Association between screen time and self-reported balance disorders in middle-aged and older adults: national health and nutrition examination survey.

BACKGROUND: Balance disorders can give rise to sensations of instability, lightheadedness, vertigo, disequilibrium, or syncope, ultimately leading to grave medical, physical, emotional, and societal ramifications. These conditions are highly prevalent among individuals aged 40 and above. Screen time encompasses activities associated with television viewing, video game playing, and non-work-related computer usage. Prolonged screen exposure may engender a spectrum of health issues and even elevate overall mortality rates. However, the available evidence on the potential link between excessive screen time and balance dysfunction remains limited. AIMS: The primary aim of this study was to explore the possible association between prolonged screen exposure and impaired balance function. METHODS: This cross-sectional study utilized data from participants who completed a comprehensive questionnaire in the NHANES database between 1999 and 2002, all of whom were aged over 40 and under 85 years. Participants' screen time was categorized into two groups (< 4 h/d and ≥4 h/d) for subsequent data analysis. Logistic regression, combined with propensity score matching (PSM), was employed to investigate the correlation between screen time and balance disorders. RESULTS: A total of 5176 participants were enrolled in this study, comprising 2,586 men and 2,590 women, with a prevalence rate of balance disorders at 25.7% (1331/5176). The incidence of balance disorders was found to be significantly higher among individuals who spent 4 hours or more per day on screen time compared to those with less screen time (P<0.001). Multivariate logistic analysis conducted on the unmatched cohort revealed a significant association between screen time and balance disorders, with an odds ratio (OR) 1.8 (95%CI 1.57 ∼ 2.05). These findings remained consistent even after adjusting for confounding factors, yielding an OR 1.43 (95%CI 1.24 ∼ 1.66). Moreover, the association persisted when employing various multivariate analyses such as propensity score matching adjusted model, standardized mortality ratio weighting model and pairwise algorithmic model; all resulting in ORs ranging from 1.38 to 1.43 and p-values < 0.001. CONCLUSIONS: After controlling for all covariates, screen time (watching TV, playing video games, and using computers outside of work) was associated with balance dysfunction among middle-aged and older adults. This finding may offer a possible idea for the prevention of dizziness and balance disorders. Nevertheless, additional research is imperative to further validate these results.

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.