Stephanie K Rigot1, Michael L Boninger2, Dan Ding3, Gina McKernan4, Edelle C Field-Fote5, Jeanne Hoffman6, Rachel Hibbs7, Lynn A Worobey8. 1. Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA. 2. Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA. 3. Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA. 4. Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA. 5. Crawford Research Institute, Shepherd Center, Atlanta, GA; Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA; Program in Applied Physiology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA. 6. Department of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA. 7. Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA. 8. Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA. Electronic address: law93@pitt.edu.
Abstract
OBJECTIVE: To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models. DESIGN: Cross-sectional, 1-5 days of data collection. SETTING: Community-based data collection. PARTICIPANTS: Adults with chronic (>1 year), motor incomplete SCI (N=27). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results. RESULTS: The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models. CONCLUSIONS: The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.
OBJECTIVE: To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models. DESIGN: Cross-sectional, 1-5 days of data collection. SETTING: Community-based data collection. PARTICIPANTS: Adults with chronic (>1 year), motor incomplete SCI (N=27). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results. RESULTS: The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models. CONCLUSIONS: The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.
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