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- The power of self-love: an artificial neural network based on neuroscience inference to predict university students self-reported mental health dimensionsPublication . Fortunato, Álvaro; Encarnação, Samuel; Vaz, Paula Marisa Fortunato; Vaz, Filipe J.A.; Monteiro, A.M.In line with the sustainable development goals of the United Nations 2030 agenda, namely goal number 3 – Good health and well-being -, student mental health is a global goal, first because it is health we are talking about and secondly because it has implications in the quality of learning and, consequently, in the adequate preparation of professionals for society. This study aimed to conduct an artificial neural network (ANN) to predict the student’s self-reported mental health dimensions. This is a cross-sectional and observational study enrolling data collected by applying a questionnaire comprising sociodemographic and health state variables from 2050 university students aged (18-30 years). The algorithm predicted the student’s overall mental health state self-perception with 94% accuracy (weighted average= [precision= 0.67%, recall= 0.67%, F-1 score0 0.67%]) and was cross-validated with reasonable accuracy (60%). The student’s depressive state was predicted with 97% accuracy (weighted average= [precision= 0.79%, recall=0.79%, F-1 score0 0.79%], and was cross-validated with good accuracy (73%). The student’s lack of interest in performing their activities of daily living (ADLs) was predicted with 94% accuracy (weighted average= [precision= 0.69%, recall=0.77%, F-1 score0 0.76%], and was cross-validated with reasonable accuracy (67%). The ANN presented excellent learning performance (>90%) for all targeted variables, within reasonable to good generalization capacity (60-73%). Finally, the university student’s depressive state was the best-predicted variable (73%).
- Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component ApproachPublication . Teixeira, José Eduardo; Branquinho, Luís; Ferraz, Ricardo; Morgans, Ryland; Encarnação, Samuel; Ribeiro, Joana; Afonso, Pedro; Ruzmetov, Nemat; Barbosa, Tiago M.; Monteiro, A.M.; Forte, PedroUtilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 1.75 years) were continuous monitored across six weeks during the 2019–2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed.
- A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health PoliciesPublication . Forte, Pedro; Encarnação, Samuel; Monteiro, A.M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Branquinho, Luís; Amaro, Bruna; Sampaio, Tatiana; Flores, Pedro Miguel; Silva-Santos, Sandra; Ribeiro, Joana; Batista, Amanda; Ferraz, Ricardo; Rodrigues, FilipeThe increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.
- Aerobic fitness as an important moderator risk factor for loneliness in physically trained older people: an explanatory case study using machine learningPublication . Encarnação, Samuel; Vaz, Paula Marisa Fortunato; Fortunato, Álvaro; Forte, Pedro; Vaz, Cátia; Monteiro, A.M.Loneliness in older people seems to have emerged as an increasingly prevalent social problem. Objective: To apply a machine learning (ML) algorithm to the task of understanding the influence of sociodemographic variables, physical fitness, physical activity levels (PAL), and sedentary behavior (SB) on the loneliness feelings of physically trained older people. Materials and Methods: The UCLA loneliness scale was used to evaluate loneliness, the Functional Fitness Test Battery was used to evaluate the correlation of sociodemographic variables, physical fitness, PAL, and SB in the loneliness feelings scores of 23 trained older people (19 women and 3 men). For this purpose, a naive Bayes ML algorithm was applied. Results: After analysis, we inferred that aerobic fitness (AF), hand grip strength (HG), and upper limb strength (ULS) comprised the most relevant variables panel to cause high participant loneliness with 100% accuracy and F-1 score. Conclusions: The naive Bayes algorithm with leave-one-out cross-validation (LOOCV) predicted loneliness in trained older with a high precision. In addition, AF was the most potent variable in reducing loneliness risk.
- Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning ApproachPublication . Teixeira, José Eduardo; Encarnação, Samuel; Branquinho, Luís; Morgans, Ryland; Afonso, Pedro; Rocha, João Pedro da Silva; Graça, Francisco M.; Barbosa, Tiago M.; Monteiro, A.M.; Ferraz, Ricardo; Forte, PedroThe aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players’ MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (xpredicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player’s MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC (xpredicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players’ ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R2 = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training.
