Depression, Anxiety, Emotional Eating, and Body Mass Index among Self-Reported Vegetarians and Non-Vegetarians: A Cross-Sectional Study in Peruvian Adults.

Background: Vegetarianism is commonly associated with various health benefits. However, the association between this dietary regimen and aspects of mental health remains ambiguous. This study compared the symptoms of depression and anxiety, emotional eating (EmE), and body mass index (BMI) in Peruvian vegetarian and non-vegetarian adults. Methods: A cross-sectional study was conducted on 768 Peruvian adults, of whom 284 (37%) were vegetarians and 484 (63%) were non-vegetarians. The Depression Patient Health Questionnaire-2 (PHQ-2), Generalized Anxiety Disorder Scale-2 (GAD-2), and an EmE questionnaire were applied; additionally, the BMI was calculated. Simple and multiple linear regression and Poisson regression models with robust variance were used to evaluate the association between depression, anxiety, EmE, and BMI with dietary patterns. Results: The vegetarians (Adjusted Prevalence Ratio [PR] = 0.24, 95% CI 0.16-0.31; p < 0.001) reported more depressive symptoms than the non-vegetarians. This trend persisted for anxiety, with an adjusted PR of 0.17 (95% CI: 0.01-0.29; p = 0.012). However, the vegetarians (adjusted PR = -0.38, 95% CI: -0.61--0.14; p < 0.001) reported lower EmE scores compared to the non-vegetarians. Likewise, the vegetarians had a lower mean BMI than the non-vegetarians (B = -0.16, 95% CI: -0.21--0.08; p < 0.001). Conclusions: Vegetarian diets are associated with increased symptoms of depression and anxiety, as well as lower EmE and BMI scores. Further longitudinal studies are needed to elucidate these associations and determine causality and the underlying mechanisms involved.

Breakfast consumption, saturated fat intake, and body mass index among medical and non-medical students: a cross-sectional analysis.

Changes in dietary patterns and body weight have become a focus of research in undergraduate students. This study compared breakfast consumption, intake of foods high in saturated fat, and BMI between medical and non-medical students. A comparative cross-sectional study was conducted in 4,561 Peruvian university students, of whom 1,464 (32.1%) were from the medical field and 3,097 (67.9%) from the non-medical field. We compared the frequency of breakfast consumption (categorized as regular: 6 to 7 days/week; occasional: 3 to 5 days/week; and rarely or never: 0 to 2 days/week) and the frequency of consumption of foods high in saturated fat. We created simple and multiple linear and Poisson regression models with robust variance to evaluate the association of the mentioned variables with academic fields. Non-medical students (Adjusted Prevalence Ratio [PR] = 0.92, 95% CI 0.86-0.99; p = 0.008) were less likely to eat breakfast regularly compared to medical students. Likewise, consumption of foods high in saturated fats was higher in non-medical students (B = 1.47, 95% CI 0.91-2.04; p < 0.001) compared to medical students. Similarly, the mean BMI of these students was significantly higher than that of medical students (B = 0.33, 95% CI 0.12-0.53; p = 0.002). Although medical students reported relatively healthy eating habits and a lower BMI, there is a widespread need to promote improved diet and lifestyle among the entire university population to reduce the risks of communicable diseases and improve quality of life.

Associations between social network addiction, anxiety symptoms, and risk of metabolic syndrome in Peruvian adolescents-a cross-sectional study.

Background: The link between physical and mental health and screen time in adolescents has been the subject of scientific scrutiny in recent years. However, there are few studies that have evaluated the association between social network addiction (SNA) and metabolic risk in this population. Objective: This study determined the association between SNA and anxiety symptoms with the risk of metabolic syndrome (MetS) in adolescents. Methods: A cross-sectional study was conducted in Peruvian adolescents aged 12 to 18 years, who completed a Social Network Addiction Questionnaire and the Generalized Anxiety Disorder 2-item scale (GAD-2), between September and November 2022. A total of 903 participants were included in the study using a non-probability convenience sample. Sociodemographic and anthropometric data were also collected. Binary logistic regression was used to explore the association between SNA and anxiety symptoms with MetS in a cross-sectional analysis. Results: Males were more likely to have MetS than females (OR = 1.133, p = 0.028). Participants who were 16 years of age or older and those with excess body weight were 2.166, p = 0.013 and 19.414, p < 0.001 times more likely to have MetS, respectively. Additionally, SNA (OR = 1.517, p = 0.016) and the presence of anxiety symptoms (OR = 2.596, p < 0.001) were associated with MetS. Conclusion: Our findings suggest associations between SNA, anxiety symptoms, and MetS among youth. However, more studies are needed to better understand this association and to deepen the possible clinical and public health implications.

