Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform.

Diabetes is a high-prevalence disease that leads to an alteration in the patient's blood glucose (BG) values. Several factors influence the subject's BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient's BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject's metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

V-care: An Application to Support Lifestyle Improvement in Children with Obesity.

Obesity is increasing in the pediatric population and it represents an important risk factor for the life-long development of several diseases. The aim of this work is to reduce children obesity through an educational program delivered through a mobile application. Novelties of our approach are the involvement of the families in the program and a design inspired to psychological/behavioral change theories, with the aim of maximizing the chance of patients' compliance to the program. A pilot usability and acceptability study has been performed on ten children aged 6-12 years using a questionnaire to evaluate eight system features on a Likert scale from 1 to 5. Encouraging results were obtained: mean scores were all above 3.

V-care: An application to support lifestyle improvement in children with obesity.

BACKGROUND: Obesity is increasing in the pediatric population, and it represents an important risk factor for the life-long development of several diseases. Although health promotion represents the mainstay of obesity prevention and treatment, lifestyle modification programs are often unsuccessful. OBJECTIVES: The purpose of this article is to introduce the V-care app, a mobile health platform specifically developed to offer effective interaction and support young people in a long-term obesity treatment, combining different strategies to maximize the results of the lifestyle modification program and minimize the possibility of dropouts. METHODS: The V-care app is based on a conventional client-server architecture, but novelties of our approach are the involvement of families in the lifestyle modification program, and the design inspired to psychological/behavioral change theories, with the aim of raising the chance of patients' compliance to the program. V-care implements a goal-based behavioral intervention, providing specific feedbacks according to the patient's performance. A pilot usability and acceptability study was performed on a sample of thirteen children aged 6-12 years, using a questionnaire with a 5-points Likert scale to evaluate eight system features, identified as essential requirements based on the analysis of strengths and weaknesses of similar systems in literature. RESULTS: The pilot study highlighted very high rate of overall friendliness and perceived utility evaluation, while some critical issues emerged especially for the chatbot section, which may be due to the novelty of the technology. The positive evaluation of the design choices is confirmed by the average score greater than 3 for all the questions. CONCLUSIONS: The V-care app represents a digital innovation in the pediatric healthcare, and it could be introduced in children's primary healthcare nationwide, with the aim to offer an intervention program for controlling and preventing childhood obesity.

Effects of Endurance Exercise Intensities on Autonomic and Metabolic Controls in Children with Obesity: A Feasibility Study Employing Online Exercise Training.

Exercise is one of the major determinants of a healthy lifestyle, which is particularly important in childhood and serves as a powerful preventive tool. On the other hand, obesity and arterial hypertension rates are increasing in children, representing a huge risk for developing major cardiovascular and metabolic diseases in adult life. Of fundamental importance is the modality and volume of exercise required to obtain benefits. In this feasibility study, we considered a group of obese children, studied before and after a 12-week online exercise training program, and subdivided the participants into two groups considering the volume of exercise performed (above or below 1200 MET·min/week). This threshold level was applied in two different ways: subdivision A considered the total weekly physical activity volume (considering both time spent walking for at least 10 min consecutively and time spent performing structured exercise) and subdivision B considered only the weekly volume of structured exercise. We assessed autonomic and metabolic control and auxological and lifestyle parameters. We observed that the improved volume of structured exercise was associated with reduced arterial pressure percentile only in subdivision B and an improvement in markers of vagal and metabolic control was evident. Moreover, the 12-week online exercise training program, defined considering individual fitness level and progressively adapted as the goal was reached, proved to be sustainable from an economical and organizational point of view.

A comparative study of the definitions of intradialytic hypotension correlated with increased mortality to identify universal predictors.

Intradialytic hypotension (IDH) is a common complication in patients undergoing hemodialysis therapy. No consensus on the definition of intradialytic hypotension has been established so far. As a result, coherent and consistent evaluation of its effects and causes is difficult. Some studies have highlighted existing correlations between certain definitions of IDH and the risk of mortality for the patients. This work is mainly focused on these definitions. Our aim is to understand if different IDH definitions, all correlated with increased mortality risk, catch the same onset mechanisms or dynamics. To check whether the dynamics captured by these definitions are similar, we performed analyses of the incidence, of the IDH event onset timing, and checked whether there were similarities between the definitions in those aspects. We evaluated how these definitions overlap with each other and we evaluated which common factors could allow identifying patients at risk of IDH at the beginning of a dialysis session. The definitions of IDH we analyzed through statistical and machine learning approaches, showed a variable incidence on the HD sessions and had different onset time. We found that the set of parameters relevant for the prediction of the IDH was not always the same for the definitions considered. However, it can be observed that some predictors, such as the presence of comorbidities such as diabetes or heart disease, and a low pre-dialysis diastolic blood pressure, have shown universal relevance in highlighting an increased risk of IDH during the treatment. Among those parameters, the one that showed a major importance is the diabetes status of the patients. Diabetes or heart disease presence are permanent risk factors pointing out an increased IDH risk during the treatments, while, pre-dialysis diastolic blood pressure is instead a parameter that can change at every session and should be used to evaluate the specific risk to develop IDH for each session. The identified parameters could be used in the future to train more complex prediction models.

