Is Zumba® Fitness Effective to Manage Overweight Without Dietary Intervention?

Background: Zumba® Fitness is a popular aerobic exercise and sometimes due to its high-intensity is considered sufficient for weight management,, from both trainers and trainees,, regardless of the type of diet followed. Its effectiveness in weight and body fat loss,, with or without dietary intervention has been slightly studied. Subjects and Methods: In the current randomized controlled trial,, thirty two healthy adult overweight women who attended exclusively Zumba® fitness for three times per week,, were randomly divided into 3 subgroups and received parallel dietary advice for two months: Group A did not receive dietary intervention (control group),. Group B received general healthy eating guidelines based on the Mediterranean pyramid and the food plate model and Group C individualized diet plan according anthropometric characteristics,, lifestyle,, and dietary habits. A Food Frequency Questionnaire used at baseline to assess dietary habits before the study,, and three 24-hour recalls evaluated compliance upon dietary intervention. Results: Significant reductions in body weight,, fat,, hip and waist circumference revealed in Group C, and in body fat of Group B. Conclusions: Zumba® fitness is enjoyable and could be used to enhance weight loss with appropriate dietary individualized advice in overweight subjects. In parallel,. it could be effective when combined with healthy eating guidance for improving fat loss and general well being

RAN-related neural-congruency: a machine learning approach toward the study of the neural underpinnings of naming speed

ObjectiveNaming speed, behaviorally measured via the serial Rapid automatized naming (RAN) test, is one of the most examined underlying cognitive factors of reading development and reading difficulties (RD). However, the unconstrained-reading format of serial RAN has made it challenging for traditional EEG analysis methods to extract neural components for studying the neural underpinnings of naming speed. The present study aims to explore a novel approach to isolate neural components during the serial RAN task that are (a) informative of group differences between children with dyslexia (DYS) and chronological age controls (CAC), (b) improve the power of analysis, and (c) are suitable for deciphering the neural underpinnings of naming speed.MethodsWe propose a novel machine-learning-based algorithm that extracts spatiotemporal neural components during serial RAN, termed RAN-related neural-congruency components. We demonstrate our approach on EEG and eye-tracking recordings from 60 children (30 DYS and 30 CAC), under phonologically or visually similar, and dissimilar control tasks.ResultsResults reveal significant differences in the RAN-related neural-congruency components between DYS and CAC groups in all four conditions.ConclusionRapid automatized naming-related neural-congruency components capture the neural activity of cognitive processes associated with naming speed and are informative of group differences between children with dyslexia and typically developing children.SignificanceWe propose the resulting RAN-related neural-components as a methodological framework to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties

Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer’s Brain Responses to Movie Trailers

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer’s decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers’ decision to watch a movie. Towards that, we recorded neural activity—through the use of EEG—and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie’s market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films’ performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain’s rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence

Eye-tracking based Methodological framework for optimal distribution of online advertisement locations

Similar with the rest of the world, Internet in Cyprus is now used as a mean of low cost targeted advertising. With more than 30 thousand Cypriot Websites and with Cyprus Web usage rising to 50,2% in 2010, online advertising is now the upcoming trend in advertising expenses in Cyprus. The increase in the number of commercial Cypriot Website, and consequently the increase of available online advertising spots raise the question: Which advertising locations has to be chosen by an agent in order to optimize the effectiveness and maximize the visibility of an online campaign? There are currently around 10 thousand available advertising positions and types of advertisements. Currently, online advertisement placement is highly subjective and is performed by an advertising agent who typically chooses the position based of his personal experience, generic statistical data and on the motto that “the higher the position of the ad on the page, the more effective” The project’s main aim is to implement an evaluation methodology service for the identification of the best locations on Cypriot web space based on eye tracking studies. This will first consist of the state-of-the-art analysis in existing patterns of advertisement placement on websites. Then user data will be collected with the use of eye tracking technologies in order to understand how users look at Web advertising and how effective each location is. A methodological framework will be then developed based on a prediction model for the optimization of the online advertising locations. Factors such as the advertising budget, social status, and Web use will be some of the many to be considered. This innovative project would benefit anyone who want to advertise on the Web since it will be based on factual research with an eye tracker and volunteers from Cyprus. Advertising will now be based on facts, not just speculative statistics. The outcome of this project will help the future of advertisement on the Web

Journal of Machine Learning Research XX (2007) XX-XX Submitted 09/06; Revised 02/07; Published XX/XX Bilinear Discriminant Component Analysis

Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination

Fixation-related potentials in naming speed : A combined EEG and eye-tracking study on children with dyslexia

Objective We combined electroencephalography (EEG) and eye-tracking recordings to examine the underlying factors elicited during the serial Rapid-Automatized Naming (RAN) task that may differentiate between children with dyslexia (DYS) and chronological age controls (CAC). Methods Thirty children with DYS and 30 CAC (Mage = 9.79 years; age range 7.6 through 12.1 years) performed a set of serial RAN tasks. We extracted fixation-related potentials (FRPs) under phonologically similar (rime-confound) or visually similar (resembling lowercase letters) and dissimilar (non-confounding and discrete uppercase letters, respectively) control tasks. Results Results revealed significant differences in FRP amplitudes between DYS and CAC groups under the phonologically similar and phonologically non-confounding conditions. No differences were observed in the case of the visual conditions. Moreover, regression analysis showed that the average amplitude of the extracted components significantly predicted RAN performance. Conclusion FRPs capture neural components during the serial RAN task informative of differences between DYS and CAC and establish a relationship between neurocognitive processes during serial RAN and dyslexia. Significance We suggest our approach as a methodological model for the concurrent analysis of neurophysiological and eye-gaze data to decipher the role of RAN in reading.peerReviewe