Background: The prevalence of flatfeet in normal and obese children has not been studied well in the past. The prevalence of flatfoot and foot dimensions of school children in Taiwan were investigated in this study. Materials and Methods: One thousand twenty-four school children (549 boys and 475 girls) aged 5 to 13 years participated in this study. Fifteen foot dimensions were measured using a 3D coordinate measuring probe, digital tape measure and Harris mat imprint. Two-way ANOVA was conducted to evaluate the flatfoot and obesity effects on foot dimensions as well as on the change in foot size between weightbearing and nonweightbearing conditions. Results: The prevalence of flatfoot for 5- to 13-year-old children was 28% with a decreasing trend with age. Boys had a significantly higher frequency for flatfoot than girls (chi-square = 26.3; p < 0.001). The prevalence of flatfoot was 35% in boys and 20% in girls. The percent of overweight and obese children was 20%. A significant difference in the prevalence of flatfoot occurred between normal-weight (27%), overweight (31%), and obese (56%) children (chi-square = 18.0; p < 0.001). The obesity effect was significant ( p < 0.01) for most foot dimensions. However, the flatfoot effect was only significant ( p < 0.05) on foot height for both genders. Conclusion: Obese children have a higher frequency of flatfoot, greater foot dimensions, and less change in foot width with weightbearing than normal weight children. Children with flatfeet have a lower foot height and greater foot length change than children with normal feet with weightbearing. Level of Evidence: III, Comparative Case Series

This study identified several physiological indices that can accurately monitor mental workload while participants performed multiple tasks with the strategy of maintaining stable performance and maximizing accuracy. Thirty male participants completed three 10-min. simulated multitasks: MATB (Multi-Attribute Task Battery) with three workload levels. Twenty-five commonly used mental workload measures were collected, including heart rate, 12 HRV (heart rate variability), 10 EEG (electroencephalography) indices (α, β, θ, α/θ, θ/β from O1-O2 and F4-C4), and two subjective measures. Analyses of index sensitivity showed that two EEG indices, θ and α/θ (F4-C4), one time-domain HRV-SDNN (standard deviation of inter-beat intervals), and four frequency-domain HRV: VLF (very low frequency), LF (low frequency), %HF (percentage of high frequency), and LF/HF were sensitive to differentiate high workload. EEG α/θ (F4-C4) and LF/HF were most effective for monitoring high mental workload. LF/HF showed the highest correlations with other physiological indices. EEG α/θ (F4-C4) showed strong correlations with subjective measures across diff erent mental workload levels. Operation strategy would affect the sensitivity of EEG α (F4-C4) and HF.