Abstract
The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart “sensor-based” practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today’s smartphones, as objective tools for various health applications towards personalized precision medicine.
Highlights
In a world that is rapidly embracing precision medicine and patient-centric state-of-the-art tools and technologies designed for healthcare, the quantitative classification of non-specific low back pain (NSLBP) and effective personalized treatment, remain elusive in most clinical settings.while, several nonspecific low back pain (NSLBP) classification systems were developed in the last few decades, their incompleteness and/or complexity, have led to the use of simpler tools
The current study presented a novel methodology towards the practical clinical application of inertial sensors as a quantitative tool to classify NSLBP
The results can be summarized as follows—(1) Increasing asymmetry in the planes of motion, resulted in decreasing the acceleration and angular velocity, while increasing the jerk; (2) The angular velocity and jerk were higher during extension, while the acceleration was higher during flexion
Summary
While, several NSLBP classification systems were developed in the last few decades, their incompleteness and/or complexity, have led to the use of simpler tools. These include observational assessment, surveys and questionnaires, such as the STarT Back Screening Tool (STarT) [1]. This tool classifies NSLBP patients into three specific subgroups—low risk, medium risk and high risk based on 9 questions spanning educational therapy, physical therapy, as well as physical therapy combined with cognitive behavioral therapy. Abedi et al found an approximately 80% correlation between the translated STarT questionnaire and other screening questionnaires, including the Roland Morris Disability Questionnaire (RMDQ), Tampa Scale for Kinesiophobia (TSK), Coping Strategies Questionnaire (CSQ) and Hospital Anxiety and Depression
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