The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.

BACKGROUND: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. OBJECTIVE: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. METHODS: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. RESULTS: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. CONCLUSIONS: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. TRIAL REGISTRATION: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.

Leadership and contagion by COVID-19 among residence hall students: A social network analysis approach.

University students have changed their behaviour due to the COVID-19 pandemic. In this paper, we describe the characteristics of PCR+ and PCR- nodes, analyse the structure, and relate the structure of student leaders to pandemic contagion as determined by PCR+ in 93 residential university students. Leadership comes from the male students of social science degrees who have PCR +, with an eigenvector centrality structure, ß-centrality, and who are part of the bow-tie structure. There was a significant difference in ß-centrality between leaders and non-leaders and in ß-centrality between PCR+ and non-leaders. Leading nodes were part of the bow-tie structure. MR-QAP results show how residence and scientific branch were the most important factors in network formation. Therefore, university leaders should consider influential leaders, as they are vectors for disseminating both positive and negative outcomes.

Men who have sex with men: An approach to social network analysis.

BACKGROUND: Dating apps for men who have sex with men (MSM) have favored unprotected sexual encounters; other unsafe practices, including drug use, are widespread. No evidence is available from the perspective of the structure of their relationships, a personal aspect included in all nursing meta-paradigms. AIM: To study the structure of MSM networks through dating and contact applications and this relationship to risky sexual activities such as condom use, chemsex (sex while using drug), and group sex. DESIGN: Descriptive cross-sectional study. SAMPLE: A total of 32 MSM participants from Madrid (Spain). MEASUREMENTS: Socio-demographic and structural variables with Social Network Analysis (SNA) metrics. Data on condom use, drug use during encounters, and group sex were included. RESULTS: Twenty-five percent of respondents practiced chemsex, and 75% of these used poppers. MSM with higher socioeconomic status participated in group sex sessions more frequently than those with lower socioeconomics. Within the network analysis, the relationships strong showed greater ease in having unprotected anal intercourse. CONCLUSION: SNA can be effective in the study of MSM sexual networks and their risk behaviors for community nurses to improve their interventions in sexual health promotion.

[Return rate to the Primary Care Emergency Service for elderly patients over 65 years old and their assistance requirements]. / Tasa de retorno y características asistenciales de pacientes mayores de 65 años en un servicio de Urgencias en Atención Primaria.

OBJECTIVE: To determine the unexpected return rate to the Primary Care Emergency Service of elderly patients over 65 years old within the following 72h of a previous visit, as well as to determine the clinical and assistance requirements of these patients. PROCEDURE: Retrospective and observational epidemiologic study. LOCATION: Cotolino's Primary Care Emergency Service in Cantabria, Spain. PARTICIPANTS: 1940 elderly patients over 65 years old were included. These patients returned to the Primary Care Emergency Service in 2016. MAIN DATA FOR THE STUDY: The dependent variable was the return rate to the Primary Care Emergency Service. The independent variables were socio-demographic characteristics, health details and medical assistance information. All data was collected from the Primary Care Emergency Service Management Office database. All variables were analysed applying Pearson's chi-squared test and Fisher's exact test, with statistical significance P≤.05. RESULTS: The rate of unexpected return was 2.3%. The average age was 77.4 years old (standard deviation (SD): 8.4), of which the 37.6% were male. The most frequent range of age was from 75 to 84 years old, with males being the predominant group. A history of polymedication was detected in 54.4% of the cases, as well as a medium cardiovascular risk within this group. Nursing professionals attended the 42.2% of these return cases (P<.001). Patients with dysnea (P=.015), scheduled care or scheduled injection returned with a higher frequency (P<.001). It was as well noticed a higher frequency of return for subsequent attention during the months of December and January (P<.001). CONCLUSIONS: The rate of unexpected return is low. The main causes why elderly patients returned to the service requiring urgent assistance were issues categorised as unspecific general health indicators and/or respiratory system illnesses. Our proposal is to develop specific protocols combining the work from both Geriatrics and Gerontology professionals, in order to improve the support to this group of population at every Primary Care Emergency Service.

Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children.

This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and 91 students in the control group, in two schools during 14 weeks. The intervention group had access to the web through a user account and a password. They were able to create friendship relationships, post comments, give likes and interact with other users, as well as receive notifications and information about nutrition and physical activity on a daily basis and get (virtual) rewards for improving their habits. The control group did not have access to any of these features. The homogeneity of the samples in terms of gender, age, body mass index and initial health-related habits was demonstrated. Pre- and post-measurements were collected through self-reports on the application website. After applying multivariate analysis of variance, a significant alteration in the age-adjusted body mass index percentile was observed in the intervention group versus the control group, as well as in the PAQ-A score and the KIDMED score. It can be concluded that eHealth interventions can help to obtain healthy habits. More research is needed to examine the effectiveness in achieving adherence to these new habits.

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

Thermal Infrared Imaging to Evaluate Emotional Competences in Nursing Students: A First Approach through a Case Study.

During university studies of nursing, it is important to develop emotional skills for their impact on academic performance and the quality of patient care. Thermography is a technology that could be applied during nursing training to evaluate emotional skills. The objective is to evaluate the effect of thermography as the tool for monitoring and improving emotional skills in student nurses through a case study. The student was subjected to different emotions. The stimuli applied were video and music. The process consisted of measuring the facial temperatures during each emotion and stimulus in three phases: acclimatization, stimulus, and response. Thermographic data acquisition was performed with an FLIR E6 camera. The analysis was complemented with the environmental data (temperature and humidity). With the video stimulus, the start and final forehead temperature from testing phases, showed a different behavior between the positive (joy: 34.5 °C-34.5 °C) and negative (anger: 36.1 °C-35.1 °C) emotions during the acclimatization phase, different from the increase experienced in the stimulus (joy: 34.7 °C-35.0 °C and anger: 35.0 °C-35.0 °C) and response phases (joy: 35.0 °C-35.0 °C and anger: 34.8 °C-35.0 °C). With the music stimulus, the emotions showed different patterns in each phase (joy: 34.2 °C-33.9 °C-33.4 °C and anger: 33.8 °C-33.4 °C-33.8 °C). Whenever the subject is exposed to a stimulus, there is a thermal bodily response. All of the facial areas follow a common thermal pattern in response to the stimulus, with the exception of the nose. Thermography is a technique suitable for the stimulation practices in emotional skills, given that it is non-invasive, it is quantifiable, and easy to access.

Promotion of Healthy Habits in University Students: Literature Review.

The increase in responsibilities, together with the multiple challenges that students face in the university period, has a direct impact on their healthy lifestyles. This literature review describes the benefits of promoting healthy habits in college, highlighting the fundamental role of prevention and promotion. A systematic review was carried out following the PRISMA recommendations, searching for information in the WOS and Scopus databases. On the other hand, a search was carried out within the existing and available grey literature. The review focused on finding information about physical activity, nutrition, and stress (with an emphasis on resilience and academic burnout) in university students. This bibliographic review includes 32 articles and six web pages, containing information on the benefits of physical activity, healthy habits, and health prevention. The information collected in this study shows that university students are exposed to multiple changes during this period, increasing as the academic years progress. At that time, their habits worsen, with low adherence to the Mediterranean diet, low physical activity, and high levels of stress, specifically increasing cases of academic burnout. The establishment of healthy habits during the university period is necessary, observing an improvement in all the variables studied. Prevention has played a fundamental role.

Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing.

Purpose: The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. Methods: We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform's API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. Results: The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. Conclusion: Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study.

Objectives: Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types. Methods: Various machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques. Results: Experimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results. Conclusions: The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques.

AIM: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. METHODS: 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. RESULTS: After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. CONCLUSION: Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Survival and grade of the glioma prediction using transfer learning.

Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3-6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

Evaluation of the Satisfaction of Patients Seen in the Dermatology Department of a Spanish Tertiary Hospital.

