GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

Background: The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space

City4Age: Smart Cities for Health Prevention

City4Age is a research project co-funded by the European Commission. It utilizes data from smart cities and adhoc sensors for the prevention of Mild Cognitive Impairment (MCI) and frailty of aged people. Data are used to understand behavior and behavior changes of aged individuals. Technology enhanced highly individualized intervention is then used to “persuade” individuals to a better behavior. The data modeling, the processes and the SW are absolutely general, so that they could be reused for different segments of population and for different purposes

Data driven MCI and frailty prevention: Geriatric modelling in the City4Age project

This paper presents a step toward the development of a data-centric approach to prevention of Mild Cognitive Impairment and frailty in the elderly population. The scientific literature provides a large number of “indicators” for assessing the quality of behavior for aged individuals, in order to predict possible decaying. On the opposite side, a large variety of sensors and datasets today allows the effective collection of elementary data about actions performed by individuals. This paper proposes to build a bridge between these two sides. In a bottom-up vision, data from sensors and smart cities' datasets are aggregated and interpreted in a way that leads to reliable assessment of the indicators. In a top-down vision, indicators are translated into data analysis. The work described in this paper is part of City4age, a project partially funded by the EU within the H2020 Programme

A multicenter randomized trial for quality of life evaluation by noninvasive intelligent tools during post-curative treatment follow-up for head and neck cancer:clinical study protocol

Patients surviving head and neck cancer (HNC) suffer from high physical, psychological, and socioeconomic burdens. Achieving cancer-free survival with an optimal quality of life (QoL) is the primary goal for HNC patient management. So, maintaining lifelong surveillance is critical. An ambitious goal would be to carry this out through the advanced analysis of environmental, emotional, and behavioral data unobtrusively collected from mobile devices. The aim of this clinical trial is to reduce, with non-invasive tools (i.e., patients' mobile devices), the proportion of HNC survivors (i.e., having completed their curative treatment from 3 months to 10 years) experiencing a clinically relevant reduction in QoL during follow-up. The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary endpoint is a clinically relevant global health-related EORTC QLQ-C30 QoL deterioration (decrease & GE;10 points) at any point during 24 months post-treatment follow-up. The target sample size is 420 patients. Patients will be randomized to be followed up using the BD4QoL platform or per standard clinical practice. The BD4QoL platform includes a set of services to allow patients monitoring and empowerment through two main tools: a mobile application installed on participants' smartphones, that includes a chatbot for e-coaching, and the Point of Care dashboard, to let the investigators manage patients data. In both arms, participants will be asked to complete QoL questionnaires at study entry and once every 6 months, and will undergo post-treatment follow up as per clinical practice. Patients randomized to the intervention arm (n=280) will receive access to the BD4QoL platform, those in the control arm (n=140) will not. Eligibility criteria include completing curative treatments for non-metastatic HNC and the use of an Android-based smartphone. Patients undergoing active treatments or with synchronous cancers are excluded.Clinical Trial Registration: , identifier (NCT05315570)

Brucella Dysregulates Monocytes and Inhibits Macrophage Polarization through LC3-Dependent Autophagy

Brucellosis is caused by infection with Brucella species and exhibits diverse clinical manifestations in infected humans. Monocytes and macrophages are not only the first line of defense against Brucella infection but also a main reservoir for Brucella. In the present study, we examined the effects of Brucella infection on human peripheral monocytes and monocyte-derived polarized macrophages. We showed that Brucella infection led to an increase in the proportion of CD14++CD16− monocytes and the expression of the autophagy-related protein LC3B, and the effects of Brucella-induced monocytes are inhibited after 6 weeks of antibiotic treatment. Additionally, the production of IL-1β, IL-6, IL-10, and TNF-α from monocytes in patients with brucellosis was suppressed through the LC3-dependent autophagy pathway during Brucella infection. Moreover, Brucella infection inhibited macrophage polarization. Consistently, the addition of 3-MA, an inhibitor of LC3-related autophagy, partially restored macrophage polarization. Intriguingly, we also found that the upregulation of LC3B expression by rapamycin and heat-killed Brucella in vitro inhibits M2 macrophage polarization, which can be reversed partially by 3-MA. Taken together, these findings reveal that Brucella dysregulates monocyte and macrophage polarization through LC3-dependent autophagy. Thus, targeting this pathway may lead to the development of new therapeutics against Brucellosis

Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project

27Despite advances in treatments, 30% to 50% of stage III-IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.nonenoneCavalieri, Stefano; De Cecco, Loris; Brakenhoff, Ruud H; Serafini, Mara Serena; Canevari, Silvana; Rossi, Silvia; Lanfranco, Davide; Hoebers, Frank J P; Wesseling, Frederik W R; Keek, Simon; Scheckenbach, Kathrin; Mattavelli, Davide; Hoffmann, Thomas; López Pérez, Laura; Fico, Giuseppe; Bologna, Marco; Nauta, Irene; Leemans, C René; Trama, Annalisa; Klausch, Thomas; Berkhof, Johannes Hans; Tountopoulos, Vasilis; Shefi, Ron; Mainardi, Luca; Mercalli, Franco; Poli, Tito; Licitra, LisaCavalieri, Stefano; De Cecco, Loris; Brakenhoff, Ruud H; Serafini, Mara Serena; Canevari, Silvana; Rossi, Silvia; Lanfranco, Davide; Hoebers, Frank J P; Wesseling, Frederik W R; Keek, Simon; Scheckenbach, Kathrin; Mattavelli, Davide; Hoffmann, Thomas; López Pérez, Laura; Fico, Giuseppe; Bologna, Marco; Nauta, Irene; Leemans, C René; Trama, Annalisa; Klausch, Thomas; Berkhof, Johannes Hans; Tountopoulos, Vasilis; Shefi, Ron; Mainardi, Luca; Mercalli, Franco; Poli, Tito; Licitra, Lis

DataSheet_1_A multicenter randomized trial for quality of life evaluation by non-invasive intelligent tools during post-curative treatment follow-up for head and neck cancer: Clinical study protocol.pdf

Patients surviving head and neck cancer (HNC) suffer from high physical, psychological, and socioeconomic burdens. Achieving cancer-free survival with an optimal quality of life (QoL) is the primary goal for HNC patient management. So, maintaining lifelong surveillance is critical. An ambitious goal would be to carry this out through the advanced analysis of environmental, emotional, and behavioral data unobtrusively collected from mobile devices. The aim of this clinical trial is to reduce, with non-invasive tools (i.e., patients’ mobile devices), the proportion of HNC survivors (i.e., having completed their curative treatment from 3 months to 10 years) experiencing a clinically relevant reduction in QoL during follow-up. The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary endpoint is a clinically relevant global health-related EORTC QLQ-C30 QoL deterioration (decrease ≥10 points) at any point during 24 months post-treatment follow-up. The target sample size is 420 patients. Patients will be randomized to be followed up using the BD4QoL platform or per standard clinical practice. The BD4QoL platform includes a set of services to allow patients monitoring and empowerment through two main tools: a mobile application installed on participants’ smartphones, that includes a chatbot for e-coaching, and the Point of Care dashboard, to let the investigators manage patients data. In both arms, participants will be asked to complete QoL questionnaires at study entry and once every 6 months, and will undergo post-treatment follow up as per clinical practice. Patients randomized to the intervention arm (n=280) will receive access to the BD4QoL platform, those in the control arm (n=140) will not. Eligibility criteria include completing curative treatments for non-metastatic HNC and the use of an Android-based smartphone. Patients undergoing active treatments or with synchronous cancers are excluded.Clinical Trial Registration: ClinicalTrials.gov, identifier (NCT05315570).</p