Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study.

Low levels of physical activity in individuals with chronic pain can lead to additional functional impairment and disability. This study aims to investigate the predictors of low physical activity levels in individuals with chronic pain, and to determine the accuracy of the artificial neural network used to analyze these predictors. Community-dwelling older adults with chronic pain (n = 103) were surveyed for their physical activity levels and classified into low, moderate, or high physical activity level groups. Chronic pain-related measurements, physical function assessment, and clinical history, which all influence physical activity, were also taken at the same time. Logistic regression analysis and analysis of multilayer perceptron, an artificial neural network algorithm, were performed. Both analyses revealed that history of falls was a predictor of low levels of physical activity in community-dwelling older adults. Multilayer perceptron analysis was shown to have excellent accuracy. Our results emphasize the importance of fall prevention in improving the physical activity levels of community-dwelling older adults with chronic pain. Future cross-sectional studies should compare multiple analysis methods to show results with improved accuracy.

Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning.

Hallux valgus, a frequently seen foot deformity, requires early detection to prevent it from becoming more severe. It is a medical economic problem, so a means of quickly distinguishing it would be helpful. We designed and investigated the accuracy of an early version of a tool for screening hallux valgus using machine learning. The tool would ascertain whether patients had hallux valgus by analyzing pictures of their feet. In this study, 507 images of feet were used for machine learning. Image preprocessing was conducted using the comparatively simple pattern A (rescaling, angle adjustment, and trimming) and slightly more complicated pattern B (same, plus vertical flip, binary formatting, and edge emphasis). This study used the VGG16 convolutional neural network. Pattern B machine learning was more accurate than pattern A. In our early model, Pattern A achieved 0.62 for accuracy, 0.56 for precision, 0.94 for recall, and 0.71 for F1 score. As for Pattern B, the scores were 0.79, 0.77, 0.96, and 0.86, respectively. Machine learning was sufficiently accurate to distinguish foot images between feet with hallux valgus and normal feet. With further refinement, this tool could be used for the easy screening of hallux valgus.