Impact of COVID-19 lockdown on PM concentrations in an Italian Northern City: A year-by-year assessment.

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.

A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.

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.

Effects of an Online Supervised Exercise Training in Children with Obesity during the COVID-19 Pandemic.

COVID-19 restrictions have dramatically reduced the active lifestyle and physical activity (PA) levels in the whole population, a situation that can contribute to weight gain and to develop obesity. To improve physical fitness (PF) in children with obesity during COVID-19 restrictions, sport specialists started to deliver physical training through tele-exercise. For these reasons, the aim of this study was to evaluate the effects of a 12-week online supervised training program in children with obesity on different PF components and PA levels. We enrolled a total of 40 Caucasian children (9 F/31 M; aged 11 ± 1.9 years) with obesity. The data collection consisted of a series of anthropometric measures, the PAQ-C questionnaire, and PF tests, valid and reliable tools to assess PF in children. We used a Wilcoxon's t-test and a Student's t-test, as appropriate, to assess the differences before and after the training protocol. A total of 37 patients completed the training protocol and were considered in the analysis. Our results show an improvement in all the PF tests, a reduction in the BMI z-score, the waist circumference, and in the waist-to-height ratio, and an increased PA level. In conclusion, the results of our study show that an online supervised training program is effective to promote PA, improving PF and reducing the BMI z-score in children with obesity.

Computer-based genealogy reconstruction in founder populations.

This paper describes a software tool that reconstructs entire genealogies from data collected from different and heterogeneous sources, including municipal and parish records archived over centuries. The tool exploits a record linkage algorithm relying on a rule-based data matching approach. It applies a general strategy for managing the ambiguities due to missing, imprecise or erroneous input data. The process follows an iterative approach that combines automatic pedigree reconstruction with software-empowered human data revision to improve the quality and the accuracy of the results and to optimize the matching rules. The paper discusses the results obtained by reconstructing the entire genealogy of the population of the Val Borbera, a geographically isolated valley in Northern Italy. The genealogy could be reconstructed from data going back as far as the XVI century. The resulting pedigree includes 75,994 trios, 58.9% of which belonging to a unique big family, reconstructed over 13 generations.

Information technology solutions to support translational research on inherited cardiomyopathies.

The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the condition within families that suffer from heart conditions that are caused by DCMs. The project is supported by a number of advanced biomedical informatics tools, including data warehousing, automated literature search and decision support. The paper describes the design of these tools and the current status of implementation.

i2b2 to Optimize Patients Enrollment.

i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable.

Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes.

OBJECTIVES: Despite the widespread diffusion of continuous glucose monitoring (CGM) systems, which includes both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), an effective application of CGM technology in clinical practice is still limited. The study aimed to investigate the relationship between isCGM-derived glycemic metrics and glycated hemoglobin (HbA1c), identifying overall CGM targets and exploring the inter-subject variability. METHODS: A group of 27 children and adolescents with type 1 diabetes under multiple daily injection insulin-therapy was enrolled. All participants used the isCGM Abbott's FreeStyle Libre system on average for eight months, and clinical data were collected from the Advanced Intelligent Distant-Glucose Monitoring platform. Starting from each HbA1c exam date, windows of past 30, 60, and 90 days were considered to compute several CGM metrics. The relationships between HbA1c and each metric were explored through linear mixed models, adopting an HbA1c target of 7%. RESULTS: Time in Range and Time in Target Range show a negative relationship with HbA1c (R2>0.88) whereas Time Above Range and Time Severely Above Range show a positive relationship (R2>0.75). Focusing on Time in Range in 30-day windows, random effect represented by the patient's specific intercept reveals a high variability compared to the overall population intercept. CONCLUSIONS: This study confirms the relationship between several CGM metrics and HbA1c; it also highlights the importance of an individualized interpretation of the CGM data.

Impaired Glucose-Insulin Metabolism in Multisystem Inflammatory Syndrome Related to SARS-CoV-2 in Children.

An interaction between metabolic glucose impairment and coronavirus disease 2019 is reported. The development of a severe multisystem inflammatory syndrome in children (MIS-C) related to SARS-CoV-2 infection has been described. We evaluated the impact of MIS-C on glycemic patterns in pediatric patients. A group of 30 children and adolescents affected by MIS-C were considered; all patients were normal weight. Clinical and biochemical assessments, including surrogate markers of insulin resistance (IR) such as homeostasis model analysis-IR (HOMA-IR) and triglyceride-glucose (TyG) indexes, were recorded. Patients were also invited to undergo an intermittently scanned continuous glucose monitoring (isCGM). HOMA-IR index was calculated in 18 patients (60%), of which 17 (94%) revealed a pathological value. TyG index was computed for all patients and pathological values were detected in all cases. In 15 patients, isCGM data were recorded on average for 9 days (±3 days). Overall, average glucose was 105 mg/dL (±16 mg/dL) and average time spent in the 70-180 mg/dL range (TIR) was 93.76%, with nearly 10% of glucose readings in the 141-180 mg/dL range; glycemic fluctuations over the hyperglycemic threshold were detected in four patients. Regular glucose monitoring may be useful to prevent metabolic imbalance and obtain a better outcome.

