A prediction model for determining over ground walking speed after locomotor training in persons with motor incomplete spinal cord injury

用于预测运动功能不全脊髓损伤患者运动训练后地面步行速度的预测模型

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Abstract

BACKGROUND/OBJECTIVE: To develop and test a clinically relevant model for predicting the recovery of over ground walking speed after 36 sessions of progressive body weight-supported treadmill training (BWSTT) in individuals with motor incomplete spinal cord injury (SCI). DESIGN: A retrospective review and stepwise regression analysis of a SCI clinical outcomes data set. SETTING: Outpatient SCI laboratory. SUBJECTS: Thirty individuals with a motor incomplete SCI who had participated in locomotor training with BWSTT. Eight individuals with similar diagnoses were used to prospectively test the prediction model. MAIN OUTCOME MEASURES: Over ground walking speed was assessed using the 10-m walking test. METHODS: The locomotor training program consisted of 36 sessions of sequential comprehensive training comprised of robotic assisted BWSTT, followed by manual assisted BWSTT, and over ground walking. The dose of locomotor training was standardized throughout the protocol. RESULTS: Clinical characteristics with predictive value for walking speed were time from injury onset, the presence or absence of voluntary bowel and bladder voiding, a functional spasticity assessment, and over ground walking speed before locomotor training. The model identified that these characteristics accounted for 78.3% of the variability in the actual final over ground walking speed after 36 sessions of locomotor training. The model was successful in prospectively predicting over ground walking speed in the 8 test participants within 4.15 +/- 2.22 cm/s in their recovered walking speed. CONCLUSIONS: This prediction model can identify individuals who are most likely to experience success using locomotor training by determining an expected magnitude of training effect, thereby allowing individualized decisions regarding the use of this intensive approach to rehabilitation.

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