Effects of Movement Behaviors on Overall Health and Appetite Control: Current Evidence and Perspectives in Children and Adolescents

Purpose of Review To present the definitions and recommendations for movement behaviors in children and adolescents, including physical activity (PA), sedentary behaviors (SB), and sleep, and to provide an overview regarding their impact on health and obesity outcomes from childhood to adulthood, as well as interactions with appetite control. Recent Findings PA represents a variable proportion of daily energy expenditure and one can be active with high SB or vice versa. Studies have described movements across the whole day on a continuum from sleep to SB to varying intensities of PA. More PA, less SB (e.g., less screen time) and longer sleep are positively associated with indicators of physical health (e.g., lower BMI, adiposity, cardiometabolic risk) and cognitive development (e.g., motor skills, academic achievement). However, less than 10% of children currently meet recommendations for all three movement behaviors. Movement behaviors, adiposity, and related cardiometabolic diseases in childhood track into adolescence and adulthood. Furthermore, low PA/high SB profiles are associated with increased energy intake. Recent studies investigating energy balance regulation showed that desirable movement behavior profiles are associated with better appetite control and improved eating habits. Summary Early identification of behavioral phenotypes and a comprehensive approach addressing all key behaviors that directly affect energy balance will allow for individual strategies to prevent or treat obesity and its comorbidities. Investigating exercise as a potential “corrector” of impaired appetite control offers a promising weight management approach.

Movement behavior of residual oil droplets and CO2: insights from molecular dynamics simulations

After primary and secondary recovery of tight reservoirs, it becomes increasingly challenging to recover the remaining oil. Therefore, improving the recovery of the remaining oil is of great importance. Herein, molecular dynamics simulation (MD) of residual oil droplet movement behavior under CO2 displacement was conducted in a silica nanopores model. In this research, the movement behavior of CO2 in contact with residual oil droplets under different temperatures was analyzed, and the distribution of molecules number of CO2 and residual oil droplets was investigated. Then, the changes in pressure, kinetic energy, potential energy, van der Waals' force, Coulomb energy, long-range Coulomb potential, bond energy, and angular energy with time in the system after the contact between CO2 and residual oil droplets were studied. At last, the g(r) distribution of CO2–CO2, CO2-oil molecules, and oil molecules-oil molecules at different temperatures was deliberated. According to the results, the diffusion of CO2 can destroy residual oil droplets formed by the n-nonane and simultaneously peel off the n-nonane molecules that attach to SiO2 and graphene nanosheets (GN). The cutoff radius r of the CO2–CO2 is approximately 0.255 nm and that of the C–CO2 is 0.285 nm. The atomic force between CO2 and CO2 is relatively stronger. There is little effect caused by changing temperature on the radius where the maximum peak occurs in the radial distribution function (RDF)-g(r) of CO2–CO2 and C–CO2. The maximum peak of g(r) distribution of the CO2–CO2 in the system declines first and then rises with increasing temperature, while that of g(r) distribution of C–CO2 changes in the opposite way. At different temperatures, after the peak of g(r), its curve decreases with the increase in radius. The coordination number around C9H20 decreases, and the distribution of C9H20 becomes loose.

The Feasibility and Challenges of Conducting Online Research to Examine Movement Behavior in Parents and Children During the COVID-19 Pandemic

The global pandemic of COVID-19 shifted the methodology of this research project. The purpose of this perspective article is to discuss the feasibility and challenges of converting an in-person mixed methods study that examined associations among and beliefs about physical activity, motor competence, and perceived competence to an online format with parents and children during the COVID-19 pandemic. Recruitment was conducted through a University research registry, social media, and public listservs. All correspondence with participants was through email and secure platforms. Physical activity was assessed with accelerometers mailed to participants. Motor competence was assessed through participant-filmed trials of motor skills. Perceived competence was assessed with the Self-Perception Profile for Adults and Children delivered on Qualtrics. Semi- structured interviews to examine beliefs were conducted over Zoom. Approximately 200 families expressed interest in the study, 76 parent-child dyads consented and assented, and 61 parent-child dyads completed at least one component of the study. It is feasible to conduct online research that contributes to scientific knowledge and has potential advantages. However, various challenges need to be considered regarding the application of online research. These challenges included recruitment, the data collection process, and data quality. Future research needs to address these challenges by utilizing wide-reaching and diverse recruitment methods, easing participants' burden with technology, and developing motor competence and perceived competence assessments that can be administered online. The way research was conducted changed due to COVID-19 and adapting to and/or integrating online methods is both necessary and feasible, but modifications must be taken into consideration.

