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Serial Backward Locomotor Treadmill Training Improves Bidirectional Walking Performance in Chronic Stroke.
Oluwole O Awosika1, Dorothy Chan1, Bridget A Rizik1
1Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, United States.
Frontiers in Neurology
|April 1, 2022
Summary
Backward Locomotor Treadmill Training (BLTT) improved walking speed and step length in chronic stroke survivors. This training may enhance both backward and forward overground walking, offering a new rehabilitation approach.
Area of Science:
- Neurorehabilitation
- Biomechanics
- Motor Recovery
Background:
- Walking impairment is a significant challenge for functional independence post-stroke.
- Current rehabilitation strategies for improving post-stroke walking function are limited.
- Backward Locomotor Treadmill Training (BLTT) shows promise, but its mechanisms and effects on overground walking are unclear.
Purpose of the Study:
- To investigate the effects of serial BLTT on spatial walking parameters in chronic stroke survivors.
- To determine the relationship between backward walking training and subsequent overground forward walking performance.
Main Methods:
- Thirty-nine adults with chronic stroke (>6 months) participated in 6 BLTT sessions over 2 weeks.
- Outcome measures included changes in walking speed, step length, and center of pressure distances.
- Correlation analyses assessed the association between BLTT-induced changes and overground walking improvements.
Main Results:
- Overall improvements were observed in BLTT and overground walking speeds and step lengths.
- Enhancements in single-support center of pressure distances were also noted.
- Weak positive associations were found between BLTT improvements and overground forward walking speed.
Conclusions:
- Individuals with chronic stroke and walking impairment showed improved spatial walking measures with BLTT.
- BLTT may serve as a valuable adjunctive training method in post-stroke rehabilitation.
- Further research into BLTT's mechanisms can optimize its application.
