FIND Disability Statistics
American Community Survey (ACS)
- Prevalence
- Employment Rate
- Not Working but Actively Looking for Work
- Full-Time / Full-Year Employment
- Annual Earnings
- Annual Household Income
- Poverty
- Supplemental Security Income (SSI)
- Educational Attainment
- Veterans Service-Connected Disability
- Health Insurance Coverage (and Type)
Current Population Survey (CPS)
EEOC Charge Data
Report Dashboard
ACCESS Research
Tools
Rehabilitation Dataset Directory: Dataset Profile
Dataset: Medical University of South Carolina Stroke Data (ARRA)
Basic Information | |
---|---|
Dataset Full Name | Medical University of South Carolina Stroke Data |
Dataset Acronym | ARRA |
Summary | The Medical University of South Carolina Stroke Data (ARRA*) was a NIH funded study conducted in 2011-12. It was designed to delineate the cause/effect relationship between neural output and the biomechanical functions executed in walking. Subjects included 27 post-stroke patients (at least 6 months post-stroke) and 17 healthy controls. Each subject walked on a treadmill at their self-selected walking speed as well as completing a randomized set of four steady-state mobility capability tasks: walking at maximum speed, and walking at self-selected speed with maximum cadence, maximum step length, and maximum step height. Kinematic, kinetic, and electromyography (EMG) data were collected. The data collected allow scientists interested in EMG analyses of hemiparetic walking to have a test set for their analyses. The data collected includes demographics, clinical assessments, kinetic (from treadmill force plates), kinematic (from active markers), EMG and Over-ground spatial temporal measures (GaitRite Platinum Walkway). * Documentation refers to the study as ARRA: American Recovery and Reinvestment Act |
Key Terms | Stroke, Post-stroke, Mobility, Gait, Walking, Kinematic, Kinetic, Electromyography (EMG), Treadmill, Motion capture system |
Study Design | Cross-Sectional |
Data Type(s) |
Administrative Clinical |
Sponsoring Agency/Entity | Department of Health and Human Services (HHS), National Institutes of Health (NIH), the Department of Veterans Affairs, and the National Science Foundation |
Health Conditions/Disability Measures |
Health Condition(s) | Stroke |
Disability Measures | Ambulatory disability |
Measures/Outcomes of Interest |
Topics | Stroke, Post-stroke mobility, Walking, Walking speed, Electromyography (EMG), Video motion capture |
Sample |
Sample Population | Males and Females, healthy and 6+ months post stroke, ages 40-80, living in the Southeastern United States.
|
Sample Size/Notes | 44 subjects total:
|
Unit of Observation | Individual/Patient |
Continent(s) | North America |
Countries | United States |
Geographic Coverage | Southeastern United States (South Carolina) |
Geographic Specificity | NA |
Special Population(s) | Aging/Older people (ages 40-80) |
Data Collection |
Data Collection Mode | Multiple modes of data collection:
|
Years Collected | 2011-12 |
Data Collection Frequency | Single time period data collection |
Strengths and Limitations |
Strengths | Well documented and extensive data collection. Data includes both post stroke patients and control individuals allowing for comparisons. The number of subjects and data collected provides researchers interested in EMG analyses of hemiparetic walking with a test set for analyses. |
Limitations | From related paper (Routson et.al 2014) https://doi.org/10.14814/phy2.12055 : "Due to our limited recording of EMG from eight muscles, we were only able to identify four modules during healthy control walking... our methods did not include an analysis to determine whether a particular subject's muscle strength was sufficient to perform the mobility capability tasks." |
Data Details |
Primary Website | |
Data Access |
Download Restricted Data Use Agreement here: https://www.icpsr.umich.edu/icpsrweb/ADDEP/studies/37122/datadocumentation |
Data Access Requirements | Data Use agreement, No cost (Restricted Data Use Agreement) |
Summary Tables/Reports | See the following publication: Routson, Rebecca L., Kautz, Steven A., Neptune, Richard R. (2014) Modular organization across changing task demands in healthy and poststroke gait. Physiological Reports. 2, (6), e12055. |
Data Components | NA |
Similar/Related Dataset(s) | Stroke focused studies: |
Selected Papers |
Other Papers | Routson, Rebecca L., Kautz, Steven A., Neptune, Richard R. (2014) Modular organization across changing task demands in healthy and poststroke gait. Physiological Reports. 2, (6), e12055. |
Technical | Main documentation page: https://www.icpsr.umich.edu/icpsrweb/ADDEP/studies/37122/datadocumentation Includes the following:
|
Related Repositories |
Repositories |
Ask Our Researchers
Have a question about disability data or datasets?
E-mail your question to our researchers at disabilitystatistics@cornell.edu
The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).
Other CLDR supported resources and collaborative opportunities:
- Archive of Data on Disability to Enable Policy and research (ADDEP)
- Data Sharing & Archiving at CLDR
- Pilot Project Program
- Visiting Scholars Program
Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.
For questions or comments please contact disabilitystatistics@cornell.edu