Text Mining approaches for automated literature knowledge extraction and representation.

Due to the overwhelming volume of published scientific papers, information tools for automated literature analysis are essential to support current biomedical research. We have developed a knowledge extraction tool to help researcher in discovering useful information which can support their reasoning process. The tool is composed of a search engine based on Text Mining and Natural Language Processing techniques, and an analysis module which process the search results in order to build annotation similarity networks. We tested our approach on the available knowledge about the genetic mechanism of cardiac diseases, where the target is to find both known and possible hypothetical relations between specific candidate genes and the trait of interest. We show that the system i) is able to effectively retrieve medical concepts and genes and ii) plays a relevant role assisting researchers in the formulation and evaluation of novel literature-based hypotheses.

Continuous Glucose and Heart Rate Monitoring in Young People with Type 1 Diabetes: An Exploratory Study about Perspectives in Nocturnal Hypoglycemia Detection.

A combination of information from blood glucose (BG) and heart rate (HR) measurements has been proposed to investigate the HR changes related to nocturnal hypoglycemia (NH) episodes in pediatric subjects with type 1 diabetes (T1D), examining whether they could improve hypoglycemia prediction. We enrolled seventeen children and adolescents with T1D, monitored on average for 194 days. BG was detected by flash glucose monitoring devices, and HR was measured by wrist-worn fitness trackers. For each subject, we compared HR values recorded in the hour before NH episodes (before-hypoglycemia) with HR values recorded during sleep intervals without hypoglycemia (no-hypoglycemia). Furthermore, we investigated the behavior after the end of NH. Nine participants (53%) experienced at least three NH. Among these nine subjects, six (67%) showed a statistically significant difference between the before-hypoglycemia HR distribution and the no-hypoglycemia HR distribution. In all these six cases, the before-hypoglycemia HR median value was higher than the no-hypoglycemia HR median value. In almost all cases, HR values after the end of hypoglycemia remained higher compared to no-hypoglycemia sleep intervals. This exploratory study support that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns can be helpful to improve NH detection and prevent fatal events.

Body hydration assessment using bioelectrical impedance vector analysis in neurologically impaired children.

Dehydration is common and frequently under-diagnosed in chronic malnourished children, leading to life-threatening conditions. In this pilot study we applied bioimpedance vector analysis (BIVA) to determine hydration status in 52 neurologically impaired (NI) paediatric patients (14.08 ± 5.32). Clinical and biochemical data were used to define malnutrition and dehydration. Body composition analysis and hydration were also assessed by BIVA and we considered 143 normal-weight healthy subjects (15.0 ± 1.7), as controls for hydration status assessment. BIVA revealed a pathological hydration status in NI children, showing higher resistance (p < 0.001) and reactance values (p = 0.001) compared to controls. No differences in reactance and resistance were detected between well-nourished and under-nourished subjects. Four patients out of 52 showed mild signs of dehydration; no severe dehydration was detected. Laboratory data, suggestive for dehydration, were similar in well-nourished and under-nourished NI subjects. In conclusion, in our sample of NI paediatrics, dehydration according to clinical signs and laboratory data was under-diagnosed. BIVA showed specific bioelectrical characteristics that could be compatible with impaired hydration status. Further studies are necessary to confirm that BIVA may an applicable tool for defining dehydration status and guiding rehydration in NI children.

An Extension of the i2b2 Data Warehouse to Support REDCap Dynamic Data Pull.

i2b2 and REDCap are two widely adopted solutions respectively to facilitate data re-use for research purpose and to manage non-for-profit research studies. REDCap provides the design specifications to build a web service used to import data from an external source with a procedure called DDP. In this work we have developed a web service that implements these specifications in order to import data from i2b2. Our approach has been tested with a real REDCap study.

Permutation Entropy Applied to Fitbit Data: Long-Term Sleep Analysis on One Healthy Subject.

In this work we exploited an algorithm, already present in the literature, and based on the notion of signal permutation entropy, to analyze a very long time series of sleep data from a single subject. The aim of the work is to explore methods for personalizing alerts related to sleep anomalies, and recommendations for improving sleep quality. As a matter of fact, sleep duration and sleep quality may differently affect daily performance of different people, as well as daily activities may differently affect sleeping during the night. Data have been collected from a Fitbit Alta HR activity tracker worn by the subject for about three years. Results show that personalized inferences may be very different from the generic (population-based) ones, and that correlations found may suggest subject-specific life-style modifications useful to improve sleep quality.