ESS - BBB - Comunicações em eventos científicos
URI permanente para esta coleção:
Navegar
Entradas recentes
- Contributions to the validation of a European Portuguese version of Cornell Musculoskeletal Discomfort QuestionnairePublication . Vieira, F.; Alves, Sandra Maria; Silva, M. A.; Rodrigues, A.; Santos, J.; Pimenta, Rui Esteves; Pimenta, Rui; Alves, Sandra MariaThe European Agency for Safety and Health at Work reported, in 2020, that 22% of European workers experience persistent musculoskeletal pain, with WMSDs accounting for 60% of occupational diseases. Lower back pain and upper limb disorders are major causes of absenteeism and reduced productivity. The Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) is a widely used tool for assessing the frequency, severity, and interference of work-related musculoskeletal disorders (WRMSDs) across 20 body regions. This study aimed to translate and validate the CMDQ for European Portuguese, enhancing its applicability for assessing WRMSDs in Portugal.
- Nasa task load index: Preliminary results from the Portuguese adaptationPublication . Rodrigues, A.; Pimenta, Rui Esteves; Alves, Sandra Maria; Silva, M.; Vieira, F.; Santos, J.; Pimenta, RuiThe NASA Task Load Index (TLX) is a subjective method for assessing workload. Mental workload is a psychosocial risk factor that can negatively impact both the physical and mental health of workers. Early assessment and identification of these risks can lead to more effective diagnosis and treatment, enhancing human performance. The NASA-TLX assesses workload across six domains: mental, physical, temporal demands, performance, effort and frustration level. The aim of this work was producing a Portuguese adaptation of the NASA-TLX scale.
- Preliminary results of a portuguese version of Prevent for Work Questionnaire (P4WQ)Publication . Silva, M.A.; Pimenta, Rui Esteves; Rodrigues, A.; Vieira, F.; Alves, Sandra Maria; Santos, J.; Pimenta, RuiMusculoskeletal disorders (MSDs) are among the most prevalent work-related health conditions. While both occupational and non-occupational factors contribute to the prevalence of work-related musculoskeletal disorders (WRMSDs), psychosocial risks have gained increasing importance in understanding their determinants. The Prevent for Work questionnaire (P4Wq) is a 20-item tool based on a biopsychosocial model. It assesses risk factors associated with WRMSDs, addressing both well-established physical risks and emerging psychosocial factors. Using validated tools in the local language enhances result comparability and generalizability, supporting more effective interventions and policy recommendations tailored to the population's needs. The main aim of this study was to translate and validate the P4Wq for the Portuguese. The P4Wq identifies risk groups and provides data for defining priorities, pedagogical actions, and preventive measures related to WRMSDs.
- A virtual reality game to support cognitive stimulation in elderly people with Alzheimer's and Mild DementiaPublication . Barbosa, Alice; Melro, Henrique; Pacheco, Rodrigo; Almeida, Raquel Simões; Teixeira, Bruno; Sá, Vitor J.; Simões de Almeida, Raquel; Sá, Vítor J.This article presents the development of ReLembra, an immersive Virtual Reality game designed to support elderly people with Alzheimer's and mild dementia in preserving their cognitive abilities, that is, in preventing activities and some motor functions for daily life. Through interactive activities in calm and familiar virtual environments, the games promote the stimulation of memory, language, organization and spatial orientation. The application was developed in the Unity 3D tool and optimized for the Meta Quest 3 device. Tests were carried out with five participants, evaluating presence (IPQ), cybersickness (SSQ), usability (SUS) and performance metrics. The results indicate high usability and excellent acceptance. However, some limitations related to the digital literacy of users and the difficulty in distinguishing between the virtual and the real world were identified. This project represents an innovative and promising tool to support health professionals and caregivers, with potential for future expansion.
- Switch para biossimilares de anticorpos monoclonais: análise económica num hospital especializado de PortugalPublication . Machado, Sara; Cruz, Agostinho; Pimenta, Rui; Pimenta, RuiSe as quotas de biossimilares (SBMs) nos hospitais do Serviço Nacional de Saúde (SNS) português fossem equivalentes às mais altas, a poupança anual estimada ascenderia a 13,9 milhões de euros [1]. O processo de switch, contributo indiscutível para a sustentabilidade do SNS, depara-se ainda com obstáculos que terão de ser ultrapassados com vista a uma maior adoção de SBMs em Portugal [2]. Deste modo, torna-se imperioso encontrar formas de o ultrapassar. Analisar o impacte económico do processo de switch para SBMs de anticorpos monoclonais num hospital especializado de Portugal. Foi desenvolvido um estudo observacional, longitudinal e descritivo a partir de dados secundários relativos aos três SBMs de anticorpos monoclonais (mAbs) utilizados em oncologia (rituximab, trastuzumabe bevacizumab). O período selecionado para análise foi entre janeiro 2018 e dezembro de 2022. Foi aplicado um modelo para análise das quotas e poupanças[3]. Em 2022, os SBMsde rituximab apresentaram quota próximo dos 100%, revelando a transição quase completa dos doentes oncológicos para a opção SBM. Por outro lado, as quotas em valor para as moléculas SBMs de trastuzumabe bevacizumab foram inferiores às do medicamento biológico de referência, ao longo dos anos de estudo. Deste modo, foi realizado o cálculo das potenciais poupanças resultantes do aumento do consumo de SBMs através de um processo de switch. Para estes dois medicamentos, a potencial poupança anual poderia atingir os 4 milhões de euros para a instituição. Este estudo demonstrou a melhor relação custo-efetividade nos tratamentos com mAbs através do processo de switch para SBMs. No entanto, em Portugal, verifica-se a ausência de normas em relação à interpermutabilidade e ao switch destes fármacos, tornando necessário a implementação de medidas políticas que incrementem a utilização de SBMs na área da oncologia.