Potsdam Eye-Movement Corpus for Scene Memorization and Search with Color and Spatial-Frequency Filtering

The availability of large eye-movement corpora has become increasingly important over the past years. In scene viewing, scan-path analyses of time-ordered fixations, for example, allow for investigating individual differences in spatial correlations between fixation locations, or for predicting individual viewing behavior in the context of computational models. However, time-dependent analyses require many fixations per scene, and only few large eye-movement corpora are publicly available. This manuscript presents a new corpus with eye-movement data from two hundred participants. Viewers memorized or searched either color or grayscale scenes while high or low spatial frequencies were filtered in central or peripheral vision. Our database provides the scenes from the experiment with corresponding object annotations, preprocessed eye-movement data, and heatmaps and fixation clusters based on empirical fixation locations. Besides time-dependent analyses, the corpus data allow for investigating questions that have received little attention in scene-viewing research so far: (i) eye-movement behavior under different task instructions, (ii) the importance of color and spatial frequencies when performing these tasks, and (iii) the individual roles and interaction of central and peripheral vision during scene viewing. Furthermore, the corpus allows for validation of computational models of attention and eye-movement control, and finally, analyses on an object- or cluster-based level.

The effect of landscape fragmentation on Turing-pattern formation

<abstract><p>Diffusion-driven instability and Turing pattern formation are a well-known mechanism by which the local interaction of species, combined with random spatial movement, can generate stable patterns of population densities in the absence of spatial heterogeneity of the underlying medium. Some examples of such patterns exist in ecological interactions between predator and prey, but the conditions required for these patterns are not easily satisfied in ecological systems. At the same time, most ecological systems exist in heterogeneous landscapes, and landscape heterogeneity can affect species interactions and individual movement behavior. In this work, we explore whether and how landscape heterogeneity might facilitate Turing pattern formation in predator–prey interactions. We formulate reaction-diffusion equations for two interacting species on an infinite patchy landscape, consisting of two types of periodically alternating patches. Population dynamics and movement behavior differ between patch types, and individuals may have a preference for one of the two habitat types. We apply homogenization theory to derive an appropriately averaged model, to which we apply stability analysis for Turing patterns. We then study three scenarios in detail and find mechanisms by which diffusion-driven instabilities may arise even if the local interaction and movement rates do not indicate it.</p></abstract>

Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.

The Topodiverse City: Urban Form for Subjective Well-Being

Research is now better than ever able to unveil how urban inhabitants’ movement, behavior and experiences relate to the urban forms in which they take place. Consequently, urban form might increasingly be able to function as a focal point for different strands of research that focus on sustainable urban life, and as a link between research and planning practice through the development of empirically informed design principles. Drawing on literature from urban morphology, complex systems analysis, environmental psychology, and neuroscience, I provide a wide-angle view of how urban form relates to subjective well-being through movement, social and economic activity, experiences and psychological restoration. I propose three principles for urban form that could promote subjective well-being while also mitigating the environmental impact of cities in industrialized societies. The principles revolve around so-called topodiversity, meaning variation across an urban area in spatial conditions that allows subjective well-being to be promoted through several different pathways. The principles together suggest an urban form that I call the topodiverse city. The topodiverse city displays a polycentric structure and is more spatially contained than the sprawling city, yet not as compact as the dense city. I also propose indicators to measure the principles using mostly openly available data and analysis methods, to further research on how urban form can enable urban subjective well-being with low environmental impact.

The Development of Explicit and Implicit Game-Based Digital Behavioral Markers for the Assessment of Social Anxiety

Social relationships are essential for humans; neglecting our social needs can reduce wellbeing or even lead to the development of more severe issues such as depression or substance dependency. Although essential, some individuals face major challenges in forming and maintaining social relationships due to the experience of social anxiety. The burden of social anxiety can be reduced through accessible assessment that leads to treatment. However, socially anxious individuals who seek help face many barriers stemming from geography, fear, or disparities in access to systems of care. But recent research suggested digital behavioral markers as a way to deliver cheap and easily accessible digital assessment for social anxiety: As earlier work shows, players with social anxiety show similar behaviors in virtual worlds as in the physical world, including tending to walk farther around other avatars and standing farther away from other avatars. The characteristics of the movement behavior in-game can be harnessed for the development of digital behavioral markers for the assessment of social anxiety. In this paper, we investigate whether implicit as well as explicit digital behavioral markers, proposed by prior work, for social anxiety can be used for predicting the level of social anxiety. We show that both, explicit and implicit digital behavioral markers can be harnessed for the assessment. Our findings provide further insights about how game-based digital behavioral markers can be used for the assessment of social anxiety.

An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA.