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- A gamified virtual reality exposure therapy for individuals with Arachnophobia: a pilot studyPublication . Lopes, Inês; Almeida, Raquel Simões de; Gomes, Paulo Veloso; Marques, António; Simões de Almeida, Raquel; Machado Veloso Gomes, Paulo Sérgio; Pereira da Silva Marques, António JoséArachnophobia is a specific phobia characterized by an intense and persistent fear of spiders, often leading to avoidance behaviors that can significantly impact daily life. Virtual Reality Exposure Therapy (VRET) offers a controlled, adaptable, and immersive therapeutic environment, allowing for greater personalization, flexibility, and the real-time modulation of exposure parameters. This pilot study employed a quasi-experimental design without a control group to evaluate the efficacy of a gamified VRET intervention for treating arachnophobia. A sample of 25 participants underwent the intervention, with outcomes assessed through a Behavioral Approach Test (BAT) and self-report measures, including the Fear of Spiders Questionnaire (FSQ) and the Spider Phobia Questionnaire (SPQ-15), administered pre-intervention, post-intervention, and at a two-week follow-up. Findings indicate that gamified VRET led to significant reductions in self-reported fear and avoidance behaviors, suggesting its potential as an effective therapeutic tool for arachnophobia. Although some results were not entirely conclusive, the overall improvements observed support further investigation in larger, controlled trials.
- Life cycle assessment using machine learningPublication . Gomes, Sofia; Faria, Brígida Mónica; Oliveira, Alexandra Alves; Pinto, Edgar; Rodrigues, Matilde; Vieira, Manuela; Faria, Brigida Monica; Oliveira, Alexandra; Pinto, Edgar; Rodrigues, Matilde; Vieira da Silva, ManuelaLife Cycle Assessment (LCA) is a scientific tool that allows calculating the impact of a product or service on the environment, considering the different phases from planting to transportation, commercialization, consumption, and disposal. (1) LCA requires comprehensive data collection of the inputs and outputs such as raw materials, energy, water, used chemicals and pollutants emissions at each stage of the life cycle. Data is usually obtained from different sources like producers or farmers (primary data), literature reviews, government reports and scientific publications (secondary data) or from associations, non-governmental organizations (NGOs) and international organizations. (2) Data processing and analysis are conducted with the aim of uncovering the resultant environmental impacts. This dissertation, integrated into the project REtail using Technology based on Artificial InteLLigence (RETAILL) (3) aims to apply Machine Learning (ML) techniques to develop surrogate LCA models that can be used to estimate LCA results for new products or services. Both public data and data from the Terras de Felgueiras Cooperative (4) will be used to develop the intended model. By preprocessing and modelling this data, the study aims to provide valuable insights for enhancing sustainability in the production of fresh fruits and vegetables. These insights can guide decision-making and drive continuous improvement in the supply chain. The objective of this study is to develop a ML model that estimates LCA results for new products or services and that translates environmental indicators into measurable impacts on both the environment and human health, specifically focusing on the production process. Another objective is to establish clusters that represent similar environmental performance of producers or products. The first step was to review the existing literature on the subject. To accurately determine emissions from agricultural activities, validated equations from the Agri-footprint 6 methodology (5) were employed. Preliminary analyses and descriptive statistics of variables such as fertilizers, pesticides and fungicides applied on agriculture from public data assessments were conducted using tools like SPSS and RapidMiner. This latter was used to carry out the construction of decision trees and clusters. To evaluate clustering models, certain indices were considered namely the Davies-Bouldin Index and the Calinski-Harabasz Index. Meanwhile, for assessing decision trees, measures such as accuracy rate, F- measure, and confusion matrix are well-known evaluation criteria. Subsequently, the ML model was developed using Python programming language and libraries such as Scikit- learn, Pandas, and SciPy. The analysis of public data reveals results from the cultivation of kiwi, watermelon, citrus, tea, and hazelnut across a total of 865 orchards (6). The results include the development of a tailored ML model for LCA phase that allows. the identification and translation of key environmental indicators into environmental and human health impacts. Furthermore, the clustering results of products and producers enables the observation of patterns in the environmental impact of the production process. Overall, this study contributes to the field of sustainability by providing a framework for integrating ML techniques with life cycle assessment, ultimately leading to more efficient and effective practices in agricultural production. The utilization of validated equations from Agri-footprint 6 enhances the reliability of emissions determination from agriculture, contributing to more accurate assessments of environmental impacts. Ultimately, the goal of an LCA is to support informed decision- making and promote more sustainable practices across industries.
