BACKGROUND: The link between spasticity and impaired voluntary movement after stroke remains unclear because of the lack of suitable tools characterizing properties of spastic muscles. Describing this relationship early poststroke can potentially help predict the extent and time course of recovery. OBJECTIVE: To describe the time course of changes in neuromuscular properties after stroke using the upper extremity Fugl-Meyer Assessment (FMA) at 1 month to predict recovery patterns over 1 year. METHODS: Using a parallel cascade system identification technique, this study characterized intrinsic and reflex behaviors for different mean elbow joint angles, at specified times poststroke. Then the "growth mixture" model was used to characterize recovery patterns over 1 year. Logistic regression analyses were applied to predict these patterns. The impact of patient characteristics was also investigated. RESULTS: In 21 stroke survivors, 14 had sustained hemorrhage and 7 had thromboses. The study observed several recovery classes, relating intrinsic and reflex stiffness magnitudes with changing elbow angle at different time points. The largest group (48%) showed progressive increase in reflex stiffness over time, but 33% showed declining reflex stiffness over the same period. A third class (19%) showed invariant reflex properties. These differences were linked to the initial reflex magnitudes. The FMA at 1 month showed an inverse relationship with key reflex patterns and proved to be a strong predictor of class membership. Stroke type was also influential. CONCLUSIONS: The logistical regression class may enable us to accurately predict reflex responses during the first year, allowing us to apportion impairment between central and peripheral mechanisms.
BACKGROUND: The link between spasticity and impaired voluntary movement after stroke remains unclear because of the lack of suitable tools characterizing properties of spastic muscles. Describing this relationship early poststroke can potentially help predict the extent and time course of recovery. OBJECTIVE: To describe the time course of changes in neuromuscular properties after stroke using the upper extremity Fugl-Meyer Assessment (FMA) at 1 month to predict recovery patterns over 1 year. METHODS: Using a parallel cascade system identification technique, this study characterized intrinsic and reflex behaviors for different mean elbow joint angles, at specified times poststroke. Then the "growth mixture" model was used to characterize recovery patterns over 1 year. Logistic regression analyses were applied to predict these patterns. The impact of patient characteristics was also investigated. RESULTS: In 21 stroke survivors, 14 had sustained hemorrhage and 7 had thromboses. The study observed several recovery classes, relating intrinsic and reflex stiffness magnitudes with changing elbow angle at different time points. The largest group (48%) showed progressive increase in reflex stiffness over time, but 33% showed declining reflex stiffness over the same period. A third class (19%) showed invariant reflex properties. These differences were linked to the initial reflex magnitudes. The FMA at 1 month showed an inverse relationship with key reflex patterns and proved to be a strong predictor of class membership. Stroke type was also influential. CONCLUSIONS: The logistical regression class may enable us to accurately predict reflex responses during the first year, allowing us to apportion impairment between central and peripheral mechanisms.