Range of Motion and Motion Patterns in Patients With Low Back Pain Before and After Rehabilitation
| Autor: | Malcolm H. Pope, Kevin F. Spratt, Marek Szpalski, Marianne Magnusson, Leif Hasselquist, Jeffrey B. Bishop |
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| Rok vydání: | 1998 |
| Předmět: |
Adult
Male medicine.medical_specialty Rotation Movement medicine.medical_treatment Kinematics Motion (physics) Humans Medicine Orthopedics and Sports Medicine In patient Range of Motion Articular Rehabilitation business.industry Lumbosacral Region Recovery of Function Middle Aged Trunk Low back pain Chronic low back pain Treatment Outcome Multivariate Analysis Physical therapy Female Neural Networks Computer Neurology (clinical) medicine.symptom business Range of motion Low Back Pain |
| Zdroj: | Spine. 23:2631-2639 |
| ISSN: | 0362-2436 |
| DOI: | 10.1097/00007632-199812010-00019 |
| Popis: | Study design Data were collected from 27 patients who were participating in a rehabilitation program for chronic low back pain. The patients were tested on day 2 and day 11 of a 2-week rehabilitation program. Objectives To determine specific characteristics of trunk motion associated with long-term dysfunction caused by low back pain of various origin, to determine if a neural network analysis system can be effective in distinguishing between patterns, and to determine if the rehabilitation has an effect on range and pattern of motion. Summary of background data There is a lack of objective measures for evaluating the efficacy of rehabilitation programs. Numerous studies have established the difficulty of evaluating low back pain. Existing techniques, such as imaging methods, are in many cases either very rough and inaccurate or expensive and ineffective. A technique for evaluation of motion patterns in low back pain was developed based on analysis of dynamic motion features such as shape, velocity, and symmetry of movements. Methods Dynamic motion data were collected before and after rehabilitation from 27 patients with low back pain by using a triaxial goniometer. Range of motion and features of the movement, such as shape, velocity, and repetitiveness, were extracted for analysis. Results Motion features showed significant improvement after the rehabilitation program. Conclusions A neural network based on kinematic data is an excellent model for classification of low back pain dysfunction. Such a system could markedly improve the management of low back pain for an individual patient. |
| Databáze: | OpenAIRE |
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