Patient satisfaction is of great importance and is a key factor in the quality of care. The most commonly used measure of this factor is satisfaction surveys. This study used the modified SERVQHOS healthcare quality survey model, which adapts the SERVQUAL survey. The main objective was to determine the degree of satisfaction of patients seen in the outpatient department of the Dermatology Service, as well as to describe and detect those aspects that could be improved to offer better quality care. A total of 250 patients responded to the survey. The mean Likert scale score for the 19 items on the perceived quality of care was 4.17 ± 0.796 points. Up to 92.8% were satisfied or very satisfied with the care received. All items were statistically correlated with overall satisfaction (p < 0.001). In the multivariate study, the variables with predictive capacity in relation to overall satisfaction (p < 0.05) were "the technology of the medical equipment"; "the directions to the consultation"; "the confidence that the staff transmits"; "the state of the consultation"; and "the interest of the staff in solving problems". Satisfaction was significantly higher in men (p < 0.05), with a level of education up to primary school (p < 0.05) and no work activity (p < 0.001). The final mean score in the degree of perceived satisfaction was very high, indicating that the expectations of the patients were exceeded, and showing that satisfaction is closely linked to the qualities and skills of the staff in their relationship with the patient.

Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques.

Background: Postpartum urinary incontinence is a fairly widespread health problem in today's society among women who have given birth. Recent studies analysing the different variables that may be related to Postpartum urinary incontinence have brought to light some variables that may be related to Postpartum urinary incontinence in order to try to prevent it. However, no studies have been found that analyse some of the intrinsic and extrinsic variables of patients during pregnancy that could give rise to this pathology. Objective: The objective of this study is to assess the most influential variables in Postpartum urinary incontinence by means of machine learning techniques, starting from a group of intrinsic variables, another group of extrinsic variables and a mixed group that combines both types. Methods: Information was collected on 93 patients, pregnant women who gave birth. Experiments were conducted using different machine learning classification techniques combined with oversampling techniques to predict four variables: urinary incontinence, urinary incontinence frequency, urinary incontinence intensity and stress urinary incontinence. Results: The results showed that the most accurate predictive models were those trained with extrinsic variables, obtaining accuracy values of 70% for urinary incontinence, 77% for urinary incontinence frequency, 71% for urinary incontinence intensity and 93% for stress urinary incontinence. Conclusions: This research has shown that extrinsic variables are more important than intrinsic variables in predicting problems related to postpartum urinary incontinence. Therefore, although not conclusive, it opens a line of research that could confirm that the prevention of Postpartum urinary incontinence could be achieved by following healthy habits in pregnant women.

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study.

BACKGROUND: Eating disorders affect an increasing number of people. Social networks provide information that can help. OBJECTIVE: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. METHODS: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. RESULTS: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). CONCLUSIONS: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.

Detecting racism and xenophobia using deep learning models on Twitter data: CNN, LSTM and BERT.

With the growth that social networks have experienced in recent years, it is entirely impossible to moderate content manually. Thanks to the different existing techniques in natural language processing, it is possible to generate predictive models that automatically classify texts into different categories. However, a weakness has been detected concerning the language used to train such models. This work aimed to develop a predictive model based on BERT, capable of detecting racist and xenophobic messages in tweets written in Spanish. A comparison was made with different Deep Learning models. A total of five predictive models were developed, two based on BERT and three using other deep learning techniques, CNN, LSTM and a model combining CNN + LSTM techniques. After exhaustively analyzing the results obtained by the different models, it was found that the one that got the best metrics was BETO, a BERT-based model trained only with texts written in Spanish. The results of our study show that the BETO model achieves a precision of 85.22% compared to the 82.00% precision of the mBERT model. The rest of the models obtained between 79.34% and 80.48% precision. On this basis, it has been possible to justify the vital importance of developing native transfer learning models for solving Natural Language Processing (NLP) problems in Spanish. Our main contribution is the achievement of promising results in the field of racism and hate speech in Spanish by applying different deep learning techniques.

eHealth Intervention to Improve Health Habits in the Adolescent Population: Mixed Methods Study.

