Toward Systems Models for Obesity Prevention: A Big Role for Big Data.

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

Exploring Associations Between Children's Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard.

BACKGROUND: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO: Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data. OBJECTIVE: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. METHODS: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children's obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard's functionality was evaluated during a focus group discussion with public health experts. RESULTS: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children's eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children's physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard's potential to aid in a better understanding of the interplay between children's obesogenic behaviors and the environment, and improvements were suggested. CONCLUSIONS: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.

Fast Eating Is Associated with Increased BMI among High-School Students.

Fast self-reported eating rate (SRER) has been associated with increased adiposity in children and adults. No studies have been conducted among high-school students, and SRER has not been validated vs. objective eating rate (OBER) in such populations. The objectives were to investigate (among high-school student populations) the association between OBER and BMI z-scores (BMIz), the validity of SRER vs. OBER, and potential differences in BMIz between SRER categories. Three studies were conducted. Study 1 included 116 Swedish students (mean ± SD age: 16.5 ± 0.8, 59% females) who were eating school lunch. Food intake and meal duration were objectively recorded, and OBER was calculated. Additionally, students provided SRER. Study 2 included students (n = 50, mean ± SD age: 16.7 ± 0.6, 58% females) from Study 1 who ate another objectively recorded school lunch. Study 3 included 1832 high-school students (mean ± SD age: 15.8 ± 0.9, 51% females) from Sweden (n = 748) and Greece (n = 1084) who provided SRER. In Study 1, students with BMIz ≥ 0 had faster OBER vs. students with BMIz < 0 (mean difference: +7.7 g/min or +27%, p = 0.012), while students with fast SRER had higher OBER vs. students with slow SRER (mean difference: +13.7 g/min or +56%, p = 0.001). However, there was "minimal" agreement between SRER and OBER categories (κ = 0.31, p < 0.001). In Study 2, OBER during lunch 1 had a "large" correlation with OBER during lunch 2 (r = 0.75, p < 0.001). In Study 3, fast SRER students had higher BMIz vs. slow SRER students (mean difference: 0.37, p < 0.001). Similar observations were found among both Swedish and Greek students. For the first time in high-school students, we confirm the association between fast eating and increased adiposity. Our validation analysis suggests that SRER could be used as a proxy for OBER in studies with large sample sizes on a group level. With smaller samples, OBER should be used instead. To assess eating rate on an individual level, OBER can be used while SRER should be avoided.

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment.

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.

Formative Evaluation of a Smartphone App for Monitoring Daily Meal Distribution and Food Selection in Adolescents: Acceptability and Usability Study.

BACKGROUND: Obesity interventions face the problem of weight regain after treatment as a result of low compliance. Mobile health (mHealth) technologies could potentially increase compliance and aid both health care providers and patients. OBJECTIVE: This study aimed to evaluate the acceptability and usability and define system constraints of an mHealth system used to monitor dietary habits of adolescents in real life, as a first step in the development of a self-monitoring and lifestyle management system against adolescent obesity. METHODS: We recruited 26 students from a high school in Stockholm, Sweden. After a 30-minute information meeting and 5-minute individual instruction on how to use an mHealth system (smartphone with app and two external sensors), participants used it for 2-3 weeks to objectively collect dietary habits. The app and sensors were used by the participants, without supervision, to record as many main meals and snacks as possible in real life. Feasibility was assessed following the "mHealth evidence reporting and assessment checklist," and usability was assessed by questionnaires. Compliance was estimated based on system use, where a registration frequency of 3 main meals (breakfast, lunch, and dinner) per day for the period of the experiment, constituted 100% compliance. RESULTS: Participants included in the analysis had a mean age of 16.8 years (SD 0.7 years) and BMI of 21.9 kg/m2 (SD 4.1 kg/m2). Due to deviations from study instructions, 2 participants were excluded from the analysis. During the study, 6 participants required additional information on system use. The system received a 'Good' grade (77.1 of 100 points) on the System Usability Scale, with most participants reporting that they were comfortable using the smartphone app. Participants expressed a willingness to use the app mostly at home, but also at school; most of their improvement suggestions concerned design choices for the app. Of all main meals, the registration frequency increased from 70% the first week to 76% the second week. Participants reported that 40% of the registered meals were home-prepared, while 34% of the reported drinks contained sugar. On average, breakfasts took place at 8:30 AM (from 5:00 AM to 2:00 PM), lunches took place at 12:15 PM (from 10:15 AM to 6:15 PM), and dinners took place at 7:30 PM (from 3:00 PM to 11:45 PM). When comparing meal occurrence during weekdays vs weekends, breakfasts and lunches were eaten 3 hours later during weekends, while dinner timing was unaffected. CONCLUSIONS: From an infrastructural and functional perspective, system use was feasible in the current context. The smartphone app appears to have high acceptability and usability in high school students, which are the intended end-users. The system appears promising as a relatively low-effort method to provide real-life dietary habit measurements associated with overweight and obesity risk.

