TY - JOUR
T1 - Do clinical assessments, steady-state or daily-life gait characteristics predict falls in ambulatory chronic stroke survivors?
AU - Punt, Michiel
AU - Bruijn, Sjoerd M.
AU - Wittink, Harriet
AU - van de Port, Ingrid G.
AU - Van Dieën, Jaap H.
PY - 2017
Y1 - 2017
N2 - Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a "fall calendar" and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily- life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
AB - Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a "fall calendar" and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily- life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
KW - Accelerometry
KW - Cerebrovascular accident
KW - Fall prediction
KW - Gait
KW - Prospective falls
KW - Stroke
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U2 - 10.2340/16501977-2234
DO - 10.2340/16501977-2234
M3 - Article
AN - SCOPUS:85019475796
VL - 49
SP - 402
EP - 409
JO - Journal of Rehabilitation Medicine
JF - Journal of Rehabilitation Medicine
SN - 1650-1977
IS - 5
ER -