- Euterpe Oleracea Martius (Açaí) Extract and Resistance Exercise Modulate Cardiac Parameters of Hypertensive RatsPublication . Meireles, Pilar Barbosa; Miranda, Denise Coutinho; Moura, Anselmo Gomes; Ribeiro, Willian Cruz; Oliveira, Ângela; Leite, Luciano Bernardes; Forte, Pedro; Ribeiro, Lucia M.; Encarnação, Samuel; Guimarães-Ervilha, Luiz Otávio; Machado-Neves, Mariana; Dias, Mariana Moura; Campos, Iasmim Xisto; Reis, Emily Correna Carlo; Peluzio, Maria do Carmo Gouveia; Natali, Antônio José; Lavorato, Victor NeivaThe study evaluated the effects of resistance exercise training and açaí supplementation on cardiac parameters in hypertensive animals. Methods: For this study, rats from the Wistar and SHR lines (spontaneously hypertensive rats) were used. The animals were divided into 5 groups: Wistar Control (C); Control Hypertensive (H); Trained Hypertensive (HT); Hypertensive and Supplemented with Açaí (HA); and Hypertensive Trained and Supplemented with Açaí (HAT). Resistance exercise training was carried out through climbing. The supplemented groups received 3 g of açaí/kg of body mass. The animals’ systolic blood pressure (SBP), body mass, and physical test were measured at the beginning and end of the intervention. At the end, an echocardiographic analysis was performed. Histological analysis and oxidative stress of the LV were performed. Results: It was found that hypertensive animals showed an increase in SBP, and the treatments reduced this parameter. The trained groups achieved higher values of maximum carrying load. Hypertension increased the dimension of the left ventricular free wall in diastole and reduced ejection and shortening fractions. The trained groups showed improvement in ejection and shortening fractions. The H group increased the proportion of extracellular matrix and reduced the proportion of cells, with the HAT group attenuating this change. Cell diameter was greater in group H, and all treatments reduced this parameter. Hypertension increased the concentration of malondialdehyde and decreased catalase activity in LV. The treatments managed to mitigate this damage. Conclusions: It is concluded that the treatments managed to generate positive cardiovascular adaptations, and their combination enhanced these effects.
- Comparison of Physical Activity Level, Body Composition, Strength, and Flexibility of Teen Basketball Players and Adolescents Non-Practitioners of Sport: An Observational Study with Machine Learning AnalysisPublication . Encarnação, Samuel; Rezende, Vitor Hugo Santos; Gonçalves, Iarni Martins; Prata, Patrícia de Oliveira Ramalho; Mansur, Henrique Novais; Sampaio, Tatiana; Forte, Pedro; Teixeira, José Eduardo; Monteiro, A.M.; Guttierres, Ana Paula MunizIncreasing youths’ physical activity is mandatory to reduce the risk of non-communicable diseases (NCCDs). Basketball is a team sport that is potentially positive in increasing teenagers’ physical performance, health indicators, and well-being. Objective: The objective was to compare the physical activity level (PAL), body composition, strength, and flexibility of teen male basketball players (BG) (n = 15) and adolescent non-practitioners of sport (NS: n = 14). Methodology: All participants were healthy and free from any health disability from a Brazilian high school. A linear regression machine learning algorithm was applied to predict the adolescent´s physical components. In a quasi-experimental analysis, data were extracted by PAL, body fat percentage (BF%), handgrip strength (HG), back extensor muscle’s’ strength (BMS), lower limb power (LLP), and lower limb flexibility (LLF). Parametric (independent T-test) and non-parametric (Mann-Whitney U test) were employed to compare the variable’s average and chi-square was applied to compare categorical data. Results: BG presented an upper number of adolescents classified with high PAL than the NS group (p = 0.0002, large ES, V = 0.73) and a lower number of adolescents classified with low PAL than the NS group (p = 0.0002, V = 0.73), less BF% (p = 0.02, r = 0.85), greater values of HGS (p = 0.005, r = 0.34), greater values of BMSLS (p = 0.005, r = 0.33), greater values of LLP (p = 0.007, r = 0.30), and greater values for LLF (p = 0.02, r = 0.17). Therefore, there was a positive effect of high PAL compared with low PAL in HG, (p = 0.005, r = 0.24) and also for high PAL in LLF, (High PAL: (p = 0.006, r = 0.23). Regarding machine learning analysis, the four models (linear regression, Ridge regression, random forest regression, and Bayesian regression) expressed good generalization performance, with a coefficient of determination (R2) ranging from 0.77 to 0.88, root mean square error (RMSE) from 1.01 to 3.92, with an average mean difference of four points between the predicted and real values. The worst model was random forest regression R2 = 0.77, RMSE = 3.92, and the best model was Bayesian regression (R2 = 0.88, RMSE = 1.01). Conclusion: The BG group presented better results than the NS group for PAL, BF%, HG, BMS, LLP, and LLF. Body fat percentage precisely predicted the player’s’ vertical jump (VJ). In addition to the physical superiority of the BG, this study revealed the importance of managing body composition for both health and performance improvements.
