Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors.

Physical inactivity is responsible for 7-10% of all premature deaths worldwide. Thus, valid, reliable and unobtrusive methods for monitoring activities of daily living (ADL) to predict total energy expenditure (TEE) is desired. Multiple methods exist to quantify TEE, but microelectromechanical systems (MEMSs) are the only method, which has shown promising results and are applicable for long-term monitoring in the field. However, no perfect method exists for predicting TEE on a daily basis. The present study evaluates TEE estimation based on a MEMS (Xsens Link system) taking gender and heart rate into account. Fifteen individuals performed seven ADL wearing the Xsens Link system, a heart rate belt and an oxygen mask. Multiple linear regression models were established for sedentary and dynamic activities and evaluated by leave-one-out cross-validation and compared with indirect calorimetry. The linear regression model showed better prediction for dynamic activities (adjusted R2 0.95±0.16) compared to sedentary activities (adjusted R2 0.61±0.19). The root-mean-square error for the TEE estimation ranged between 0.02 and 0.08 kJ/min/kg for the sedentary and dynamic models, respectively. The study showed a viable approach to predict TEE in ADL compared to previously published results. Further studies are warranted to reduce the number of sensors in the estimation of TEE.

Remote optimization of an ultracold atoms experiment by experts and citizen scientists.

We introduce a remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.

Protocol: Personality assessment as a support for referral and case-work in treatment for substance use disorders (PASRC-study).

BACKGROUND: Assessment of co-morbid personality disorders in substance use disorders may lead to important insights concerning individual patients. However, little is known about the potential value of routine personality disorder assessment in a clinical context. METHODS: Patients are adults with past-year substance dependence seeking treatment at a centralized intake unit for substance abusers in the City of Copenhagen. A randomized controlled trial of assessment of personality disorders and individual feedback vs. a general life situation interview. Patients are followed at 3 and 6 months post-treatment DISCUSSION: If routine personality assessment improves outcomes of substance abuse treatment, the clinical implication is to increase the use of personality disorder assessment in substance abuse treatment settings. TRIAL REGISTRATION: Current controlled trials ISRCTN39851689.