BACKGROUND: Technology has provided a new way of life for the adolescent population. Indeed, strategies aimed at improving health-related behaviors through digital platforms can offer promising results. However, since it has been shown that peers are capable of modifying behaviors related to food and physical exercise, it is important to study whether digital interventions based on peer influence are capable of improving the weight status of adolescents. OBJECTIVE: The purpose of this study was to assess the effectiveness of an eHealth app in an adolescent population in terms of improvements in their age- and sex-adjusted BMI percentiles. Other goals of the study were to examine the social relationships of adolescents pre- and postintervention, and to identify the group leaders and study their profiles, eating and physical activity habits, and use of the web app. METHODS: The BMI percentiles were calculated in accordance with the reference guidelines of the World Health Organization. Participants' diets and levels of physical activity were assessed using the Mediterranean Diet Quality Index (KIDMED) questionnaire and the Physical Activity Questionnaire for Adolescents (PAQ-A), respectively. The variables related to social networks were analyzed using the social network analysis (SNA) methodology. In this respect, peer relationships that were considered reciprocal friendships were used to compute the "degree" measure, which was used as an indicative parameter of centrality. RESULTS: The sample population comprised 210 individuals in the intervention group (IG) and 91 individuals in the control group (CG). A participation rate of 60.1% (301/501) was obtained. After checking for homogeneity between the IG and the CG, it was found that adolescents in the IG at BMI percentiles both below and above the 50th percentile (P50) modified their BMI to approach this reference value (with a significance of P<.001 among individuals with an initial BMI below the P50 and P=.04 for those with an initial BMI above the P50). The diet was also improved in the IG compared with the CG (P<.001). After verifying that the social network had increased postintervention, it was seen that the group leaders (according to the degree SNA measure) were also leaders in physical activity performed (P=.002) and use of the app. CONCLUSIONS: The eHealth app was able to modify behaviors related to P50 compliance and exert a positive influence in relation to diet and physical exercise. Digital interventions in the adolescent population, based on the improvement in behaviors related to healthy habits and optimizing the social network, can offer promising results that help in the fight against obesity.

The socialisation of the adolescent who carries out team sports: a transversal study of centrality with a social network analysis.

OBJECTIVES: To analyse the physical activity carried out by the adolescents in the study, its relationship to being overweight (overweight+obese) and to analyse the structure of the social network of friendship established in adolescents doing group sports, using different parameters indicative of centrality. SETTING: It was carried out in an educational environment, in 11 classrooms belonging to 5 Schools in Ponferrada (Spain). PARTICIPANTS: 235 adolescents were included in the study (49.4% female), who were classified as normal weight or overweight. PRIMARY AND SECONDARY OUTCOME MEASURES: Physical Activity Questionnaire for Adolescents (PAQ-A) was used to study the level of physical activity. A social network analysis was carried out to analyse structural variables of centrality in different degrees of contact. RESULTS: 30.2% of the participants in our study were overweight. Relative to female participants in this study, males obtained significantly higher scores in the PAQ-A (OR: 2.11; 95% CI: 1.04 to 4.25; p value: 0.036) and were more likely to participate in group sport (OR: 4.59; 95% CI: 2.28 to 9.22; p value: 0.000). We found no significant relationship between physical activity and the weight status in the total sample, but among female participants, those with overweight status had higher odds of reporting high levels of physical exercise (OR: 4.50; 95% CI: 1.21 to 16.74; p value: 0.025). In terms of centrality, differentiating by gender, women who participated in group sports were more likely to be classified as having low values of centrality, while the opposite effect occurred for men, more likely to be classified as having high values of centrality. CONCLUSIONS: Our findings, with limitations, underline the importance of two fundamental aspects to be taken into account in the design of future strategies: gender and the centrality within the social network depending on the intensity of contact they have with their peers.

A case study of university student networks and the COVID-19 pandemic using a social network analysis approach in halls of residence.

The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, León, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic. This could be described on the basis of aspects of similarities between students, and even leaders connecting the cohabitation sub-networks. In this context, Social Network Analysis could be considered as a methodological approach for future network studies in health emergency contexts.

Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach.

THE AIMS: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan-Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan-Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.