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Eating disorder symptoms and weight pressure in female rowers: associations between self-concept, psychological well-being and body composition
(BioMed Central Ltd, 2024-06-14) Larrinaga García, Beñat; Borrajo, Erika; Muñoz Pérez, Iker; Urquijo Cela, Itziar; García Rodríguez, Ana; Arbillaga Etxarri, Ane
Background: Female rowers may be at risk of eating disorders and high weight pressure. Aim: The purpose of the study was to investigate the prevalence of disordered eating symptoms and weight-related pressure and the associations with self-concept, psychological well-being, socio-demographic data, experience, performance level and body composition in female fixed-bench rowers. Methods: Female rowers (n = 208; age ranged mean ± SD 23.6 ± 6.5 years) completed the SCOFF scale, Weight-Pressures in Sport-Females (WPS-F), Physical Self-Concept Questionnaire and the Ryff scales of psychological well-being and provided information on their experience and level of competition. In a subgroup of 115 athletes, body composition was assessed using bioimpedance. Results: It was found that 42.3% of the athletes scored ≥ 2 on SCOFF and mean ± SD value of WPS-F score was 3.65 ± 0.82. Stepwise regression analysis revealed that self-concept of strength and pressure from teammates and the uniform were associated with higher ED symptoms, whereas better psychological well-being in terms of autonomy, self-concept of attractiveness, and age were protective factors for ED symptoms. BMI, athletes’ physical condition, strength, and experience were associated with more weight-related pressure and better self-concept of attractiveness and physical well-being of autonomy were significantly associated with less pressure. In body composition analysis, higher extra cellular water, self-acceptance, and physical condition were associated with more weight-related pressure in female rowers, being attractiveness and the environmental mastery protective elements. Conclusions: The prevalence of ED symptomatology and weight-related pressure are high in female fixed bench rowing. The psychological factors of well-being and self-concept, team environment, body image concerns and body composition analysis should be considered to promote healthy eating behaviours in female rowers.
Análisis del efecto de la pretemporada en los parámetros morfológicos y fisiológicos en remo tradicional femenino. Traineras
(Consejo General de Colegios Oficiales de Licenciados en Educación Física y en Ciencias de la Actividad Física y del Deporte de España, 2024) Larrinaga García, Beñat; Santiesteban Leguina, Aitor; Catañeda Babarro, Arkaitz; Coca Núñez, Aitor; Arbillaga Etxarri, Ane
Este estudio descriptivo tiene como objetivo dual el establecer valores específicos inéditos en la modalidad de traineras y, por otro lado, examinar los cambios morfológicos y fisiológicos durante la pretemporada en remeras mediante una valoración pre-post. Participaron 22 remeras de remo tradicional con una edad de 24,69 ± 4,26 años y una experiencia de 5,73 ± 4,55, sometiéndose a pruebas de composición corporal y de ergometría en dos momentos, de la pretemporada, la evaluación denominada “pre” se realizó a mediados de enero, durante el periodo preparatorio de la pretemporada y tras un entrenamiento controlado de 19 semanas se realizó la medición denomina “post” justo antes del inicio del periodo competitivo. Tras la valoración pre-post, se mostró que el entrenamiento físico aplicado durante la pretemporada mediante el programa combinado de resistencia aeróbica y entrenamiento de la fuerza es efectivo para obtener cambios significativos (p < 0,05) pen la composición corporal con bajada de la masa grasa de 14,40 ± 4,49 a 13,40 ± 3,41 y aumentando el porcentaje de masa muscular de 42,90% a 43,70%. A su vez, las mejoras en parámetros fisiológicos reflejaron un aumento en la potencia máxima de 341W a 368W, Potencia aeróbica máxima de 170W a 186 W así como en su valor relativo y la mejora significativa de los umbrales VT1, VT2 y Dmax con una mejora de 10W, 10W y 7W respectivamente, sin llegar a mejorar el VO2máx. Considerando los valores analizados, al realizar las evaluaciones pre-post se proporciona información valiosa para atletas, entrenadores y científicos del deporte.
