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.

(1) Background: Neck pain intensity, psychosocial factors, and physical function have been identified as potential predictors of neck disability. Machine learning algorithms have shown promise in classifying patients based on their neck disability status. So, the current study was conducted to identify predictors of neck disability in patients with neck pain based on clinical findings using machine learning algorithms. (2) Methods: Ninety participants with chronic neck pain took part in the study. Demographic characteristics in addition to neck pain intensity, the neck disability index, cervical spine contour, and surface electromyographic characteristics of the axioscapular muscles were measured. Participants were categorised into high disability and low disability groups based on the median value (22.2) of their neck disability index scores. Several regression and classification machine learning models were trained and assessed using a 10-fold cross-validation method; also, MANCOVA was used to compare between the two groups. (3) Results: The multilayer perceptron (MLP) revealed the highest adjusted R2 of 0.768, while linear discriminate analysis showed the highest receiver characteristic operator (ROC) area under the curve of 0.91. Pain intensity was the most important feature in both models with the highest effect size of 0.568 with p < 0.001. (4) Conclusions: The study findings provide valuable insights into pain as the most important predictor of neck disability in patients with cervical pain. Tailoring interventions based on pain can improve patient outcomes and potentially prevent or reduce neck disability.

Background: Researchers are prioritizing the development of an effective treatment approach for third-trimester pregnancy-related low back pain (LBP), a prevalent and costly disorder. Therefore, this study aimed to examine the effects of Tui-Na (TN) versus positional release techniques (PRT) on third trimester pregnancy-related LBP. Methods: Fifty pregnant women in their third trimester with low back pain were randomly assigned to 1 of 2 groups for 4 weeks of prescribed treatment (TN or PRT). The primary outcome was LBP intensity. Secondary outcomes included the Oswestry disability index for back disability and the pressure pain threshold of lumbar tender points. Two-way multivariate analysis of variance was used for the data analysis. Results: Multivariate tests indicated statistically significant effects of group (F = 10.062, P < .001, partial η ² = 0.302), time (F = 473.5, P < .001, partial η ² = 0.953), and group-by-time interactions (F = 4.045, P < .001, partial η ² = 0.148). However, the TN group, when compared to the PRT group, revealed a significant decrease in back disability (P < .001, partial η ² = 0.124) and a significant increase in pressure pain threshold at the Rt and Lt points (P = .02 and .001, partial η ² = 0.055, and 0.108, respectively). Within-group comparisons were significant for all measured variables in both the groups (P < .001). Conclusion: Although both TN and PRT are beneficial treatments for third trimester pregnancy-related LBP, TN leads to more beneficial outcomes.

Herein, we reply to comments made by Kovacs and Seco-Calvo