Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 402-409 |
| Number of pages | 8 |
| Journal | Journal of Rehabilitation Medicine |
| Volume | 49 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2017 |
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Keywords
- Accelerometry
- Cerebrovascular accident
- Fall prediction
- Gait
- Prospective falls
- Stroke
Cite this
}
Do clinical assessments, steady-state or daily-life gait characteristics predict falls in ambulatory chronic stroke survivors? / Punt, Michiel; Bruijn, Sjoerd M.; Wittink, Harriet; van de Port, Ingrid G.; Van Dieën, Jaap H.
In: Journal of Rehabilitation Medicine, Vol. 49, No. 5, 2017, p. 402-409.Research output: Contribution to Journal › Article › Academic › peer-review
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
UR - http://www.scopus.com/inward/record.url?scp=85019475796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019475796&partnerID=8YFLogxK
U2 - 10.2340/16501977-2234
DO - 10.2340/16501977-2234
M3 - Article
VL - 49
SP - 402
EP - 409
JO - Journal of Rehabilitation Medicine
JF - Journal of Rehabilitation Medicine
SN - 1650-1977
IS - 5
ER -