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ObjectivesThe South Korean government has recently implemented policies to prevent suicide. However, there were few studies examining the recent changing trends in suicide rates. This study aims to examine the changing trends in suicide rates by time and age group.DesignA descriptive study using nationwide mortality rates.SettingData on the nationwide cause of death from 1993 to 2016 were obtained from Statistics Korea.ParticipantsPeople living in South Korea.InterventionsImplementation of national suicide prevention policies (first: year 2004, second: year 2009).Primary outcome measuresSuicide was defined as ‘X60-X84’ code according to the ICD-10 code. Age-standardised suicide rates were estimated, and a Joinpoint regression model was applied to describe the trends in suicide rate.ResultsFrom 2010 to 2016, the suicide rates in South Korea have been decreasing by 5.5% (95% CI −10.3% to −0.5%) annually. In terms of sex, the suicide rate for men had increased by 5.0% (95% CI 3.6% to 6.4%) annually from 1993 to 2010. However, there has been no statistically significant change from 2010 to 2016. For women, the suicide rate had increased by 7.5% (95% CI 6.3% to 8.7%) annually from 1993 to 2009, but since 2009, the suicide rate has been significantly decreasing by 6.1% (95% CI −9.1% to −3.0%) annually until 2016. In terms of the age group, the suicide rates among women of almost all age groups have been decreasing since 2010; however, the suicide rates of men aged between 30 and 49 years showed continuously increasing trends.ConclusionOur results showed that there were differences in the changing trends in suicide rate by sex and age groups. Our finding suggests that there was a possible relationship between implementation of second national suicide prevention policies and a decline in suicide rate.

This prediction algorithm, weighted towards common non-invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations.

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This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.