm (n = 55:23) | 7.00 ± 1.26 | 5.24 ± 1.39 | < 0.001* | 5.38-7.22 | 7.85 (2.56–24.05)b | < 0.001* | 6.75 (2.20–20.74)b | 0.001* |
Vertical pelvic displacement, cm (n = 55:23) | 3.42 ± 1.25 | 2.83 ± 0.96 | 0.047* | 2.26-4.10 | 2.03 (0.74–5.56)b | 0.168 | 1.25 (0.36–4.31)b | 0.724 |
Balance variables |
TUG - normal, s (n = 58:23)c | 16.49 ± 9.26 | 27.61 ± 23.84 | 0.014* | 11.30-22.56 | 4.06 (1.44–11.46) | 0.008* | 7.84 (1.42–43.26) | 0.018* |
TUG - dual task, s (n = 54:19)c | 21.47 ± 14.60 | 24.22 ± 3.06 | 0.148 | 11.91–25.44 | 1.30 (0.50–3.41) | 0.589 | 1.38 (0.35–5.39)b | 0.647 |
Step test - affected, taps/15 s (n = 58:23) | 10.19 ± 5.08 | 6.35 ± 4.20 | 0.005* | 6.00-12.50 | 4.50 (1.64–12.35)b | 0.004* | 5.28 (1.46–19.16)b | 0.011* |
Step test - less affected, taps/15 s (n = 58:23) | 8.95 ± 4.81 | 4.74 ± 3.44 | < 0.001* | 4.50-10.00 | 6.98 (2.06–23.61)b | 0.002* | 10.29 (2.26–46.84)b | 0.003* |
COP vel EO total, cm/s (n = 57:22)c | 1.52 ± 0.65 | 1.96 ± 1.13 | 0.186 | 1.14–1.36 | 1.26 (0.61–2.61) | 0.527 | 1.29 (0.61–2.76) | 0.505 |
COP vel EO ML, cm/s (n = 57:22)c | 0.68 ± 0.35 | 0.83 ± 0.50 | 0.193 | 0.43–0.86 | 1.40 (0.70–2.80) | 0.345 | 1.31 (0.64–2.69) | 0.458 |
COP vel EO AP, cm/s (n = 57:22)c | 1.20 ± 0.55 | 1.59 ± 1.00 | 0.128 | 0.89–1.40 | 1.18 (0.64–2.17) | 0.590 | 1.24 (0.66–2.34) | 0.508 |
COP vel EC total, cm/s (n = 56:22)c | 2.11 ± 0.94 | 2.75 ± 1.64 | 0.172 | 1.45–2.86 | 1.17 (0.49–2.78) | 0.725 | 1.20 (0.49–2.93) | 0.687 |
COP vel EC ML, cm/s (n = 56:22)c | 0.79 ± 0.37 | 0.99 ± 0.53 | 0.127 | 0.52–1.10 | 1.63 (0.60–4.44) | 0.338 | 1.48 (0.53–4.14) | 0.461 |
COP vel EC AP, cm/s (n = 56:22)c | 1.78 ± 0.84 | 2.35 ± 1.54 | 0.230 | 1.21–2.32 | 1.06 (0.49–2.32) | 0.882 | 1.12 (0.50–2.50) | 0.788 |
When all participants requiring physical assistance or gait aids (
n = 24) were excluded from analysis, between-group differences were significant for stride length, gait speed variability, mediolateral and vertical pelvic displacement (Additional File
1). However, mediolateral pelvic displacement was the only significant gait variable for both regression models (IQR-OR = 9.35 and 8.54). Of note, mediolateral pelvic displacement had no significant correlation (Spearman’s rho) with gait speed, cadence or step width, for the whole sample (
n = 74; absolute rho = 0.066–0.155;
P = 0.191–0.574) or when those requiring aids or assistance were removed (
n = 54; absolute rho = 0.083–0.225;
P = 0.102–0.577).
Balance variables
Fallers demonstrated significantly worse TUG and step test scores (Table
2). The same variables were significant following regression analysis adjusted for country, prior falls and assistance, and when the 6mWT was added as a covariate. One IQR increase in TUG scores indicated between 4.06–7.84 times greater falls risk. One IQR decrease in step test scores was associated with increased odds of falling of between 4.06–10.29.
Discussion
The range of gait variables assessed in the current study revealed that a reduction in mediolateral pelvic displacement during fast-paced walking was the strongest predictor of falling following discharge from inpatient stroke rehabilitation. Mediolateral pelvic displacement was superior to, and independent of, a commonly used clinical measure of gait speed for predicting falls. However, mediolateral pelvic displacement is currently not easily and accurately quantifiable in clinical practice due to the technology required for assessment. The step test and TUG were more strongly predictive of falls than static balance variables or the standard measure of gait speed.