- Asthma prevalence in adolescent students from a Portuguese primary and secondary schoolPublication . Flores, Pedro Miguel; Teixeira, José Eduardo; Leal, Anna Kosmider; Branquinho, Luís; Fonseca, Rui Brito; Silva-Santos, Sandra; Batista, Amanda; Encarnação, Samuel; Monteiro, A.M.; Ribeiro, Joana; Forte, PedroAsthma is one of the most prevalent chronic diseases worldwide, with a considerable increase, especially in children. It is considered the main cause of childhood morbidity, school absenteeism, and limitations in sports practice. The causes are multifactorial, and their prevalence varies from region to region, thus verifying a great disparity in the estimates of the prevalence of asthma. In this sense, the objective of this study is to investigate the prevalence of asthma, its control, as well as the frequency of associated symptoms, in adolescents who attended the 3rd cycle of basic education and secondary education in schools in the municipalities of Paços de Ferreira, Paredes, and Penafiel. The sample consisted of 1222 (587 males and 635 females) (p = 0.17) aged between 12 and 17 years. The instruments used to diagnose asthma-associated symptoms were the standard questionnaire of the “International Study of Asthma and Allergies in Childhood—ISAAC” and to check whether asthma was controlled, the “Test for Asthma Control” questionnaire was used. The results reveal a high prevalence of adolescents with asthma (8.9%) with a significant percentage that did not have the disease under control (38%). There was also a considerable percentage of adolescents who, despite not having asthma, have many symptoms associated with the disease. These results may be associated with environmental factors.
- The effectiveness of pilates training interventions on older adults’ balance: a systematic review and meta-analysis of randomized controlled trialsPublication . Sampaio, Tatiana; Encarnação, Samuel; Santos, Olga Maria; Narciso, Diogo; Oliveira, João P.; Teixeira, José Eduardo; Forte, Pedro; Morais, J.E.; Vasques, Catarina; Monteiro, A.M.Pilates training intervention programs have gained attention as a potential approach to enhancing balance in older adults, thereby reducing the risk of falls. In light of these considerations, this systematic review and meta-analysis aimed to critically evaluate the existing evidence and determine the effect of Pilates training intervention programs on older adults’ balance. Materials and Methods: The literature was searched through the PubMed, Web of Science, and Scopus databases from inception until July 2023. The primary keywords used for the literature search included “elderly” or “older adults” and “pilates training” and “balance”. Results: The systematic review through qualitative analysis showed robust evidence about the efficacy of Pilates intervention programs in improving older adults’ balance. The pooled meta-analysis of static and dynamic balance showed that eight (53%) out of a total fifteen analyzed interventions presented a significant effect of Pilates in improving the participants’ balance, without between-study heterogeneity. In addition, the meta-analysis regarding dynamic balance showed that six (67%) out of nine analyzed interventions presented a significant effect of Pilates in improving the participants’ balance, without heterogeneity between studies. Similarly, the meta-analysis regarding static balance showed that four (50%) out of eight analyzed studies presented significant effects on the older adults’ balance, where moderate between-study heterogeneity was found. Sensitivity analysis showed that three studies reduced the between-study heterogeneity (19, 17.6, and 17%), regressing from moderate to low heterogeneity, p < 0.05. Conclusions: This systematic review and meta-analysis underscores the potential of Pilates training as a valuable intervention to enhance balance in the elderly population
- Influence of multicomponent exercise program or self-selected physical activity on physical, mental, and biochemical health indicators of older womenPublication . Encarnação, Samuel; Fazolo, Sthefany Lemos; Pereira, Felipe Soares Tomaz; Araújo, Daniele Pereira; Miranda, Cíntia Neves de; Pinto, Beatriz Woyames Ferreira; Forte, Pedro; Teixeira, José Eduardo; Barbosa, Tiago M.; Monteiro, A.M.; Moreira, Osvaldo Costa; Carneiro-Júnior, Miguel AraújoThe aim of this study was to compare physical, mental, and biochemical health indicators of 48 older women (67 ± 1 year) who practiced multicomponent exercise program (ME, n = 25) and self-selected physical activity (PA, n = 23) for 6 months. It was an observational study, which aimed to relate a prospective intervention. Displacement speed, lower limb (LL) power, functional capacity, body composition, biochemical profile, physical activity levels (PAL), sedentary behavior (SB), quality of life (QoL), and mental illness risk (MIR) were evaluated. ME presented better values compared to the PA in the gait speed (p = 0.001, large ES), aerobic capacity (p = 0.0001, large ES), agility/dynamic balance (p = 0.0001, large ES), LL flexibility (p = 0.0003, large ES), UL flexibility (p = 0.04, large ES), upper limb (UL) strength (p = 0.07, moderate ES), Total cholesterol (p = 0.009, large ES), triglycerides (p = 0.003, large ES), creatinine (p = 0.007, large ES), glycated hemoglobin (p= 0.007, large ES), and lower mean glucose value (p = 0.008, large ES). ME was more efficient than PA to improve indicators of gait speed, and functional capacity, regulate glycated hemoglobin, blood glucose, and serum creatinine. Thys study also brings practical applications for coaches, which could adapt and use creativity to develop different types of systematized ME, aiming to enhance positive adaptations in the older people at multilevel outcomes.