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

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.

In Spain, the 2021/22 influenza season overlapped with the sixth wave of the 2019 coronavirus disease pandemic (COVID-19). Influenza is a major public health problem associated with high morbidity and mortality. The objectives of this study were to determine the knowledge, perceptions and practices of influenza vaccination in the Spanish population, coinciding with the COVID-19 pandemic, with special attention paid to people over 65 years of age. A cross-sectional study was carried out by conducting 2211 telephone interviews. It was observed that 81.6% of people ≥ 65 years were vaccinated annually or with some frequency compared to 35.5% of those under 65 years (p < 0.001). Fifty percent of Spaniards showed an intention to be vaccinated in the 2021/22 campaign, during the SARS-CoV2 pandemic. In the case of people ≥ 65 years old, this figure was 83% compared to 42% of those under 65 years old (p < 0.001). Significant predictors of intention to be vaccinated were age of 65 years or older (OR 1.8, 95% CI 1.3–2.5), female sex (OR 1.9, 95% CI 1.5–2.4), belonging to risk groups (OR 2.2, 95% CI 1.6–3.1) and having been previously vaccinated (OR 29.7, 95% CI 22.5–39.2). The main reasons for deciding to be vaccinated were the need to be protected against the virus and to be vaccinated annually. On the other hand, lack of recommendation and considering the influenza vaccine as not necessary were the main reasons for not getting vaccinated. In addition, health personnel stood out as the main source of information (32.9%) compared to traditional media (26.9%) and public administration (12.3%). This study aimed to assess and analyse the factors influencing willingness to receive influenza vaccines in the COVID-19 era among Spanish adults, as well as the main information channels and strategies to encourage vaccination.

Seasonal influenza is an acute respiratory infection caused by the influenza virus which constitutes a significant public health issue associated with high morbidity and mortality. The aim of this study was to investigate changes in attitudes, perceptions, and practices regarding influenza vaccination in the Spanish adult population during the COVID-19 pandemic, as well as their vaccination intentions, with special attention paid to those over 65 years old and in high-risk groups. To this end, a cross-sectional study was conducted through 2219 telephone interviews, and the results were compared with results obtained a year earlier. Regarding the reasons for deciding to get vaccinated in the 2022/23 season, a significant increase was observed in vaccine confidence (36.7% vs. 42.8%), social responsibility (32.5% vs. 43.8%), and in awareness of the importance of vaccination due to COVID-19 (21.7% vs. 25.4%). Advanced age (OR 2.8, 95% CI 2.0–3.9), belonging to high-risk groups (OR 2.7, 95% CI 2.0–3.7), and prior vaccination (OR 25.3, 95% CI 19.5–32.7) emerged as significant predictors for the intent to receive the influenza vaccine in the 2022/23 season. Continuously observing shifts in perceptions and behaviors related to influenza immunization is crucial to pinpoint factors that may influence the willingness to receive the vaccine and, in this way, design public health strategies that achieve a greater acceptance of it.

Objectives: Accurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, aiming to improve predictive accuracy and identify key contributing variable. Methods: We applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Performance was evaluated based on mean squared error (MSE) and coefficient of determination ( R 2 ), to assess how combining multiple data types influences prediction accuracy. Results: Models trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746, R 2 = 0.9941), demonstrating superior performance compared to decision trees (MSE = 0.1907, R 2 = 0.8991) and SVR (MSE = 1.5243, R 2 = − 2.2532). Non-accelerometric factors, including age and comorbidities, significantly contributed to fall risk prediction. Conclusions: Integrating accelerometric and non-accelerometric data improves fall risk prediction accuracy in older adults. Bayesian ridge regression trained on combined datasets provides superior predictive power compared to traditional models. These findings highlight the importance of multi-source data fusion for effective fall prevention strategies. Future work should validate these models in larger, more diverse populations to enhance clinical applicability.

Purpose: Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance. Methods: Various machine learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN with RandomOverSampler, KNN with SMOTE, and grid-searched optimized versions of KNN and Decision Tree, were applied to a dataset of 244 spine surgery patients. The dataset, comprising pre-surgical, psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study explored the impact of different variable groupings and oversampling techniques. Results: Experimental results indicate that the KNN model, especially when enhanced with RandomOverSampler and SMOTE, demonstrated superior performance, achieving accuracy values as high as 76% and an F1-score of 67%. Grid-searched optimized versions of KNN and Decision Tree also yielded significant improvements in predictive accuracy and F1-score. Conclusions: The study highlights the potential of advanced machine learning techniques and oversampling methods in predicting spine surgery outcomes. The results underscore the importance of careful variable selection and model optimization to achieve optimal performance. This system holds promise as a tool to assist healthcare professionals in decision-making, thereby enhancing spine surgery outcomes. Future research should focus on further refining these models and exploring their application across larger datasets and diverse clinical settings.