- Comparing time series forecasting models for health indicators: A clustering analysis approachPublication . Vinhal, Cláudia; Oliveira, Alexandra; Faria, Brígida; Nascimento, Ana Paula; Pimenta, Rui; Oliveira, Alexandra; Faria, Brigida Monica; Pimenta, RuiTime series are the sequence of observations ordered by equal time intervals, crucial for understanding causality, trends, and forecasts. Its analysis can be applied to several areas, such as engineering, finance, and health (1,2). One problem with the time series study is clustering, mainly understanding when two parametric time series are considered similar (3). The sum of mortality and morbidity, referred to as “Burden of Disease”, is measured by a metric called “Disability Adjusted Life Years” (DALYs) (4). These indicators are direct measures of health care needs, reflecting the global burden of disease in the population, and are crucial for public health study and surveillance (5). DALYs can be represented by Autoregressive Integrated Moving Averages (ARIMA) models, and in this context understanding clusters is crucial. The primary goal is to compare different distance measures between ARIMA processes when used in clustering techniques. The study begins by exploring the temporal characteristics of DALYs, highlighting underlying patterns and trends. Then, ARIMA models are applied to represent and describe the time series. It’s on this representation of the time series that the Piccolo, the Maharaj, and the LPC distance measures are applied to use clustering techniques and identify clusters. Additionally, 8 distinct cluster validation metrics are used. Specific to 48 European countries, the results show that the choice of distance measure can greatly influence clustering outcomes and the number of clusters formed. While certain methods revealed geographic patterns, other factors, such as cultural or economic similarities, also influence cluster formation. These insights contribute to advancing the field of public health surveillance and intervention, ultimately aiming to alleviate the global burden of disease. This study offers insights into applying ARIMA processes in clustering techniques for analysing temporal health data. By comparing different distance measures, this research improves our understanding of underlying patterns and trends in health indicators over time.
- Swallowing assessment in a clinical context: artificial intelligence applicationsPublication . Torres, Teresa; Faria, Brígida; Araújo, Pedro; Oliveira, Alexandra; Alves, Elisa; Faria, Brigida Monica; Oliveira, AlexandraEating and swallowing are intricate actions comprehending both voluntary and reflexive movements, engaging over thirty nerves and muscles (1). Dysphagia occurs when the normal swallowing process is compromised, increasing the risk of the swallowed material entering the larynx, and causing complications for the patient (2). Cervical auscultation (CA) is a clinical method used to evaluate the pharyngeal phase of swallowing by listening to the sounds of swallowing-related respiration. However, the reliability of CA is susceptible to the subjectivity and experience of the speech therapist (3). The application of artificial intelligence (AI) tools in healthcare has the potential to support healthcare workers with a variety of tasks. It can be used for disease prediction and diagnostics treatment, outcome prediction and prognosis evaluation (4). Contribute to the development of an AI tool to aid speech therapists in their daily evaluation of deglutition in patients, applying machine learning algorithms to process and analyse the sound recorded during cervical auscultation. Using an electronic stethoscope, audio samples were recorded from individuals with and without pathology while they swallowed different liquid quantities (5 mL and 10 mL) and consistency (moderately thick and solid). The audio is divided into three sections (5) and pre- processed to remove unnecessary noise. Data is classified using machine learning algorithms and the models will be evaluated according to precision, accuracy, sensibility, confusion matrix and ROC curve. Previous work in this field reported by Santoso et al. (6) using supervised machine learning algorithms obtained promising results in swallowing detection. In this study, our dataset is composed of 87 samples of patients, and similar results are to be expected. AI can offer an objective and quantitative analysis of swallowing sounds, potentially providing more accurate and consistent results compared to subjective assessments. Additionally, it can identify complex patterns in swallowing sounds that may be challenging for less experienced speech therapists to recognize.