The faller group demonstrated smaller mediolateral pelvic amplitudes than the non-faller group which more closely approximated normal values (i.e., between 4 and 5 cm) [
48]. Conversely, research has indicated greater lateral pelvic displacement in people following stroke compared with healthy controls [
13,
14] and moderate strength negative correlations with gait speed [
13,
49]. The smaller displacement of the pelvis in the frontal plane during walking in the faller group may reflect a compensatory or cautious movement strategy where the centre of mass is kept well within the boundary of the base of support to minimise lateral forces and increase stability. This is supported by research demonstrating reduced weight transfer to the paretic limb during walking in people with stroke [
49,
50]. Difficulty in controlling lateral stability has been identified as a major contributor to falls in older adults [
51] and research has shown smaller mediolateral trunk displacement in elderly individuals with a falls history [
15].
Walking speed, stepping pattern or use of aids may have influenced movement at the pelvis. Interestingly, in the current study step width was not different between the faller groups and mediolateral pelvic displacement had no correlation with gait speed, cadence or step width. Although individuals needing aids or assistance were not excluded from testing in the current study, reduced mediolateral pelvic displacement was a significant predictor of falls despite assistance being included as a covariate or when these individuals were removed from the analyses. Indeed, prior research in a chronic stroke cohort has shown no significant effect of gait aid use on lateral pelvic displacement during walking [
49]. Research suggests gait speed may increase with the use of aids [
52] but others have found minimal impact on velocity, cadence or step symmetry [
53,
54]. A study involving healthy adults demonstrated relatively low reliability and accuracy for Kinect-derived mediolateral pelvic displacement [
36]. Measurement error in this variable could have led to over- or underestimation of the odd ratios [
55]. Therefore, caution must be used when interpreting the findings of the current study. Research with a larger cohort is necessary to further explore the relationship between mediolateral pelvic displacement and falls, and the potential efficacy of training approaches targeting lateral weight transference to reduce falls risk.
In contrast to prior research [
8], comfortable gait speed was not a significant predictor of falls. However, Harris et al. (2005) found that neither slow or fast gait speed measures discriminated between fallers and non-fallers in a chronic stroke cohort [
56] and similar findings were seen for community-dwelling older adults [
57]. Despite the faller group having slower gait speeds and a fast-paced speed which was similar to the comfortable speed of the non-faller group, the findings were not statistically significant. Significant predictive strength for stride length was demonstrated and this easily-assessed outcome may be superior to gait speed for evaluating falls risk. Step length asymmetry also warrants further investigation as this was found to be predictive of falls, though not independent of gait speed.
Dynamic balance assessments were better predictors of falls than were static measures. In contrast to the current study, a large study in older adults found no additive value of the TUG over gait speed for predicting falls [
58]. However, this prior study involved a higher functioning cohort. The non-significant results for the dual-task TUG in the current study may have also been influenced by missing data from those unable or refusing to perform the test (
n = 8). These participants were likely to have worse performance and their inclusion may have led to more significant findings. Although the use of aids or assistance was accounted for as a covariate in the regression analyses and remained significant with those individuals removed, research has suggested gait aid use is associated with improved performance of the TUG [
59].
Previous research has similarly supported the use of the step test for falls prediction following inpatient stroke rehabilitation [
1]. This test involves effective lateral weight transference onto the affected limb when it is in the stance position, and adequate clearance when tapping. This easy-to-implement test is therefore recommended as an important inclusion in the clinical assessment of falls risk post stroke.
None of the static balance COP velocity variables were significantly associated with falls. Static balance tasks are less reflective of activities where falls occur, such as during transfers and walking. Nonetheless, prior research has shown significant differences between fallers and non-fallers for mediolateral velocity SD and total COP sway area [
11] and between non-fallers and repeat fallers for mediolateral and anteroposterior COP velocity [
24]. While it is difficult to compare COP outcomes with these studies due to differences in equipment and analyses, the former cohort had slower gait speeds than the current study [
11] and the latter was a chronic stroke sample. Another study investigating post-stroke falls following inpatient rehabilitation discharge, found root-mean-square COP variables to not be predictive of falls (versus no falls), but the mediolateral variable was significant for predicting increased fall rates when covariates were not controlled for [
25]. Research in older adults has also suggested force platform measures of lateral control have predictive strength for falls [
60].
The rate of falls in the current study (i.e., 28%) was lower than that previously reported in the literature [
61]. This may be due to the inclusion of more highly functioning individuals who were able to walk with no more than minimal assistance, attending inpatient rehabilitation and discharged home.
There were some limitations associated with the methodology adopted in our study. Although participants were assessed at different time points post stroke, all were within the subacute window of recovery (i.e., less than three months post stroke) and assessment prior to discharge was selected as a clinically relevant timepoint for evaluating future falls risk. The findings may also have been influenced by loss of data for some participants mainly due to inability to complete the tests or technical issues. However, these represented a small proportion (typically < 5%) of the total participant numbers. The Kinect has a relatively small capture field of between 1.8–4.0 m from the camera. It would be useful to employ technologies which provided data over a larger number of steps and examine gait variables derived from comfortable-paced walking, as faster walking may result in more normal values for some gait variables [
62]. Further, the Kinect was unable to accurately collect aspects of gait including temporal step measures [
63]. Nonetheless, the depth-sensing technology used by the Kinect and other similar devices is recommended as a relatively accessible means of obtaining more detailed information on walking performance in clinical settings [
64].