A smartphone application for semi-controlled collection of objective eating behavior data from multiple subjects.

BACKGROUND & OBJECTIVE: The study of eating behavior has made significant progress towards understanding the association of specific eating behavioral patterns with medical problems, such as obesity and eating disorders. Smartphones have shown promise in monitoring and modifying unhealthy eating behavior patterns, often with the help of sensors for behavior data recording. However, when it comes to semi-controlled deployment settings, smartphone apps that facilitate eating behavior data collection are missing. To fill this gap, the present work introduces ASApp, one of the first smartphone apps to support researchers in the collection of heterogeneous objective (sensor-acquired) and subjective (self-reported) eating behavior data in an integrated manner from large-scale, naturalistic human subject research (HSR) studies. METHODS: This work presents the overarching and deployment-specific requirements that have driven the design of ASApp, followed by the heterogeneous eating behavior dataset that is collected and the employed data collection protocol. The collected dataset combines objective and subjective behavior information, namely (a) dietary self-assessment information, (b) the food weight timeseries throughout an entire meal (using a portable weight scale connected wirelessly), (c) a photograph of the meal, and (d) a series of quantitative eating behavior indicators, mainly calculated from the food weight timeseries. The designed data collection protocol is quick, straightforward, robust and capable of satisfying the requirement of semi-controlled HSR deployment. RESULTS: The implemented functionalities of ASApp for research assistants and study participants are presented in detail along with the corresponding user interfaces. ASApp has been successfully deployed for data collection in an in-house testing study and the SPLENDID study, i.e., a real-life semi-controlled HSR study conducted in the cafeteria of a Swedish high-school in the context of an EC-funded research project. The two deployment studies are described and the promising results from the evaluation of the app with respect to attractiveness, usability, and technical soundness are discussed. Access details for ASApp are also provided. CONCLUSIONS: This work presents the requirement elucidation, design, implementation and evaluation of a novel smartphone application that supports researchers in the integrated collection of a concise yet rich set of heterogeneous eating behavior data for semi-controlled HSR.

Human papillomavirus E7 protein detection as a method of triage to colposcopy of HPV positive women, in comparison to genotyping and cytology. Final results of the PIPAVIR study.

The objective of the presented cross-sectional-evaluation-screening study is the clinical evaluation of high-risk(hr)HPVE7-protein detection as a triage method to colposcopy for hrHPV-positive women, using a newly developed sandwich-ELISA-assay. Between 2013-2015, 2424 women, 30-60 years old, were recruited at the Hippokratio Hospital, Thessaloniki/Greece and the Im Mare Klinikum, Kiel/Germany, and provided a cervical sample used for Liquid Based Cytology, HPV DNA genotyping, and E7 detection using five different E7-assays: "recomWell HPV16/18/45KJhigh", "recomWell HPV16/18/45KJlow", "recomWell HPV39/51/56/59", "recomWell HPV16/31/33/35/52/58" and "recomWell HPVHRscreen" (for 16,18,31,33,35,39,45,51,52,56,58,59 E7), corresponding to different combinations of hrHPVE7-proteins. Among 1473 women with eligible samples, those positive for cytology (ASCUS+ 7.2%), and/or hrHPV DNA (19.1%) were referred for colposcopy. Cervical Intraepithelial Neoplasia grade 2 or worse (CIN2+) was detected in 27 women (1.8%). For HPV16/18-positive women with no triage, sensitivity, positive predictive value (PPV) and the number of colposcopies needed to detect one case of CIN2+ were 100.0%, 11.11% and 9.0 respectively. The respective values for E7-testing as a triage method to colposcopy ranged from 75.0-100.0%, 16.86-26.08% and 3.83-5.93. Sensitivity and PPV for cytology as triage for hrHPV(non16/18)-positive women were 45.45% and 27.77%; for E7 test the respective values ranged from 72.72-100.0% and 16.32-25.0%. Triage of HPV 16/18-positive women to colposcopy with the E7 test presents better performance than no triage, decreasing the number of colposcopies needed to detect one CIN2+. In addition, triage of hrHPV(non16/18)-positive women with E7 test presents better sensitivity and slightly worse PPV than cytology, a fact that advocates for a full molecular screening approach.

Diagnostic accuracy of high-risk HPV DNA genotyping for primary cervical cancer screening and triage of HPV-positive women, compared to cytology: preliminary results of the PIPAVIR study.