Intake of foods high in saturated fats, vegetarian dietary pattern, and sociodemographic characteristics associated with body weight in Peruvian university students.

Background: The prevalence of obesity continues to increase among university students and the general population. Consumption of a diet high in saturated fats could be one of the risk factors. Objective: The consumption of foods high in saturated fats, the vegetarian diet pattern, and sociodemographic characteristics associated with excess body weight (overweight/obesity) were evaluated in Peruvian university students. Methods: A cross-sectional study was carried out selecting 5,608 Peruvian university students through no probabilistic convenience sampling. The survey was carried out during the months of February and April 2022. The Chi-square test and binary logistic regression analysis were used to evaluate the association between diet (saturated fats intake and dietary pattern) and sociodemographic factors with excess body weight in a cross-sectional analysis. Results: It was observed that students who reported high consumption of foods high in saturated fats (ORB = 1.14) and those who had a non-vegetarian dietary pattern (ORB = 2.76) were found to be more likely to have excess body weight. On the contrary, students who reported adherence to the vegetarian diet pattern for more than 5 years were less likely to be overweight or obese (ORB = 0.84). Being ≥26 years of age (ORB = 3.28), living in urban areas (ORB = 1.68) and coastal areas of the country (ORB = 1.17), and enrolled in the engineering faculty (ORB = 1.19), were significantly associated with excess body weight. Conclusion: The findings of the current study evidenced several factors associated with excess body weight in university students. Therefore, it is necessary to promote and implement healthy lifestyle programs, considering sociodemographic and dietary aspects such as age and dietary intake to control and prevent obesity in university students.

Body mass index and healthy lifestyle practices among Peruvian university students: a comparative study among academic discipline.

Background: Excess body weight and an unhealthy lifestyle are a risk factor for noncommunicable diseases. University students are susceptible to unhealthy habits and obesity. This study compared body mass index (BMI) and healthy lifestyle practices among university students from four academic disciplines: Health Sciences, Business Sciences, Human Sciences and Education, and Engineering/Architecture. Methods: A cross-sectional study was carried out using a sample of 6,642 university students selected by non-probability convenience sampling. The Diet and Healthy Lifestyle Scale (DEVS), the Peruvian validation of the Vegetarian Lifestyle Index (VLI), was used to assess healthy lifestyle practices. Results: Students in the areas of Business Sciences and Engineering/Architecture had a higher BMI than their peers in Health Sciences (B = 0.35, 95% CI: 0.15-0.56 and 0.32, 95% CI: 0.13-0.52; p = 0.001). Additionally, these students tended to adopt less healthy lifestyle (B = -0.11, 95% CI: -0.20 to -0.01 and -0.09, 95% CI: -0.18 to -0.00; p < 0.05) compared to those in Health Sciences. Conclusion: Although students of Health Sciences and Human Sciences and Education exhibited healthy lifestyle patterns, there is a clear need to improve eating and living habits in general among the university population to mitigate the risk factors associated with non-communicable diseases.

Model-Free Control of a Soft Pneumatic Segment.

Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments.

Evidence of validity and reliability of the environmental action scale in Peruvian university students.

Background: The environmental action scale is used to measure the degree of participation in collective environmental actions and has been shown to have adequate psychometric properties in developed countries. However, there are still no studies that have evaluated its performance in the Peruvian population. Methods: In this instrumental study, the environmental action scale (EAS) was translated, adapted, and validated. The EAS was administered to 352 university students between 18 and 35 years of age (Mage = 23.37, SD = 2.57) from different cities in Peru. A validity analysis was performed using two sources of evidence: content validity and internal structure, carrying out an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA). Results: The structure of the scale has been organized into three oblique factors. The findings confirmed the reliability and validity of the three dimensions of the EAS. Conclusion: Therefore, this scale is considered a valid option for assessing environmental action.

The relationship between positive and negative stress and posttraumatic growth in university students: the mediating role of resilience.

