Assessing progress under Health 2020 in the European Region of the World Health Organization.

BACKGROUND: Health 2020 is the regional health policy framework of the World Health Organization (WHO) Regional Office for Europe. The goals of Health 2020 are to improve health and well-being, reduce health inequalities and strengthen public health. To gain insight into the Health 2020 targets needing extra attention in coming years, we assessed progress under Health 2020 in the WHO European Region. METHODS: Quantitative methods were used to assess progress in 50 out of 53 Member States of the WHO European Region in 2005, 2010 and 2015. The 16 quantitative Health 2020 indicators were rescaled from 1 to 100, with 1 indicating poor performance and 100 indicating good performance. The geometric mean of all 16 rescaled indicators was taken by Health 2020 target to compose a Health 2020 index. RESULTS: The Health 2020 index (2015) ranged from 82.8 in Sweden to 30.0 in Turkmenistan. A clear east-west gradient was observed in the WHO European Region, with countries in western parts performing relatively better than countries in eastern parts. Indicators with the largest increase between 2005 and 2015 were premature mortality, mortality external causes, life expectancy and infant mortality. However, all quintiles showed a decline on overweight. CONCLUSIONS: The Health 2020 index gives a relative overview regarding the past and present performance on the Health 2020 policy framework of countries in the WHO European Region. Although improvements have been observed between 2005 and 2015, challenges remain to improve health for all in the context of the United Nations 2030 Agenda for Sustainable Development.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective.

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.