PURPOSE: The purpose of the presented PIPAVIR (persistent infections with human papillomaviruses; http://www.pipavir.com ) subanalysis is to assess the performance of high-risk (hr) HPV-DNA genotyping as a method of primary cervical cancer screening and triage of HPV positive women to colposcopy compared to liquid-based cytology (LBC) in an urban female population. METHODS: Women, aged 30-60, provided cervicovaginal samples at the Family-Planning Centre, Hippokratio Hospital of Thessaloniki, Greece, and the Department of Gynecology and Obstetrics in Mare Klinikum, Kiel, Germany. Cytology and HPV genotyping was performed using LBC and HPV Multiplex Genotyping (MPG), respectively. Women positive for cytology [atypical squamous cells of undetermined significance (ASC-US) or worse] or hrHPV were referred for colposcopy. RESULTS: Among 1723/1762 women included in the final analysis, hrHPV and HPV16/18 prevalence was 17.7 and 9.6%, respectively. Cytology was ASCUS or worse in 7.6%. Cervical Intraepithelial Neoplasia grade 2 or worse (CIN2+) was detected in 28 women (1.6%). Sensitivity of cytology (ASCUS or worse) and HPV DNA testing for the detection of CIN2+ was 50.0 and 100%, and specificity was 94.49 and 85.49%, respectively. The screening approach according to which only women positive for HPV16/18 and for hrHPV(non16/18) with ASCUS or worse were referred to colposcopy presented 78.57% sensitivity and 13.17% positive predictive value (PPV). CONCLUSIONS: HPV testing represents a more sensitive methodology for primary cervical cancer screening compared to cytology. For triage of HPV positive women to colposcopy, partial HPV genotyping offers better sensitivity than cytology, at the cost of higher number of colposcopies.

A Sensor-Enabled Smartphone Application to Collect Eating Behavior Data for Population Screening.

The worldwide extent of obesity and eating disorders (ED) today highlights the necessity for efficient treatment, but also early prevention of eating-related diseases. A promising category of therapeutic and preventive interventions comes from the domain of behavioral informatics (BI), whose purpose is to monitor and modify harmful behaviors - unhealthy eating in the particular case - with the help of information and communication technologies. Smartphones have already shown great promise in delivering such BI interventions in the field of obesity and ED. In fact, plenty of smartphone applications aiming to monitor and support the change of eating behavior with the help of built-in or external sensors have been proposed in the scientific literature. However, to the best of our knowledge, no smartphone application up to date has been designed to collect eating behavior data for the purpose of population screening against obesity or ED. In this work we describe a novel, sensor-enabled smartphone application that captures in-meal behavioral data from multiple subjects in a brief data collection process, with the end goal of recording, in detail, the user's eating style throughout a cooked meal. These data can later be employed for assessing the subjects' risk for obesity or ED. The proposed application has undergone preliminary evaluation with respect to its usability and technical soundness, yielding promising results.

Semantic integration of cervical cancer data repositories to facilitate multicenter association studies: the ASSIST approach.

The current work addresses the unification of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifies multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing flexibility by allowing the formation of study groups "on demand" and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profile) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the definition and representation of the disease's medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains.

Association studies on cervical cancer facilitated by inference and semantic technologies: the assist approach.

Cervical cancer (CxCa) is currently the second leading cause of cancer-related deaths, for women between 20 and 39 years old. As infection by the human papillomavirus (HPV) is considered as the central risk factor for CxCa, current research focuses on the role of specific genetic and environmental factors in determining HPV persistence and subsequent progression of the disease. ASSIST is an EU-funded research project that aims to facilitate the design and execution of genetic association studies on CxCa in a systematic way by adopting inference and semantic technologies. Toward this goal, ASSIST provides the means for seamless integration and virtual unification of distributed and heterogeneous CxCa data repositories, and the underlying mechanisms to undertake the entire process of expressing and statistically evaluating medical hypotheses based on the collected data in order to generate medically important associations. The ultimate goal for ASSIST is to foster the biomedical research community by providing an open, integrated and collaborative framework to facilitate genetic association studies.

Reliability assessment of home health care services.

In this paper, a model of reliability assessment of services in Home Health Care Delivery is presented. Reliability is an important quality dimension for services and is included in non-functional requirements of a system. A stochastic Markov model for reliability assessment is applied to patient communication services, in the field of home health care delivery. The methodology includes the specification of scenarios, the definition of failures in scenarios as well as the application of the analytical model. The results of the methodology reveal the critical states of the Home Health Care System and recommendations for improvement of the services are proposed. The model gives valuable results in predicting service reliability and, independently of the error types, it can be applied to all fields of Regional Health Network (RHN).

The Citizen Health System (CHS): a modular medical contact center providing quality telemedicine services.

In the context of the Citizen Health System (CHS) project, a modular Medical Contact Center (MCC) was developed, which can be used in the monitoring, treatment, and management of chronically ill patients at home, such as diabetic or congestive heart failure patients. The virtue of the CHS contact center is that, using any type of communication and telematics technology, it is able to provide timely and preventive prompting to the patients, thus, achieving better disease management. In this paper, we present the structure of the CHS system, describing the modules that enable its flexible and extensible architecture. It is shown, through specific examples, how quality of healthcare delivery can be increased by using such a system.