BACKGROUND: Information on understanding the mediating role of resilience in the relationship between posttraumatic growth (PTG) and positive and negative stress among students is limited. The objective of this research was to evaluate the mediating role of resilience in the relationship between positive and negative stress and PTG in university students. METHODS: The research was carried out using an associative strategy with an empirical approach and explanatory design, with a sample of 507 participants whose average age was 22.38 years ([Formula: see text]= 3.758), the sample was mostly composed of women (70.4%). RESULTS: The results allow us to describe that resilience plays a complete mediating role in the relationship between negative stress and PTG. On the other hand, mediation was partial when resilience mediated the relationship between positive stress and PTG. In addition, multigroup invariance analyses according to gender and geographic context show that there is no difference in males, females, and the region where they reside. CONCLUSION: In conclusion, the hypothesis that resilience fulfills the mediating function is ratified.

Structure and factorial invariance of the Grit-Original scale and convergent validity of the network with job satisfaction and happiness.

Background: The concept of Grit refers to a person's ability to maintain perseverance and passion in the pursuit of long-term objectives. However, research on the applicability of the Grit-Original scale (Grit-O) in the Latin American context is limited. Objective: This instrumental design study aimed to analyze the structure of this scale and its factorial invariance in relation to gender, as well as to examine its convergent validity with job satisfaction and happiness. Methods: A sample of 364 Peruvian workers that were selected through non-probabilistic convenience sampling in 2021. Results: The results of the confirmatory factor analysis showed that the two-dimensional structure of 12 items presented adequate goodness-of-fit indices. Additionally, the instrument is invariant between men and women. Likewise, the convergent relationship between the Grit scale, job satisfaction, and happiness variables was confirmed, which supports the validity of the instrument in the study context. Conclusion: The findings of the study confirm that the GRIT-O is a measure with adequate psychometric properties in the Peruvian context.

Psychometric Properties of the Peruvian Version of the MOS Scale for Social Support in Cancer Patients and Convergent Network with Quality of Life.

Introduction: One of the key psychosocial factors that impact mental and emotional health is social support. While much research has been conducted on the role of social support in the lives of cancer patients, there is a lack of studies that consider populations who need specific tools to assess this concept. Purpose: The aim of this study was to evaluate the psychometric properties of the MOS Social Support Scale (1991) in 499 Peruvian cancer patients between the ages of 18 and 87 (M= 46.30, SD = 15.747). Methods: In this study, three models of the MOS were analyzed based on 19-item versions (four factor, second order and bifactor model). Results: The results showed a better psychometric fit in the 19-item bifactor model with optimal fit indices through the structural equation method (SB-χ2/df = 1.94, CFI = 0.966, TLI= 0.955, SRMR = 0.038 and RMSEA = 0.058). Additionally, there was evidence of configural, metric and scalar invariance with this instrument according to the gender of the surveyed cancer patients. Convergent validity using a network analysis approach revealed positive associations between social support dimensions and quality of life. Conclusion: Emotional support and positive interactions dimensions were found to be important interconnections in the overall network, as indicated by their greater centralities. Therefore, this instrument could be suggested as a reliable way to evaluate cancer patients and their perceived support.

Association between frequency of breakfast consumption and cardiometabolic risk in Peruvian university teachers, 2019-2020.

Background: Although the relationship between health status and dietary intake has been extensively studied in the general population, there is a lack of research that has specifically examined the association between frequency of breakfast consumption and cardiometabolic risk in university teachers. Objective: To determine the association between the frequency of breakfast consumption and cardiometabolic risk in university teachers. Methods: A cross-sectional study was conducted in 176 teachers from a private university located in the eastern region of Lima, Peru (Mage: 37.0 years; SD: 0.8, range: 24-59 years). The study was conducted during the period from December 2019 to February 2020. Information was collected on anthropometric and biochemical parameters and frequency of breakfast consumption. Multinomial logistic regression models were used to explore the association between frequency of breakfast with sociodemographic, anthropometric, and biochemical variables. Results: The highest prevalence of excess body weight (44.4%) was observed in those who consumed breakfast 0 to 2 days/week, but without statistical differences. Those who reported Low-density lipoprotein cholesterol (LDL-C) < 160 mg/dL were 77% less likely to fall into the 3-5 day/week breakfast frequency category than those who reported a regular frequency of breakfast (6 to 7 days/week) (Adjusted OR = 0.23, 95% CI 0.08 to 0.73; p < 0.05). In addition, teachers who reported a breakfast frequency of 3 to 5 days/week were 83% more likely to have a glucose concentration < 110 mg/dL compared to those who consumed breakfast of 6 to 7 days/week (Adjusted OR = 0.17, 95% CI 0.04 to 0.75; p < 0.05). Conclusion: Skipping breakfast for an extended period of time can have detrimental effects on cardiometabolic health. Promoting the benefits of breakfast could be a health message of great public health interest.

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration.

The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission's first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole.

Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation.

Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.

Perspectives on ankle-foot technology for improving gait performance of children with Cerebral Palsy in daily-life: requirements, needs and wishes.

BACKGROUND: Ankle-foot orthoses (AFOs) are extensively used as a primary management method to assist ambulation of children with Cerebral Palsy (CP). However, there are certain barriers that hinder their prescription as well as their use as a mobility device in all kinds of daily-life activities. This exploratory research attempts to further understand the existing limitations of current AFOs to promote a better personalization of new design solutions. METHODS: Stakeholders' (professionals in CP and end-users with CP) perspectives on AFO technology were collected by two online surveys. Respondents evaluated the limitations of current assistive solutions and assessment methods, provided their expectations for a new AFO design, and analyzed the importance of different design features and metrics to enrich the gait performance of these patients in daily-life. Quantitative responses were rated and compared with respect to their perceived importance. Qualitative responses were classified into themes by using content analysis. RESULTS: 130 survey responses from ten countries were analyzed, 94 from professionals and 36 from end-users with CP. The most highly rated design features by both stakeholder groups were the comfort and the ease of putting on and taking off the assistive device. In general, professionals preferred new features to enrich the independence of the patient by improving gait at functional levels. End-users also considered their social acceptance and participation. Health care professionals reported a lack of confidence concerning decision-making about AFO prescription. To some degree, this may be due to the reported inconsistent understanding of the type of assistance required for each pathological gait. Thus, they indicated that more information about patients' day-to-day walking performance would be beneficial to assess patients' capabilities. CONCLUSION: This study emphasizes the importance of developing new approaches to assess and treat CP gait in daily-life situations. The stakeholders' needs and criteria reported here may serve as insights for the design of future assistive devices and for the follow-up monitoring of these patients.

Psychometric Network Analysis of the Problematic Pornography Use Scale (PPUS) in Peruvian Adults.

Objective: The aim of this research was to analyze the psychometric properties of the Problematic Pornography Use Scale (PPUS). Methods: A sample of 704 Peruvian youth and adults aged 18 to 62 years (M = 26, SD = 6.0) was considered, of which 56% were female and 43% male. The participants were from various cities in Peru: Lima (84%), Trujillo (2.6%), Arequipa (1.8%), and Huancayo (1.6%). The validity of the theoretical structure of the PPUS was carried out by means of two techniques: Confirmatory factor analysis (CFA) and Exploratory Graphical Analysis (EGA), a new effective and efficient dimension evaluation technique, whose measure consists of verifying the fit of the dimension structure. Results: Using the bifactor model, the hypothesis that PPUS has a unifactorial behavior was corroborated. These approximations of unidimensionality are also corroborated by the EGA method, where the centrality parameters and network loadings are found to have acceptable estimates. Conclusion: The results demonstrate the validity of the PPUS, contrasting with the factor model and verifying the unidimensionality of the construct, which provide useful directions for future studies on the instrumentalization of problematic pornography use scale.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System.

Background: People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear. Objective: The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder. Methods: A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships. Results: The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality. Conclusion: Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.

Use of Finite Elements in the Training of a Neural Network for the Modeling of a Soft Robot.

Soft bioinspired manipulators have a theoretically infinite number of degrees of freedom, providing considerable advantages. However, their control is very complex, making it challenging to model the elastic elements that define their structure. Finite elements (FEA) can provide a model with sufficient accuracy but are inadequate for real-time use. In this context, Machine Learning (ML) is postulated as an option, both for robot modeling and for its control, but it requires a very high number of experiments to train the model. A linked combination of both options (FEA and ML) can be an approach to the solution. This work presents the implementation of a real robot made up of three flexible modules and actuated with SMA (shape memory alloy) springs, the development of its model through finite elements, its use to adjust a neural network, and the results obtained.

Enneagram typologies and healthy personality to psychosocial stress: A network approach.

Introduction: Enneagram typologies may impact psychological well-being and stressful situations in college students. However, the literature is still limited in the study of dynamic personality models such as the Enneagram in Spanish-speaking university students, and a better understanding is needed. Objective: To analyze network associations and centrality measures of Enneagram personality typologies in Peruvian university students. Methods: A total of 859 Peruvian university students responded to two instruments assessing: The Pangrazzi's Enneagram personality types and healthy personality to psychosocial stress. All instruments showed good psychometric values (validity and consistency). A regularized cross-sectional network structure was estimated with Gaussian graphical model and the graphical LASSO. Results: Enneagram types 4, 5, and 6 presented the highest and positive associations in the network structure. Type 6 emerged as the node with the highest predictability. The healthy personality and type 7 acted as bridges between the communities, with types 6, 7, and 8 being the most central nodes. Conclusion: The findings suggest that Enneagram type 7 with healthy personality to psychosocial stress plays an important role in the development of the causal activation of the network model. The network shows causal associations between psychosocial stress and types 6, 7, 8, and 9.

