This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. Results of variable importance were also provided. Bagging algorithm using Multivariate Adaptive Regression Spline (MARS) algorithm (accuracy = 0.8966, area under receiver operating characteristic curve = 0.9387, F1-score = 0.9032) was the best performing model regarding the performance criteria. According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.

Geriatric individuals may frequently develop low back pain after physiological changes. The aim of our study was to compare the effects of core stabilization exercises alone and aerobic exercises in addition to core stabilization exercises on functional capacity, physical performance and fall risk in geriatric individuals with chronic non-specific low back pain (CNLBP). In this randomized controlled study, 30 participants in the core stabilization exercise group (CG) received intervention, and the mean age of this group was 70.43 ± 4.48 years. The mean age of the 30 participants in the core stabilization + aerobic exercise group (CAG) was determined as 69.60 ± 4.05 years. Sixty individuals with CNLBP were included in the study and divided into two groups: CG (n = 30) and CAG (n = 30). CG received core stabilization exercises for 8 weeks, while CAG received aerobic exercise using a treadmill in addition to core stabilization exercises for 8 weeks. Functional capacity (six-minute walk test), physical performance (Oswestry Disability Index), fall risk (Biodex Balance System), pain intensity (Visual Analogue Scale), depression status (Beck Depression Inventory) and kinesiophobia level (Tampa Kinesiophobia Scale) were evaluated before and after the treatment programmes. Significant improvements were found in all parameters in both groups after the treatment programmes (p < 0.05). In addition, functional capacity, physical performance, fall risk, pain severity and depression scores improved more in CAG compared to CG (p < 0.05), but the improvement in kinesiophobia was similar (p > 0.05). The results of the study showed that both core stabilization exercises alone and core stabilization exercises combined with aerobic exercise led to improvements in patients with CNLBP. However, the group that combined aerobic and core stabilization exercises (CAG) showed greater improvements, particularly in functional capacity, physical performance, fall risk, pain intensity, and depression levels. These findings suggest that core stabilization exercises are beneficial for CNLBP, but adding aerobic exercise may enhance these positive effects. Therefore, incorporating aerobic exercise into rehabilitation programmes for older adults with CNLBP may provide additional clinical benefits. This clinical trial was registered at http://clinicaltrials.gov (Number: NCT06946758; registration date: 21/04/2025).

Although Five Times-Sit-To-Stand test (FTSST) performance is known to be a valid and reliable method in people with chronic stroke, Parkinson’s disease, and balance disorder, it has not been widely...

Background: It is recognized that pain related to adult individuals with idiopathic scoliosis (IS) substantially impacts individuals’ daily activities and quality of life. The objective of this study was to identify the possible predictors of pain intensity in non‑surgically treated adults with IS. Methods: This cross-sectional study included 58 adults individuals with Lenke type 1 IS. Participants’ sociodemographic characteristics were recorded, and pain severity, curvature severity, trunk rotation angle, disability, spinal mobility, cosmetic deformity perception, and quality of life were assessed. Regression analyses with various models were performed to determine the predictors of pain severity and the best model was selected based on performance criteria. Results: Strong associations were found between pain severity with curvature severity, spinal mobility, trunk rotation angle, perception of cosmetic deformity, disability, and quality of life (p < 0.05). It was observed that Lasso regression was the best model based on the performance criteria considered. According to this model, the primary predictors of pain intensity in adult IS were determined as curvature severity, spinal mobility, trunk rotation angle, cosmetic deformity perception, back-related disability and quality of life, in order of importance. Conclusion: In accordance with the findings of this study, which examined for the first time the determinants of pain intensity in adult individuals with Lenke type 1 IS, we suggest that mentioned possible factors affecting and determining pain should be taken into consideration when establishing evaluation and treatment programs.