Data-as-a-Service Platform for Delivering Healthy Lifestyle and Preventive Medicine: Concept and Structure of the DAPHNE Project

Gibbons, Catherine; Pozo, Gonzalo Bailador del; Andrés, Javier; Lobstein, Tim; Manco, Melania; Lewy, Hadas; Bergman, Einat; O'Callaghan, David; Doherty, Gavin; Kudrautseva, Olga; Palomares, Angel; Ram, Roni; Olmo, Alberto

doi:10.2196/resprot.6589

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…The project reviewed the role of social and environmental factors related to obesity and conducted the qualitative analysis of multi-level interventions considering the reach, effectiveness, implementation, and adoption. Project Daphne [ 26 ] developed a data-as-a-service healthcare platform for tackling health, weight, physical activity, and lifestyle by linking the technology platforms with clinical supports. It should be noted that none of the aforementioned projects, which dealt with obesity prevention, provided privacy and security-aware protocols to handle sensitive personal information of the patients.…”

Section: Healthcare Data Privacymentioning

confidence: 99%

Big Data Warehouse for Healthcare-Sensitive Data Applications

Shahid

Nguyen

Kechadi

2021

Sensors

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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Section: Healthcare Data Privacymentioning

confidence: 99%

Big Data Warehouse for Healthcare-Sensitive Data Applications

Shahid

Nguyen

Kechadi

2021

Sensors

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“…This is an important point of departure from routine medical practice within a host of disease states and facilitates a proactive, preventive approach that allows for the quantification of cognitive optimization and overall brain health, the methodologies for which have been published elsewhere (39). Emerging computation methods imposed upon serial collection of agreed upon set of biomarkers and other available lifestyle factor has promise in the AD field and already has precedent in the literature (40, 41).…”

Section: Translating Prevention Research To Clinical Practicementioning

confidence: 99%

Alzheimer's “Prevention” vs. “Risk Reduction”: Transcending Semantics for Clinical Practice

et al. 2019

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The terms “prevention” and “risk reduction” are often used interchangeably in medicine. There is considerable debate, however, over the use of these terms in describing interventions that aim to preserve cognitive health and/or delay disease progression of Alzheimer's disease (AD) for patients seeking clinical care. Furthermore, it is important to distinguish between Alzheimer's disease prevention and Alzheimer's dementia prevention when using these terms. While prior studies have codified research-based criteria for the progressive stages of AD, there are no clear clinical consensus criteria to guide the use of these terms for physicians in practice. A clear understanding of the implications of each term will help guide clinical practice and clinical research. The authors explore the semantics and appropriate use of the terms “prevention” and “risk reduction” as they relate to AD in clinical practice.

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“…Another ongoing study, the Data-as-a-Service Platform for Healthy Lifestyle and Preventive Medicine (DAPHNE) project, aims to tackle obesity epidemic via personalized lifestyle medicine. By using mobile apps to continuously monitor lifestyle and behavior data in real time and a web-based health services portal, the project hopes to increase the individuals' awareness to their health trajectory and provide a holistic and individualized approach to treat obesity [ 27 ].…”

Section: A Glimpse Into the Futurementioning

confidence: 99%

A Systems Medicine Approach: Translating Emerging Science into Individualized Wellness

Bland¹,

Minich

Eck

2017

Advances in Medicine

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In today's aging society, more people are living with lifestyle-related noncommunicable diseases (NCDs) such as cardiovascular disease, type 2 diabetes, obesity, and cancer. Numerous opinion-leader organizations recommend lifestyle medicine as the first-line approach in NCD prevention and treatment. However, there is a strong need for a personalized approach as “one-size-fits-all” public health recommendations have been insufficient in addressing the interindividual differences in the diverse populations. Advancement in systems biology and the “omics” technologies has allowed comprehensive analysis of how complex biological systems are impacted upon external perturbations (e.g., nutrition and exercise), and therefore is gradually pushing personalized lifestyle medicine toward reality. Clinicians and healthcare practitioners have a unique opportunity in advocating lifestyle medicine because patients see them as a reliable source of advice. However, there are still numerous technical and logistic challenges to overcome before personal “big data” can be translated into actionable and clinically relevant solutions. Clinicians are also facing various issues prior to bringing personalized lifestyle medicine to their practice. Nevertheless, emerging ground-breaking research projects have given us a glimpse of how systems thinking and computational methods may lead to personalized health advice. It is important that all stakeholders work together to create the needed paradigm shift in healthcare before the rising epidemic of NCDs overwhelm the society, the economy, and the dated health system.

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Data-as-a-Service Platform for Delivering Healthy Lifestyle and Preventive Medicine: Concept and Structure of the DAPHNE Project

Cited by 5 publications

References 31 publications

Big Data Warehouse for Healthcare-Sensitive Data Applications

Big Data Warehouse for Healthcare-Sensitive Data Applications

Alzheimer's “Prevention” vs. “Risk Reduction”: Transcending Semantics for Clinical Practice

A Systems Medicine Approach: Translating Emerging Science into Individualized Wellness

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