- From controlled to chaotic: Disparities in laboratory vs real-world stress detectionPublication . Ferreira, Simão; Rodrigues, Fátima; Kallio, Johanna; Coelho, Filipe; Kyllonen, Vesa; Rocha, Nuno; Rodrigues, Matilde A.; Vildjiounaite, Elena; Ferreira, Simão; Rodrigues, MatildeThis paper explores the discrepancies between laboratory and real-world stress detection, emphasizing the pronounced differences in data loss, data preprocessing, feature design, and classifier selection. Laboratory studies offer a controlled environment that optimizes data quality, whereas real-world settings introduce chaotic and unpredictable elements, coupled with a diverse range of human behaviours, resulting in substantial data loss and compromised data quality. We discuss the development of stress detectors for two distinct types of data: physiological and behavioural. We also address the specific challenges associated with designing effective stress detection systems for each data type and compare the features and classifiers used in both laboratory and real-world contexts. Additionally, this paper proposes future research directions aimed at crafting stress detectors that are robust and effective in real-life scenarios.
- Virtual journey through an immersive interactive environment: A sensory exploration of an artistic space modeled by dynamic painting and emotional music by Domingos MateusPublication . Gomes, Paulo Veloso; Sá, Vítor J.; Donga, João; Marques, António; Mateus, Domingos; Machado Veloso Gomes, Paulo Sérgio; Rucha das Dores da Costa Donga, João Paulo; Pereira da Silva Marques, António JoséThis study explores the sensory impact of a virtual journey thr ough an immersive, interactive envir onment inspir ed by the artistic work of Domingos Mateus. It addr esses how sensory experiences in virtual spaces, using dynamic visual and auditory stimuli, can enhance emotional engagement and spatial awareness. This study aims to investigate the ef fectiveness of combining dynamic painting and emotionally resonant music within an interactive, digital envir onment, aiming to evoke a deeper connection between viewers and artistic content. A multi-sensory museum was implemented, blending interactive visual components (dynamic painting) with a custom musical scor e designed to elicit emotional responses. Participants navigated the virtual envir onment using VR equipment, cr eating a fully immersive experience. Sensory responses wer e monitored using real-time biofeedback to gauge emotional engagement and pr esence. Findings suggest combining interactive visual art and music significantly enhances users' emotional engagement and immersive experience. Participants reported heightened spatial pr esence, with biofeedback data indicating incr eased emotional ar ousal during key moments. This sensory appr oach is potentially used in therapeutic and educational settings, wher e emotional and sensory stimulation ar e beneficial. The study underscores the power of immersive envir onments in enhancing the user’ s connection to artistic expr essions and fostering memorable experiences.
- Optimization of surgical scheduling: Predicting surgery time duration using machine learningPublication . Malheiro, Soraia; Faria, Brígida; Dias, Celeste; Faria, Brigida MonicaThe operating room (OR) is a highly specialized hospital department that requires a large amount of resources which has a high impact on hospital funding (1). The OR is an essential area for the hospital operation and its management must guarantee the best efficiency and the highest quality of patient care. Despite some initiatives already implemented to meet the demand for surgical treatment, as described in the European Commission’s 2021 report (2), waiting times for surgery in Portugal have increased in the last Currently, this prediction is essentially based on the surgeon’s experience in a particular surgical procedure and may not take other variables into account (1). The aim of this study is to predict more accurately the duration of the surgeries in the specialties of General Surgery, Orthopedics and Urology by developing a model based in machine learning techniques with data from clinical records of surgical cases. Methods: The sample of this study includes data from surgical cases performed in a hospital center. The following cases were excluded: Surgeries with patients under the age of 18; without a defined preoperative diagnosis; unspecified surgical specialties; no record of the start and/or end time of surgery and surgeries that took place on an outpatient basis. Multiple Linear Regression (MLR) and Random Forest (RF) techniques were applied to develop the model. Accuracy in predicting the duration of surgeries can optimize the OR occupancy and at the same time decrease the waiting time experienced by the patients. decade. Surgical scheduling is fundamental in the OR management (3). One of the challenges related to surgical scheduling is the prediction of surgery duration, which is essential for the allocating OR occupancy times.