- Exploring coping profiles in informal caregivers of people with dementia through an online eHealth platformPublication . Gouveia, Tânia; Viana, João; Faria, Brígida; Teles, Soraia; Faria, Brigida MonicaInformal caregivers (IC) of people with dementia (PwD) handle multiple stressors with an acknowledged impact on their physical and mental health. health (1,2). Coping strategies may be viewed as the techniques employed by ICs to minimize distress associated with the caregiving experience. iSupport emerges as an online self-guided program developed by the World Health Organization (3) to provide support and training for ICs of PwD to minimize the negative impact of caring for a PwD. This program has been translated and culturally adapted for the Portuguese population and tested for its usability and feasibility (4,5). iSupport-Portugal1 is, being explored for its potential as an intervention-research tool, offering support to ICs of PwD but also serving as a remote measurement tool to collect data on dementia care dyads. Research goal: To describe coping styles and facets among IC of PwD and to identify profiles of IC in relation to coping styles based on underlying data pattens from demographic, clinical and psychosocial variables pertaining to both the IC and PwD. This study follows an observational cross-sectional design. Participants are IC registered on iSupport- Portugal between February 2023 and February 2024, who meet eligibility criteria. Data on the IC and PwD are collected upon registration via user’s accounts. Data analysis: Unsupervised learning methods, such as cluster analysis, enable the identification of latent patterns or underlying structures in dimensional data. Their ability to discover similarities and differences in information makes them an ideal solution for exploring IC profiles in relation to coping strategies. Expected results and Implications: Coping is a modifiable dimension with known implications to well-being outcomes for both the IC and the PwD. Identifying IC profiles in relation to coping strategies is fundamental to design more targeted interventions addressing the specific needs of the IC-PwD dyads.
- 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.
- Randomization in clinical trials: a biostatistician taskPublication . Coelho, Heitor; Albuquerque, João; Alves, Sandra Maria; Alves, Sandra MariaRandomized clinical trials stand as the golden standard for evidence-based clinical investigation on humans, providing robust methodologies to evaluate new interventions (1). By employing stochastic processes to allocate participants to treatment and control groups, randomization minimizes bias and enhances the validity of results (1,2). This ensures unpredictability in treatment assignment, mitigating selection, response, and confounding biases, thus safeguarding the statistical integrity of the study (1,3,4). Various randomization techniques address specific trial needs (5). Simple randomization is the most straightforward, offering equal allocation probabilities but risking imbalance in small samples (6). Block randomization ensures numerical balance across groups but may fail to address covariate comparability (7). Stratified randomization improves group balance concerning key covariates but demands careful variable selection to prevent empty strata (5,6). Adaptive randomization adjusts allocation probabilities during the trial to manage imbalances but introduces complexity and potential predictability (1,8). Selecting the appropriate randomization method requires careful consideration of study design, sample size, and confounding factors to ensure reliable results (5). Beyond method selection, effective implementation is crucial, necessitating adherence to best practices outlined in the CONSORT 2010 guidelines (1). Proper documentation of allocation sequence generation, masking procedures, and protocol monitoring ensures reproducibility and integrity (1). This session will introduce randomization in clinical trials, present different methodologies, and explore their strengths and weaknesses.
- 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.
