Anxiety, sleep habits and executive function during the COVID-19 pandemic through parents' perception: a longitudinal study.

The present study therefore aims to examine trait and state anxiety, sleep habits and executive functioning during 1 year and a half of the COVID-19 pandemic in children and adolescents through the lens of parents. Assessments were conducted at three different times: April 2020 (T1), October 2020 (T2) and October 2021 (T3). The main sample included 953 children and adolescents aged 6 to 18 years, and scales were used to assess anxiety (STAIC), sleep habits (BEARS) and executive functioning (BRIEF-2). The results showed that 6 months after the outbreak of the pandemic, state and trait anxiety, sleep disturbances and executive dysfunctions increased significantly. One and a half year later, trait anxiety and sleep disturbances have been maintained, while state anxiety and executive dysfunction have decreased their scores obtaining scores similar to those of April 2020. In conclusion, there has been a further decrease in children and adolescents' mental health since the beginning of the pandemic, and it seems to remain at the present time, such as trait anxiety as a part of the personality.

Physical Fitness Level Is Related to Attention and Concentration in Adolescents.

The main goal of this study was to analyze the relationships among physical fitness, selective attention and concentration in a group of 210 teenagers (43.81% male, 56.19% female) from the city of Málaga (Spain), aged between 11 and 15 years old (M = 13.27, SD = 1.80) that participated in the study. D2 attention test was used in order to analyze selective attention and concentration. Physical fitness was evaluated using the horizontal jump test, the Course Navette test and the 5 × 10 meters speed test. The analysis taken indicated a significant relationship among the physical fitness level, the attention and the concentration, as in the general sample as looking at gender. Linear regression tests performed showed that oxygen consumption is the best predictor of attentional parameters. Cluster analysis shows two groups characterized by a greater or lower physical fitness level. So, the highest physical fitness level group scores better in the attention (e.g., boys: p < 0.001, d' Cohen = 1.01, 95% CI [0.57, 1.44]; girls: p < 0.01, d' Cohen = 0.61, 95% CI [0.24, 0.98]) and the concentration tests (e.g., boys: p < 0.001, d' Cohen = 0.89, 95% CI [0.46, 1.32]; girls: p < 0.01, d' Cohen = 0.58, 95% CI [0.21, 0.95]). Results indicate that physical fitness analysis can be used as a tool for observing differences in the attention and concentration level of the analyzed adolescents, suggesting that a physical performance improvement could be an adequate procedure to develop some cognitive functions during adolescence.

Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes.

Data mining is seen as a set of techniques and technologies allowing to extract, automatically or semi-automatically, a lot of useful information, models, and tendencies from a big set of data. Techniques like "clustering," "classification," "association," and "regression"; statistics and Bayesian calculations; or intelligent artificial algorithms like neural networks will be used to extract patterns from data, and the main goal to achieve those patterns will be to explain and to predict their behavior. So, data are the source that becomes relevant information. Research data are gathered as numbers (quantitative data) as well as symbolic values (qualitative data). Useful knowledge is extracted (mined) from a huge amount of data. Such kind of knowledge will allow setting relationships among attributes or data sets, clustering similar data, classifying attribute relationships, and showing information that could be hidden or lost in a vast quantity of data when data mining is not used. Combination of quantitative and qualitative data is the essence of mixed methods: on one hand, a coherent integration of result data interpretation starting from separate analysis, and on the other hand, making data transformation from qualitative to quantitative and 1 vice versa. A study developed shows how data mining techniques can be a very interesting complement to mixed methods, because such techniques can work with qualitative and quantitative data together, obtaining numeric analysis from qualitative data based on Bayesian probability calculation or transforming quantitative into qualitative data using discretization techniques. As a study case, the Psychological Inventory of Sports Performance (IPED) has been mined and decision trees have been developed in order to check any relationships among the "Self-confidence" (AC), "Negative Coping Control" (CAN), "Attention Control" (CAT), "Visuoimaginative Control" (CVI), "Motivational Level" (NM), "Positive Coping Control" (CAP), and "Attitudinal Control" (CACT) factors against gender and age of athletes. These decision trees can also be used for future data predictions or assumptions.