Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Through the Inclusion of Limb Accelerations During Sleep and Personal Factors

• Authors: Stephanie K Rigot, University of Pittsburgh; Michal L Boninger, University of Pittsburgh; Dan Ding, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Gina McKernan, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Edelle Field-Fote, Emory University; Jeanne Hoffman, University of Washington; Rachel Hibbs, University of Pittsburgh; Lynn A Worobey, University of Pittsburgh
• Journal Title: ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
• Volume: Volume 103, Number 4
• Publisher: W B SAUNDERS CO-ELSEVIER INC | 2022-03-31, Pages 676-+
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.apmr.2021.02.029
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Rehabilitation and Therapy
• Keywords: Science & Technology; Life Sciences & Biomedicine; Rehabilitation; Sport Sciences; Prognosis; Recovery of function; Spinal cord inju-ries; Walking; Wheelchair; MODIFIED ASHWORTH SCALE; PERIODIC LEG MOVEMENTS; MANUAL WHEELCHAIR USERS; QUALITY-OF-LIFE; PHYSICAL-ACTIVITY; ACTIVITY RECOGNITION; ENVIRONMENTAL-FACTORS; LONG-TERM; INDIVIDUALS; OUTCOMES

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.