- Modelling therapeutic response in asthmatic adults: a previous exploratory analysisPublication . Alves, Cristina; Faria, Brígida Mónica; Alves, Sandra Maria; Ferreira, Jorge; Faria, Brigida Monica; Alves, Sandra MariaAsthma is a respiratory disease characterized by chronic inflammation of the airways. Effective asthma management is essentially based on choosing the appropriate treatment for each individual (1). Data science and machine learning models offer valuable insights and enhance the outcomes achieved in asthma management (2). The main objective is to develop predictive models for therapy response in patients with asthma, and secondarily to identify clinical, functional and biological characteristics that influence this response. Data from fifty adults with asthma were analyzed, collecting information on anthropometric, clinical, functional, biological, therapeutic, occupational, and allergen exposure factors. The study followed the “Knowledge Discovery in Databases, KDD” methodology. The sample consisted of 50 asthmatic adult participants, aged between 21 and 81 years old mean age=54.02 (s=14.5), from which 20 (40%) were male and 30 (60%) were female. The analysis of the characteristic symptoms of asthma (dyspnea, cough, wheezing and chest tightness), reveals a statistically significant improvement (p<0.001) of all these symptoms after the treatment. The asthma control test, the life quality questionnaire and the asthma and allergic rhinitis control test evaluated before and after treatments, demonstrate a statistically significant difference (p=0.023, p <0.001 and p<0.001, respectively). On respiratory function, only FVC reveals a significant difference (p=0.409), after treatment. However, the average did not reach the minimal important difference (MID) of 200ml. The average number of exacerbations and SU recurrences difference was also significant in both cases (p<0.01), reaching MID (>50%). The majority of the individuals in this group had a positive, clinically important response to treatment. This result may be because they have severe atopic asthma, and Th2-High endotype, and for that reason they are undergoing more differentiated treatments, such as biological treatments.
- Contributions to the cross-cultural validation of “A survey of pharmacist knowledge, attitudes, utilization and barriers toward artificial intelligence”: translation and back translationPublication . Gerardo, Sofia; Pimenta, Rui; Alves, Sandra Maria; Alves, Sandra MariaThe use of Artificial Intelligence (AI) is rapidly transforming various fields, and pharmacy is no exception. AI is increasingly being used to automate, optimize, and personalize various tasks in pharmacy practice, from drug discovery to dispensing to patients. In Community Pharmacy, in addition to these possibilities, it allows for personalized and focused patient care through the selection of more appropriate and personalized therapies, with a lower probability of prescription errors and drug interactions, as well as monitoring of therapy adherence. Despite these potential benefits, its implementation in the pharmaceutical field, as well as in other areas of healthcare, should be carefully considered, as ethical and regulatory issues may pose obstacles. Likewise, the perspective and experience of each professional, which remain highly personal, especially in patient care, should not be overlooked. Therefore, it is increasingly important to know the knowledge, attitudes, utilization, and barriers concerning AI. Firstly, knowledge, as this concept encompasses the level of awareness and understanding that individuals or organizations have regarding AI technologies. Attitudes, which refers to the perceptions, feelings, and predispositions towards AI. It includes both positive and negative sentiments, such as excitement about AI's potential benefits, concerns about ethical implications, and fears about job displacement. Finally, the barriers or obstacles that hinder the adoption and effective utilization of AI. Barriers can be technical, such as lack of expertise or inadequate infrastructure; financial, such as high costs of implementation; or cultural, such as resistance to change or lack of trust in AI systems. This study aimed to translate and validate a survey instrument designed to explore the attitudes of Community Pharmacy professionals towards the implementation of Artificial Intelligence (AI) in their field. According to the COSMIN methodology, the initial translation of the construct from its original language, English, into European Portuguese was executed by two independent translators possessing comprehensive understanding of the questionnaire concepts. Both translators are bilingual healthcare professionals, who perform functions in a hospital environment and with European Portuguese being their native language. In the subsequent step, the two acquired versions were juxtaposed, leading to the formation of a consensus version endorsed by specialists, considering the new context where the construct will be applied, without excluding the original version. In cases where there are discrepancies between the two translations, the expert panel discusses the alternatives and decides on the most suitable option. The questionnaire translation process culminates with back-translation, wherein the consensus version obtained is rendered back into the original language, English, by a bilingual translator. The resultant back-translation should closely mirrors the original questionnaire, signifying the efficacy of the content translation process. Additionally, reliability testing methods like test-retest reliability and internal consistency checks help verify the stability and consistency of the survey results. To assess consensus among different questionnaire versions, techniques such as inter-rater reliability, the Delphi method, and agreement indices are essential. These processes ensure that the survey items are interpreted consistently across different respondents and that any subjective judgments are reliably measured. The results of the translated version questionnaire maintain the intended constructs and adequately capture attitudes toward AI implementation among Community Pharmacy professionals. Understanding the attitudes and perceptions of pharmacy professionals towards AI implementation is crucial for informing policy decisions, designing targeted interventions, and facilitating the successful integration of AI technologies into pharmacy practice. It is intended that this questionnaire contributes to the growing body of literature on AI in healthcare and serves as a foundation for further investigations into this evolving field. Future work includes the validation of the PT-EU questionnaire.