Relationships between forward head posture and lumbopelvic sagittal alignment in older adults with chronic low back pain

Prediction of Back Disability Using Clinical, Functional, and Biomechanical Variables in Adults with Chronic Nonspecific Low Back Pain

Background: Researchers are focusing on understanding the etiology and predisposing factors of chronic nonspecific low back pain (CNSLBP), a costly prevalent and disabling disorder. Related clinical, functional, and biomechanical variables are often studied, but in isolation. We aimed to identify key factors for managing CNSLBP by examining the relationship between back disability and related clinical, functional, and biomechanical variables and developed prediction models to estimate disability using various variables. Methods: We performed a cross-sectional correlational study on 100 recruited patients with CNSLBP. Clinical variables of pain intensity (visual analog score), back extensor endurance (Sorenson test), functional variables of the back performance scale, 6 min walk test, and the biomechanical variable C7-S1 sagittal vertical axis were analyzed to predict disability (Oswestry disability index). Results: All variables independently, as well as in multi-correlation, were significantly correlated to disability (p < 0.05). The bivariate regression models were significant between back disability and pain intensity (Y = 11.24 + 2.189x), Sorensen results (Y = 105.48 - 0.911x), the back performance scale (Y = 6.65 + 2.486x), 6 min walk test (Y = 49.20 - 0.060x), and sagittal vertical axis (Y = 0.72 + 4.23x). The multi-regression model showed significant contributions from pain (p = 0.001) and Sorensen results (p = 0.028) in predicting back disability, whereas no significant effect was found for other variables. Conclusions: A multidisciplinary approach is essential not only for the management of but also for the assessment of chronic nonspecific low back pain, including its clinical, functional, and biomechanical characteristics. However, special emphasis should be placed on clinical characteristics, including the intensity of pain and back extensor endurance.

Differences in musculoskeletal dysfunctions and in postural alterations between chronic migraine and chronic tension type headache: A cross-sectional study

OBJECTIVE

The aim of present study is to assess postural alterations and musculoskeletal dysfunctions over all spine in patients with chronic migraine and chronic tension type headache, moreover to highlight the differences between these two forms of primary headache.

METHODS

A Cross sectional study was adopted to evaluate the musculoskeletal profile in patients with chronic migraine and with chronic tension type headache. The Bio photogrammetric evaluation was performed using the postural assessment software PAS/SAPO, while unilateral passive accessory intervertebral motion (PAIMs) were applied for manual examinations of spine segments from C0 to L5 vertebra. The One-way Analysis of Variance (ANOVA) test was used to compare the three groups with the software GraphPad InStat 3.06.

RESULTS

A total of 60 patients were recruited, 20 for chronic tension type group, 20 for chronic migraine group and 20 healthy controls. The most interesting findings was that patients with chronic primary headaches presented postural alterations in all parameters (cranio-vertebral angle and lumbar-pelvic angle) and musculoskeletal dysfunctions in all spine with respect to healthy controls. Finally, the most clinically relevant finding was that no differences were found between chronic migraine and chronic tension type headache concerning the postural alterations nor the musculoskeletal dysfunctions.

CONCLUSION

The sensitization acts as a substrate or consequence of these musculoskeletal dysfunctions in chronic primary headache. Therefore, non-pharmacological treatments targeted in the musculoskeletal system may be a good option in the management of chronic primary headache, especially when these therapies integrate various techniques that involve all spine.

Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture Parameters

Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are, therefore, often subjective and prone to errors. Machine learning (ML) methods in combination with explainable artificial intelligence (XAI) tools have proven useful for providing an objective, data-based orientation. However, only a few works have considered posture parameters, leaving the potential for more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). The posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. The label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (M_PRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible, in general. In the context of personalized medicine, the present study's approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.

Assessment on Practicing Correct Body Posture and Determinant Analyses in a Large Population of a Metropolitan Area

An incorrect posture can generate stress of the spine and can be the cause of musculoskeletal disorders. Considering the extensive use of the computer, which worsens posture disorders, among workers, is important to analyze the phenomenon in order to reduce his impact on industry. The aim of this study is to assess determinants regarding posture in a large population of a metropolitan area. A total of 1177 questionnaires was analyzed. The majority of sample showed good knowledge and attitude regarding correct posture; most of the sample, 70.4% was aware of the definition of posture and 68.7% feel that not enough attention is paid at posture at workplace. Despite the good predisposition, only 2.8% of the sample consult a specialist for posture. The multiple linear regression analysis shows that those who have higher knowledge and best attitudes will consequently have good behaviors in maintaining a correct posture. Furthermore, age and education resulted main drivers of correct posture in any model considered. The results enlighten the necessity of conducting further studies to analyze attitudes of the general population and suggest improving educational and training programs to the enrichment of knowledge and to correct posture behaviors.

Analysis of Parameters That Can Predict the Cervical Sagittal Vertical Axis in Cervical Fusion Surgery

OBJECTIVE

The absolute value of the cervical sagittal parameters cannot be guaranteed with certainty on all follow-up cervical radiographs. With the assumption that neck posture changes can occur at any time at each follow-up radiographic session, we examined whether the sagittal parameters change meaningfully and identified the factors most closely related to the C2-C7 sagittal vertical axis (SVA).

METHODS

We enrolled 200 patients who had undergone either anterior cervical fusion (n = 100) or posterior cervical fusion (n = 100). The craniovertebral angle (CVA), mandible angle (MA), occipital slope (Os), C2 slope (C2s), C7 slope (C7s), and C2-C7 SVA were measured on 2 different follow-up radiographs after surgery. The C2-C7 angle (C2-C7A) and changes (Δ) in the sagittal parameters between the 2 radiographs were then calculated.

RESULTS

The ΔC2s and ΔCVA showed a very strong correlation with the ΔC2-C7 SVA (r = |0.70-0.93|). An independent t test showed a statistically significant difference for multiple sagittal parameters (i.e., ΔMA, ΔOs, ΔC2s, ΔC7s, and ΔCVA) between the large and small ΔC2-C7 SVA groups. In contrast, the change in the C2-C7A was without statistical significance. A stepwise multivariate regression analysis revealed a high adjusted R2 value (0.841) between the ΔC2-C7 SVA and 2 parameters (standardized coefficient: ΔCVA, -0.563; ΔC2s, -0.398).

CONCLUSIONS

During cervical fusion surgery, the CVA was the most predictable parameter reflecting the C2-C7 SVA in various analyses. The upper cervical parameters (Os and C2s) provided more explanatory power regarding the C2-C7 SVA changes than did the lower cervical parameter (C7s) or the presence of cervical lordosis (C2-C7A).