Differential effects of water-based exercise on the cognitive function in independent elderly adults.

BACKGROUND: Physical exercise has been reported to be the most effective method to improve cognitive function and brain health, but there is as yet no research on the effect of water-based exercise. AIMS: The aim of the present study was to compare the effects of water-based exercise with and without cognitive stimuli on cognitive and physical functions. METHODS: The design is a single-blind randomized controlled study. Twenty-one participants were randomly assigned to a normal water-based exercise (Nor-WE) group or a cognitive water-based exercise (Cog-WE) group. The exercise sessions were divided into two exercise series: a 10-min series of land-based warm-up, consisting of flexibility exercises, and a 50-min series of exercises in water. The Nor-WE consisted of 10 min of walking, 30 min of strength and stepping exercise, including stride over, and 10 min of stretching and relaxation in water. The Cog-WE consisted of 10 min of walking, 30 min of water-cognitive exercises, and 10 min of stretching and relaxation in water. Cognitive function, physical function, and ADL were measured before the exercise intervention (pre-intervention) and 10 weeks after the intervention (post-intervention). RESULTS: Participation in the Cog-WE performed significantly better on the pegboard test and the choice stepping reaction test and showed a significantly improved attention, memory, and learning, and in the general cognitive function (measured as the total score in the 5-Cog test). Participation in the Nor-WE dramatically improved walking ability and lower limb muscle strength. CONCLUSION: Our results reveal that the benefits elderly adults may obtain from water-based exercise depend on the characteristics of each specific exercise program. These findings highlight the importance of prescription for personalized water-based exercises to elderly adults to improve cognitive function.

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography.

BACKGROUND: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. METHODS: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. RESULTS: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. CONCLUSIONS: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.