A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments. We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps. Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found. The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as—compared to the study period in 2019—during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

The COVID-19 pandemic disrupted public health, economies, and societies worldwide, with lasting effects still visible today. Containment policies were implemented to reduce viral transmission, but their effectiveness varied across geographic regions. Understanding the interplay between policies, epidemiological variables, vaccines, and variants is crucial for optimizing future epidemic responses. This study applies a vector autoregressive mixed-effect model to data from nine European countries over the entire pandemic duration, using weekly data to provide both global and country-specific analyses. Policy stringency was assessed using the Oxford Covid-19 Government Response Tracker. Our findings reveal that the relationship between epidemiological indicators and policy stringency evolved over time. Initially, hospitalizations were the primary driver of containment measures, but as vaccinations increased and new variants emerged, new case numbers became an important factor too. While all vaccine doses contributed to reducing cases and hospitalizations, only the second dose was associated with policy stringency. No significant association was found between virus variants and policy measures. Despite small country-specific differences, these insights highlight the complex interdependencies that shaped pandemic management. This framework can support future public health decision-making by balancing epidemiological trends with tailored policy responses at both national and international levels.

Introduction: Average age is increasing worldwide, raising the public health burden of age-related diseases, as more resources will be required to manage treatments. Phenotypic Age is a score that can be useful to provide an estimate of the probability of developing aging-related conditions, and prevention of such conditions could be performed efficiently studying the mechanisms leading to an increased phenotypic age. The objective of this study is to characterize the mechanisms that lead to aging acceleration from the interactions among sociodemographic factors, health predispositions and biological phenotypes. Methods: We present an approach based on the combination of mediation analysis and structural equation models (SEM) to better characterize these mechanisms, quantifying the interactions between biological and external factors and the effects of preexisting health conditions and socioeconomic disparities. We use two independent cohorts of the NHANES dataset: we use the largest (n = 13,186) to select the variables that enlarge the gap between phenotypic and chronological ages, we then create a SEM based on nested linear regressions to quantify the influence of all sociodemographic variables expressed in three latent variables indicating ethnicity, socioeconomic status and preexisting health status. We then replicate the model and apply it to the second cohort (n = 4,425) to compare the results. Results: Results show that phenotypic age increases with poor glucose control or obesity-related biomarkers, especially if combined with a low socioeconomic status or the presence of chronic or vascular diseases, and provide a framework to quantify these relationships. Black ethnicity, low income/education and a history of chronic diseases are also associated with a higher phenotypic age. Although these findings are already known in literature, the proposed SEM-based framework provides an useful tool to assess the combinations of these heterogeneous factors from a quantitative point of view. Conclusion: In an aging society, phenotypic age is an important metric that can be used to estimate the individual health risk, however its value is influenced by a myriad of external factors, both biological and sociodemographic. The framework proposed in this paper can help quantifying the combined effects of these factors and be a starting point to the creation of personalized prevention and intervention strategies.

Despite the silent effects sometimes hidden to the major audience, air pollution is becoming one of the most impactful threat to global health. Cities are the places where deaths due to air pollution are concentrated most. In order to correctly address intervention and prevention thus is essential to assest the risk and the impacts of air pollution spatially and temporally inside the urban spaces. PULSE aims to design and build a large-scale data management system enabling real time analytics of health, behaviour and environmental data on air quality. The objective is to reduce the environmental and behavioral risk of chronic disease incidence to allow timely and evidence-driven management of epidemiological episodes linked in particular to two pathologies; asthma and type 2 diabetes in adult populations. developing a policy-making across the domains of health, environment, transport, planning in the PULSE test bed cities.