Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning.

Failure to obtain the recommended 7−9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night's sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7−9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night's sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night's sleep using single-task gait.

Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.

Mapping the coordination and cooperation mechanisms of risk communication on feed/food safety in the EU.

The mapping of the EU coordination and cooperation mechanisms of risk communication in the field of food safety (i.e. encompassing all matters pertaining to the feed and food chain) was conducted for the European Food Safety Authority (EFSA), following a mandate by the European Commission, to inform the General Plan for Risk Communication that the Commission will set out. This study focuses on communication activities in 'peacetime' situations (i.e. non-crisis) on the published outputs of risk assessment and risk management. This is a specific part of the risk communication process defined in the General Food Law. Data were collected through an online survey of competent authority organisations involved in risk communication at national and regional levels in the EU-27 Member States and Norway; and interviews with selected organisations at Member State, EU and international levels. Based on these data, this report provides an overview of the current information flow of risk communication, in terms of whether and how organisations at all levels receive, produce, and disseminate risk communication material. The existing flows in each country are described and mapped in flow charts. Flows are also presented for each of the EU/international organisations involved in risk communication on food safety. A number of relevant networks are identified that actively engage in sharing/exchanges of risk communication material on food safety, including networks managed by EFSA. This project also highlighted certain challenges in current risk communication systems, and recommendations are made to address them. At national level, these are: an increase in resources dedicated to coordinating communication on EU food safety issues, to improve capacity to undertake this task; strengthen the cooperation and coordination between authorities involved in risk communication activities to ensure a more proactive approach. In addition, recommendations are provided to enhance further collaboration of communication activities at all levels (between Member States and with EFSA; between EU Agencies), so that the coordination of risk communication at EU level follows a partnership approach.