OBJECTIVES: To establish predictive equations for peak torque of muscle groups with totally and partially preserved innervation in individuals with motor complete spinal cord injury (SCI), based on hand dynamometry and strength predictor variables. METHODS: The cross-sectional study conducted at a rehabilitation hospital consecutively recruited 108 men and women with SCI. All participants performed maximum peak torque tests for shoulder abduction/adduction (isokinetic), trunk flexion/extension (isometric), and handgrip strength testing (hand dynamometer) to establish predictive peak torque equations. The primary outcomes were peak torque variables. Handgrip strength, age, injury level, time since injury, age at injury, body mass, height, body mass index, and physical activity level were the secondary outcomes used as strength predictor variables. RESULTS: Handgrip strength was a predictor variable for shoulder abduction/adduction peak torque. The best predictive models for shoulder abduction/adduction peak torque exhibited R 2 = 0.57 and R 2 = 0.60, respectively (p ≤ .05). Injury level showed the highest significant predictive capacity for trunk flexion/extension peak torque models (R 2 = 0.38 and R 2 = 0.29; p ≤ .05). CONCLUSION: Shoulder abduction/adduction peak torque predictive equations may be an alternative for use in an accessible strength tool (hand dynamometry) to evaluate training and rehabilitation programs. Trunk flexion/extension peak torque equations exhibited moderate correlations and high standard error of the estimates and should be used with caution.
OBJECTIVES: To establish predictive equations for peak torque of muscle groups with totally and partially preserved innervation in individuals with motor complete spinal cord injury (SCI), based on hand dynamometry and strength predictor variables. METHODS: The cross-sectional study conducted at a rehabilitation hospital consecutively recruited 108 men and women with SCI. All participants performed maximum peak torque tests for shoulder abduction/adduction (isokinetic), trunk flexion/extension (isometric), and handgrip strength testing (hand dynamometer) to establish predictive peak torque equations. The primary outcomes were peak torque variables. Handgrip strength, age, injury level, time since injury, age at injury, body mass, height, body mass index, and physical activity level were the secondary outcomes used as strength predictor variables. RESULTS: Handgrip strength was a predictor variable for shoulder abduction/adduction peak torque. The best predictive models for shoulder abduction/adduction peak torque exhibited R 2 = 0.57 and R 2 = 0.60, respectively (p ≤ .05). Injury level showed the highest significant predictive capacity for trunk flexion/extension peak torque models (R 2 = 0.38 and R 2 = 0.29; p ≤ .05). CONCLUSION: Shoulder abduction/adduction peak torque predictive equations may be an alternative for use in an accessible strength tool (hand dynamometry) to evaluate training and rehabilitation programs. Trunk flexion/extension peak torque equations exhibited moderate correlations and high standard error of the estimates and should be used with caution.
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