- In-silico prediction of the complete ataxin-3 protein network relevant for Spinocerebellar Ataxia type 3 (SCA3)Publication . Batista, Paulo; Vieira, Jorge; Vieira, Cristina P.; Faria, Brígida Mónica; Faria, Brigida MonicaSpinocerebellar ataxia type 3, also known as Machado Joseph disease (SCA3/MJD), is the most common inherited ataxia worldwide and is caused by a pathogenic expansion of the polyglutamine (polyQ) tract, located at the C-terminal region of the ataxin-3 protein (1). The polyQ region is involved in the stabilization of protein- protein interactions (PPIs). Abnormal polyQ expansion results in structural changes of the ataxin-3 (2,3), implying different accessibility at specific interacting residues, needed for the normal protein activity. PolyQ proteins have large protein networks. Mapping of PPIs has been performed using high-throughput methods, that are known to produce false interactions (4). Therefore, the use of multiple interactomes comparisons (conserved interactions between pairs of proteins which have interacting homologs in another organism, as well as proteomic data from cell lines, patients, mutants expressing a human protein, and cross-species genetic screens (modifier screens), available at EvoPPI3 (5)), together with in-silico analyses, can be used to support PPIs, as well as identify novel interactors. In this work we will: 1- characterize ataxin-3 network (validating the proteins identified in main databases, as well as identify new putative interactors); 2- identifying the interactors that behave differently in the presence of an expanded polyQ using different 3D structure prediction methods and protein docking methods. Using EvoPPI3 and protein expression in tissues that matter to SCA3 for PPI retrieval and validation, as well as identification of new interactors. In-silico approaches for predicting protein binding differences between wildtype and expanded ataxin-3 forms will be performed, using different a) 3D protein structure predictions (namely ITASSER (6), AlphaFold (7), and D-ITASSER (8)) and b) protein docking methodologies (such as HADDOCK (9) and ClustPro (10). Using EvoPPI3, there are 422 ataxin-3 interactors in human main databases. From this, 250 proteins have been previously studied. Of the remaining 172 proteins, 158 have been reported from proteomic analyses of human cell lines and ataxin-3 patients (H. sapiens polyQ_22 database), and these could be true interactors. 28 proteins are in common when considering the polyQ, Mus musculus interlogs and Danio rerio interlogs, and these could be novel interactors to study. From the 158, 73 proteins bind more to the expanded form of ataxin-3 using AlphaFold, to confirm these results we used ITASSER, where we obtained 46 of the 73 that bind more to the expanded form. This study contributes significantly to understanding SCA3 pathology by delineating a network of ataxin-3 interactors and analysing their behaviour in the presence of an expanded polyQ stretch.
- Allergic rhinitis and work productivity: Preliminary analysis of data from the MASK-air applicationPublication . Ferreira, Laura; Pinto, Bernardo Sousa; Alves, Sandra Maria; Amaral, Rita; Alves, Sandra Maria; Amaral, RitaAllergic rhinitis is a health condition more prevalent in developed countries that can impact the activities and quality of life of affected individuals1. Although its impact on work productivity is recognized2, there is still a need for a more detailed understanding and quantification. This cross-sectional observational study investigates the relationship between allergic rhinitis and work productivity, using data from the MASK-air mobile designed for monitoring allergic rhinitis and related respiratory conditions3. To investigate the association between the severity of allergic rhinitis symptoms and the impact on work productivity. Data was collected through the MASK-air mobile application4,5 that contains demographic, environmental and symptom variables on a daily basis, with users providing information on a scale of 0 to 100 each day. A sample of 1000 random observations of users from 30 countries, recorded between May 2015 and December 2023 was analysed. Participants were selected based on specifics criteria, including a minimum age of 15 or 16 (depending on the digital consent age in each country) and self-reported diagnosis of allergic rhinitis. Descriptive statistics and the Spearman correlation coefficient6 between symptoms and impact on productivity were calculated. The sample showed a balanced distribution between sexes, with 435 individuals identified as female (53.5%) and 378 individuals as male (46.5%). The mean age of participants was 41.41 ± 14.50 years. The data included participants from various countries; the most frequent was from Mexico with 141 participants (17.3%), followed by Lithuania with 91 participants (11.9%), and Germany with 79 participants (9.7%). Regarding comorbidities, 535 participants (65.6%) reported having conjunctivitis, and 310 participants (38.1%) reported being asthmatic. Additionally, 200 participants (20%) used immunotherapy. A strong positive correlation was observed between work impact and the severities of global allergic symptoms (ρs= 0.82, p < 0.0001) and nasal symptoms (ρs= 0.77, p < 0.0001); and a moderate correlation was observed between work impact and the severities of ocular symptoms (ρs= 0.69, p < 0.0001) and asthma (ρs= 0.48, p< 0.0001). This study offers an initial understanding of how symptoms of allergic rhinitis affect work productivity. Identifying other associated factors will allow targeting health interventions and policies to improve the well-being and performance of workers affected by this condition.