CrowdHEALTH: Big Data Analytics and Holistic Health Records.

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Faster, Stronger, Healthier: Adolescent-Stated Reasons for Dietary Supplementation.

OBJECTIVE: Examine the underlying reasons and sources of recommendation for dietary supplement (DS) use among adolescents. DESIGN: Cross-sectional analysis of children's development in Slovenia in September to October, 2014. SETTING: Nationally recruited sample. PARTICIPANTS: Adolescents aged 14-19 years enrolled in 15 high schools (n = 1,463). VARIABLES MEASURED: Reasons for and sources of recommendation for DS use, sports club membership, sports discipline, and extent of physical activity (PA) were self-reported data. ANALYSIS: Chi-square test of independence was performed to compare the prevalence of DS use between groups with different extents of PA and nonathletes/athletes, referring to 11 different reasons and 9 different sources of recommendation for DS use. RESULTS: Use of DS was widespread among adolescents (69%), athletes (76%), and nonathletes (66%). Higher prevalence of supplementation was observed in males, who justified it use for sports performance enhancement and better development and function of muscles. In contrast, females emphasized immune system improvement. Higher extent of PA was associated with a higher prevalence of DS use. This was especially evident in males, who participated in team sports. A high percentage of adolescents (41%) decided on their own to use DS and because of advice from parents or relatives (30%). CONCLUSIONS AND IMPLICATIONS: Several reasons for the widespread use of DS in adolescents were associated with sports participation. Therefore, educational programs regarding DS use should be targeted primarily to adolescents and their parents who are involved in sports, and especially team sports.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.