A Mixed-Reality Tele-Operation Method for High-Level Control of a Legged-Manipulator Robot.

In recent years, legged (quadruped) robots have been subject of technological study and continuous development. These robots have a leading role in applications that require high mobility skills in complex terrain, as is the case of Search and Rescue (SAR). These robots stand out for their ability to adapt to different terrains, overcome obstacles and move within unstructured environments. Most of the implementations recently developed are focused on data collecting with sensors, such as lidar or cameras. This work seeks to integrate a 6DoF arm manipulator to the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree to perform manipulation tasks in SAR environments. The main contribution of this work is focused on the High-level control of the robotic set (Legged + Manipulator) using Mixed-Reality (MR). An optimization phase of the robotic set workspace has been previously developed in Matlab for the implementation, as well as a simulation phase in Gazebo to verify the dynamic functionality of the set in reconstructed environments. The first and second generation of Hololens glasses have been used and contrasted with a conventional interface to develop the MR control part of the proposed method. Manipulations of first aid equipment have been carried out to evaluate the proposed method. The main results show that the proposed method allows better control of the robotic set than conventional interfaces, improving the operator efficiency in performing robotic handling tasks and increasing confidence in decision-making. On the other hand, Hololens 2 showed a better user experience concerning graphics and latency time.

Una alternativa de análisis de redes en la exploración de los estados de salud mental, condiciones crónicas y COVID-19

El análisis de redes es una técnica estadística gráfica que permite visualizar e interpretar intuitivamente asociaciones entre síntomas y múltiples variables vinculadas al funcionamiento y espectro de diversas condiciones de salud. Siendo de relevancia clínica en el contexto actual de la pandemia de COVID-19, y ante su poca difusión en Sudamérica, se tuvo como objetivo un análisis narrativo de este modelo de red durante la pandemia. Se realizó una revisión narrativa de los estudios empíricos publicados desde mayo de 2020 a julio de 2021 en la base de datos de PubMed y ScienceDirect. Se seleccionaron las investigaciones que utilizaron redes psicométricas de correlación parcial en participantes evaluados durante la pandemia de COVID-19. Esta revisión reporta 13 estudios de red que utilizaron mayormente síntomas relacionados a la ansiedad (7 estudios), depresión (6 estudios) y estrés (6 estudios). La información resultante se agrupa en 3 grupos (publicaciones en revistas de psiquiatría, ciencias psicológicas, medicina y afines). La revisión presentada refiere que este análisis de red permite una nueva forma de identificar aspectos clínicos importantes como la comorbilidad, concurrencia de los síntomas y medidas no sintomatológicas, agrupaciones de síntomas con otras variables de naturaleza latente u observable que comparten una causa común, la exploración de nuevas hipótesis clínicas holísticas con variables epidemiológicas, psicológicas, biomédicas y contextuales de mayor interés, como la comparación de sistemas de asociación causal de variables de múltiples niveles en el proceso psicobiológico y sus factores de riesgo y protección en varios periodos de tiempo.

Summary Network analysis is a graphical statistical technique that allows visualizing and intuitively interpreting the spectrum of various health conditions, being of clinical relevance in the current context of the COVID-19 pandemic. Given its limited dissemination in South America, we aimed at a narrative analysis of this network model during the pandemic. A narrative review of empirical studies published from May 2020 to July 2021 in the PubMed and ScienceDirect database was performed. We selected research that used partial correlation psychometric networks in participants assessed during the COVID-19 pandemic. This review reports 13 network studies that used mostly symptoms related to anxiety (7 studies), depression (6 studies) and stress (6 studies). The resulting information is grouped into 3 clusters (publications in psychiatry, psychological sciences, medicine and related journals). The presented review refers that this network analysis allows a new way of identifying important clinical aspects such as comorbidity, concurrence of symptoms and nonsymptomatologic measures, groupings of symptoms with other variables of latent or observable nature that share a major common cause, the exploration of new holistic clinical hypotheses with epidemiological, psychological, biomedical and contextual variables of major current interest such as the comparison of causal association systems of multilevel variables in the psychobiological process, and their risk and protective factors in various time periods.