The aim of this study was to analyze the relationships among physical cognitive ability, academic performance, and physical fitness regarding age and sex in a group of 187 students (53.48% male, 46.52% female) from one town of Norwest of Jaén, Andalusia (Spain), aged between 9 and 15 years old (M = 11.97, SD = 1.99). The D2 attention test was used in order to analyze selective attention and concentration. Physical fitness, reflected on maximal oxygen uptake (VO), was evaluated using the 6 min Walking Test (6MWT). The analysis taken indicated a significant relationship between physical fitness level, attention, and concentration, as in the general sample looking at sex (finding differences between boys and girls in some DA score in almost all age categories [p < 0.05]) and at age category (finding some differences between the younger age category groups and the older age category groups in some DA scores (p < 0.05), not finding any significant interaction between sex and age category (p > 0.05). In sum, the present study revealed that students with better aerobic fitness can present better-processed elements and smaller omission errors. Moreover, girls and older students seem to present better cognitive functioning scores than boys and younger. Our findings suggest that more research is necessary to elucidate the cognitive function between ages, sexes, and physical fitness and anthropometry levels of students.
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http://dx.doi.org/10.1186/s12887-023-04028-8 | DOI Listing |
Nutrients
January 2025
Health and Social Research Center, Universidad de Castilla La-Mancha, 16071 Cuenca, Spain.
Background/objectives: recent studies have suggested that components typical of the Mediterranean diet (MedDiet) are associated with depression and anxiety prevention. In this sense, the main objective of this study was to analyse the associations between adherence to the MedDiet and depression and anxiety symptoms and to examine whether this relationship is mediated by lean mass and the muscle strength index (MSI).
Methods: a cross-sectional study (based on data obtained from the Nuts4Brain-Z study) was conducted from 2023-2024, involving 428 university students, aged 18-30 years, from a Spanish public university.
Nutrients
January 2025
Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA.
The undergraduate college years are a critical transition period for young adults in establishing life-long health behaviors. : Within the FRESH Study, we aimed to understand the relationship between perceived physical health, perceived mental health, and specific health metrics (e.g.
View Article and Find Full Text PDFNutrients
January 2025
Department of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan.
Objective: In treating obesity, energy intake control is essential to avoid exceeding energy expenditure. However, excessive restriction of energy intake often leads to resting energy expenditure (REE) reduction, increasing hunger and making weight loss difficult. This study aimed to investigate whether providing nutritional guidance that considers energy expenditure based on the regular evaluation of REE and physical activity could effectively reduce body weight (BW) in patients with obesity.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Wearable and Gait Assessment Research (WAGAR) Group, Prince of Wales Private Hospital, Randwick, NSW 2031, Australia.
Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
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