Introduction: Applied occupational health research is complex and requires transparent communication between stakeholders to facilitate development and implementation of injury management strategies. Firefighting as a physically demanding, male-dominant occupation provides a unique context to develop and implement injury management strategies. Thesis Objectives: To investigate how individuals perform occupational tasks within a physically demanding occupational context considering the interrelationship between personal, task and environmental factors. A secondary objective was to evaluate the research partnership as integrated knowledge translation using the Knowledge-to-Action (KTA) Framework. Methods. A qualitative study was used to identify barriers and facilitators experienced by female firefighters. Dartfish movement analysis software was adapted to analyze kinematics associated with firefighter tasks. Relative and absolute reliability was used to establish measurement properties of Dartfish methods. Regression models identified the relative importance of individual factors on firefighter task performance. A case study of the research partnership using knowledge translation (KT) theory identified critical phases in developing occupational health research partnerships. Results. The qualitative study identified sex/gender and task/environment effects on firefighter task performance and injury risk. Tracking positional co-ordinate data using Dartfish demonstrated excellent relative reliability (ICC2,1 = 0.84-0.99) and lowest absolute reliability (SEM = 0.01m-0.11m). Strength was the strongest independent predictor of firefighter task performance time where increasing strength was associated with faster time. The case study identified components of the KTA Action Cycle that supported and described collaborative occupational health research. Conclusions. Although female firefighters share commonalities with their male counterparts, unique personal attributes and social experiences affect how they experience firefighting. Dartfish provides a reliable tool to measure kinematics in an applied context. Although refinements are required, recommendations for data collection and extraction using Dartfish in occupational contexts are provided. Occupational health research imbedded in KTA cycle confirms the importance of partnership with stakeholders to ensure the feasibility and relevance of the research. / Thesis / Candidate in Philosophy

En Perú los tipos de diabetes que presentan mayor incidencia en pacientes son: Diabetes mellitus tipo 1, Diabetes mellitus tipo 2 y diabetes gestacional. De acuerdo con el registro en la situación de diabetes según datos del sistema de vigilancia en el Perú (Ministerio de salud) desde el 2018 hasta el 2021 el tipo de diabetes con mayor predominio y casos registrados es la DM tipo 2 con un 96.7%, siguiendo la DM tipo 1 con 2.1%, la diabetes gestacional con 0.9% y en la región Lambayeque el 5% de personas padece diabetes. La DM tipo 2 tiene mayor preponderancia y causante principal de amputaciones en los miembros inferiores, con un 19% en personas masculinas y el 28.3% en personas femeninas con riesgo muy elevado debido al pie diabético, afectando de manera drástica el estilo de vida al ciudadano ya que deben continuar con el uso de muletas o en silla de ruedas, limitándolos a actividades físicas en su vida diaria. En la actualidad se debe tener en cuenta la ergonomía, el adecuado diseño y para prótesis que utilizarán personas con diabetes los materiales utilizados deben ser adecuados para no generar contusiones, irritabilidad, alergia, inflamación en el muñón de la persona amputada de manera transfemoral. La presente tesis tiene por objetivo diseñar una prótesis transfemoral, utilizando la matriz morfológica, seleccionando materiales, mediante el software SolidWorks realizando el análisis estático lineal, verificando que el diseño sea seguro con la finalidad de permitir la marcha normal de la persona, mejorando su estilo de vida. / In Peru, the types of diabetes that have the highest incidence in patients are Diabetes mellitus type 1, Diabetes mellitus type 2 and gestational diabetes. According to data from the surveillance system in Peru (Ministry of Health), from 2018 to 2021, the type of diabetes with the highest prevalence and recorded cases is type 2 DM with 96.7%, followed by type 1 DM with 2.1%, gestational diabetes with 0.9% and in the Lambayeque region 5% of people suffer from diabetes. Type 2 DM is more prevalent and the main cause of amputations in the lower limbs, with 19% in males and 28.3% in females with a very high risk due to diabetic foot, drastically affecting the lifestyle of the citizen since they must continue to use crutches or wheelchairs, limiting them to physical activities in their lives. daily. At present, ergonomics must be taken into account, the appropriate design and for prostheses to be used by people with diabetes, the materials used must be suitable to avoid causing contusions, irritability, allergy, inflammation in the stump of the transfemoral amputee. This thesis aims to design a transfemoral prosthesis, using the morphological matrix, selecting materials, using SolidWorks software, performing linear static analysis, verifying that the design is safe in order to allow the normal walking of the person, improving their lifestyle.

[pt] Dores no pé e joelho estão relacionadas com patologias ortopédicas e lesões nos membros inferiores. Desde a corrida de rua até o treinamento funcional CrossFit, estas dores e lesões estão correlacionadas com a distribuição iregular da pressão plantar e o posicionamento inadequado do joelho durante a prática física de longo prazo, e podem levar a lesões ortopédicas graves se o padrão de movimento não for corrigido. Portanto, o monitoramento da distribuição da pressão plantar do pé e das características espaciais e temporais das irregularidades no posicionamento dos pés e joelhos são de extrema importância para a prevenção de lesões. Este trabalho propõe uma plataforma, composta de uma rede de sensores vestíveis e um classificador de Reconhecimento de Atividade Humana (HAR), para fornecer feedback em tempo real de exercícios funcionais, visando auxiliar educadores físicos a reduzir a probabilidade de lesões durante o treinamento. Realizamos um experimento com 12 voluntários diversos para construir um classificador HAR com aproximadamente de 87 porcento de precisão geral na classificação, e um segundo experimento para validar nosso modelo de avaliação física. Por fim, realizamos uma entrevista semi estruturada para avaliar questões de usabilidade e experiência do usuário da plataforma proposta.Visando uma pesquisa replicável, fornecemos informações completas sobre o hardware e o código fonte do sistema, e disponibilizamos o conjunto de dados do experimento. / [en] Foot and knee pain fave been associated with numerous orthopedic pathologies and injuries of the lower limbs. From street running to CrossFitTM functional training, these common pains and injuries correlate highly with unevenly distributed plantar pressure and knee positioning during long-term physical practice and can lead to severe orthopedic injuries if the movement pattern is not amended. Therefore, the monitoring of foot plantar pressure distribution and the spatial and temporal characteristics of foot and knee positioning abnomalities is of utmost importance for injury prevention. This work proposes a platform, composed af an lot wearable body sensor network and a Human Activity Recognition (HAR), to provide realtime feedback of functional exercises, aiming to enhace physical educators capability to mitigate the probability of injuries during training. We conducted an experiment with 12 diverse volunteers to build a HAR classifier that achieved about 87 percent overall classification accuracy, and a second experiment to validate our physical evaluation model. Finally, we performed a semi-structured interview to evaluate usability and user experience issues regarding the proposed platform. Aiming at a replicable research, we provide full hardware information, system source code and a public domain dataset.