Anxiety in young people: Analysis from a machine learning model.

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.

Screen use during food consumption: Does it cause increased food intake? A systematic review.

Nowadays people use screens, such as mobile phones, television, or tablets, more often during mealtimes, which may have an effect on intake. This review aims to analyze the effect of screen use, during food consumption, on intake. A systematic review was carried out, based on those protocols established by PRISMA. The Cochrane Library, PubMed, Web of Science and Scopus databases were consulted. Experimental studies, published between 2010 and 2021, that recorded individual intake while using screens such as television, cell phones, or tablets, were selected. A total of 7181 relevant articles were obtained, which were then assessed in accordance with predetermined inclusion and exclusion criteria. Finally, 35 studies were included in the complete review: 22 compared different kinds of television content (e.g., adverts), five which compared television versus the absence of screens, four which compared television versus video games, two which compared the use of simultaneous screens, and two studies which included cell phones. A total of 27 studies reported consumption increases related to the presence of advertisements and food signals on screens. No significant differences in intake were reported in the eight studies that reported design or sample size limitations, or lack of control of certain variables. It is thus concluded that screen use during food consumption may increase intake. Education is necessary to regulate the habit of consuming food in the presence of screens. Also, the creation of policies that regulate advertising and food cues on screens are justified and must be accompanied by strategies to monitor compliance. As a limitation, further evidence is necessary in order to determine the effect of mobile phone and tablet use while eating. This protocol was registered via PROSPERO, ID: CRD42020211797.