Análisis de los parámetros de rendimiento del remo de traineras: una revisión sistemática
(Federación Española de Asociaciones de Docentes de Educación Física (FEADEF), 2023) Larrinaga García, Beñat; León Guereño, Patxi; Arbillaga Etxarri, Ane; Coca Núñez, Aitor
Objetivo: recopilar la información científica relacionada con parámetros fisiológicos, biomecánicos, antropométricos y de entrenamiento del deporte de traineras, comparar las investigaciones científicas que hay entre géneros y clasificar por temáticas la información. Métodos: Estudio de tipo revisión sistemática, en el cual se realizó una búsqueda sistemática en seis bases de datos (PudMed Central, PudMed, Web of Science, Scopus, DialnetPlus y Google Scholar). Después de analizar los estudios logrados con la ecuación de búsqueda, fue preciso considerar su utilidad y relevancia con respecto a la revisión. Resultados: tras el cribado y la evaluación metodológica correspondiente, 21 estudios cumplieron con los criterios de inclusión. Los resultados, muestran los artículos dirigidos a diferentes temáticas como fisiología, biomecánica, parámetros antropológicos y otras temáticas relacionadas con el rendimiento. El análisis de los participantes de los estudios muestra que dos de los estudios han tenido en cuenta en la participación deportistas femeninas. Conclusión: La literatura existente muestra el gran potencial de esta actividad para mejorar parámetros de capacidad condicionales de rendimiento. A su vez, la comparación de la muestra entre géneros afirma la escasa investigación que hay sobre el remo de traineras femenino y se detecta una carencia de los estudios de campo y situaciones de competición.
Modelling of biomedical data using Quantum Computing approach
(Universidad de Deusto, 2023-03-13) Maheshwari, Danyal; García-Zapirain, Begoña; Sierra Sosa, Daniel Esteban; Facultad de Ingeniería, Programa de Doctorado en Ingeniería para la Sociedad de la Información y Desarrollo Sostenible por la Universidad de Deusto
Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data.
In the first case study of the diabetes dataset, we used two different approaches. In the first approach, we presented a Quantum versus classical implementation of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system.
In the second approach, we presented the application of a Variational Quantum Classifier (VQC) for binary classification of the diabetes disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.
In the second case study, we presented cardiovascular diseases (CVD) as conditions affecting the heart and blood vessels. Most approaches for the prediction of ischemic heart disease (IHD) have centered on pain symptoms, age, and gender. However, numerous variables have been identified as determining risk factors for developing IHD. This case study presents a collection of computationally efficient QML algorithms for cardiovascular illness classification, including Optimized Quantum Support Vector Machine (OQSVM) and Hybrid Quantum Multi-Layer Perceptron (HQMLP). Effective pre-processing and feature selection techniques, such as wrapper and filter, enhance prediction rate and ensure the robustness of the proposed frameworks. The proposed model performance metrics are compared to those of recently published and conventional models with complex architectures. The greatest accuracy of the proposed Support Vector Machine (SVM), OQSVM, Multilayer Perceptron (MLP), and HQMLP models are 96%, 94%, 94%, and 93%, respectively, when 10 features of the cardiovascular dataset are taken into consideration.
Furthermore, the our proposed studies are computationally efficient and have the potential to be beneficial in real-time healthcare applications. This Ph.D. dissertation presents one conference and 2 journals published articles in the Q1 journals.
Teachers’ learning and pedagogical beliefs change through Lesson Study
(Universidad de Deusto, 2022-10-14) Khokhotva, Olga; Elexpuru Albizuri, Iciar; Facultad de Educación y Deporte, Programa de Doctorado en Educación por la Universidad de Deusto
This thesis is about passion for teaching, learning and intellectual and emotional growth for the betterment of society. On the central stage are the teachers, whose values, moral purpose, passion, commitment and collaborative agency kindles endless aspiration for improving teaching and learning to ensure the well-being and happiness of students.
This thesis is about enthusiastic and passionate educators open to innovation and experimenting with new approaches to professional learning and development in the context of Kazakhstan and the Basque Autonomous Community, Spain. It is about eight English as Foreign Language teachers who, along with the author of this thesis, set out on a journey of exploring their teaching practice with Lesson Study, and the outcomes of such an endeavour.
Specifically, the outcomes have been presented through a compendium of four consecutive publications bound by the logic of an action research project where each research question is shaped by the preceding research loop. Sculpted by four research questions, the exploration of teachers' learning and pedagogical beliefs change through Lesson Study commences in the context of educational Reform at Scale in Kazakhstan and finalises in the context of a school in the Basque Country, Spain. Each publication showcases a separate case study for which a unique literature review was compiled and specific and original theoretical and analytical frameworks were built.