ArticleSensors (Basel, Switzerland)2020
A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- The Use of Sensors to Prevent, Predict Transition to Chronic and Personalize Treatment of Low Back Pain: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Bioengineering Innovations for Personalized Care in Low Back Pain: From Sensors to Smart Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Wearable sensor technologies for individuals with back pain: a scoping review.Pain management · 2025Article
- Association between movement speed and instability catch kinematics and the differences between individuals with and without chronic low back pain.Scientific reports · 2024Article
- Biomechanical Phenotyping of Chronic Low Back Pain: Protocol for BACPAC.Pain medicine (Malden, Mass.) · 2023Article
- A Scoping Review of Chronic Low Back Pain Classification Schemes Based on Patient-Reported Outcomes.Pain physician · 2022Article
- Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test.Sensors (Basel, Switzerland) · 2022Article
- Distinction of non-specific low back pain patients with proprioceptive disorders from healthy individuals by linear discriminant analysis.Frontiers in bioengineering and biotechnology · 2022Article
- Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.Sensors (Basel, Switzerland) · 2020Article
- Linear and Non-linear Dynamic Methods Toward Investigating Proprioception Impairment in Non-specific Low Back Pain Patients.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.