ESSa - Artigos em Revistas Indexados à WoS/Scopus
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Browsing ESSa - Artigos em Revistas Indexados à WoS/Scopus by Field of Science and Technology (FOS) "Ciências Médicas"
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- Machine learning classification of consumption habits of creatine supplements in gym goersPublication . Magalhães, Patrícia C.; Encarnação, Samuel; Forte, Pedro; Teixeira, José Eduardo; Monteiro, A. M.; Barbosa, Tiago M.; Pereira, Ana Maria Geraldes Rodrigues; Schneider, AndréThe aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future educational interventions and recommendations for safe and effective use. The study was applied to gym goers in Bragança, where a QR code for a survey was released. 158 people participated, 65 non-consumers of creatine supplementation (37.34% men; 22.78% women) and 95 consumers (15.19% men; 24.68% women). Five machine learning algorithms were implemented to classify creatine consumption in gym goers: Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, Xgboost Classifier. K-folds cross-validation was implemented to validate the machine learning performance. There was an increased proportion of females with considered themselves not sufficiently informed about the creatine effects/side effects (22.2%) in comparison to males (8.47%), p=0.03. The AdaBoost classifier exposed the best overall performance (86%) in classifying overuse of creatine in gym goers based on their Smoke habits (r = 0.33), grams of creatine used per day (r = 0.50) and lack information about the side effects of creatine intake (r = -0.33). The K-folds method validates the results with very good performance (86%). In conclusion, the five machine learning methods employed well characterized the overuse of creatine in gym goers based on smoke habits, grams of creatine per day, and lack information about the side effects of creatine intake.
- Validation of the collett-lester fear of death scale with portuguese studentsPublication . Alves Neto, Alexandra Marisa Maia; Félix, Neto; Patrício, Costa; Gomes, Maria JoséThe Collett-Lester Fear of Death Scale (CL-FODS) is a 28-item multidimensional measure assessing fear of death and dying of self and others. This study evaluated the psychometric properties and dimensionality of the Portuguese version in two phases. Phase 1 (P1; December 2018–February 2019) involved 312 students and used Exploratory Structural Equation Modeling (ESEM) to assess validity. Phase 2 (P2; January–March 2024) tested construct reproducibility with 470 students. Participants completed the Portuguese CL-FODS alongside the Social Desirability Scale, Social Anxiety Scale, Loneliness Assessment, and the Depression, Anxiety, and Stress Scales (DASS-21). An abbreviated version (AB-CLFODS) was developed by removing 12 items across subscales: fear of death of self (Items 1, 2, and 4; e.g., "Short life"), dying of self (Items 1, 5, and 7; e.g., "Physical degradation that occurs"), fear of death of others (Items 1, 2, and 3; e.g., "Loss of a loved one"), and dying of others (Items 1, 2, and 7; e.g., "Having to be with someone who is dying"). The AB-CLFODS demonstrated strong reliability, with Cronbach’s Alpha and McDonald’s ω values of .89 in P1 and .90 in P2. Subscale reliabilities ranged from .75 to .87 in P1 and .79 to .85 in P2. In P1, the scale correlated significantly with social desirability (p = .003), stress (p = .031), and social anxiety (p = .017). P2 confirmed significant correlations with all external measures, including loneliness and DASS-21 scores. Construct validity was further supported in P2 by acceptable fit indices, such as normed chi-square, CFI, and RMSEA values. These findings establish the Portuguese AB-CLFODS as a reliable and valid instrument for assessing fear of death and dying, with consistent psychometric properties and construct validity across time, making it suitable for research and clinical applications.