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The average life expectancy of the world’s population is increasing and the healthcare systems sooner than later will be compromised by its reduced capacity and its highly economic cost; in addition, the age distribution of the population is leading towards the older spectrum. This trend will lead to immeasurable and unexpected economic problems and social changes. In order to face up this challenge and complex economic and social problem, it is necessary to rely on the appropriate digital tools and technological infrastructures for ensuring that the elderly are properly cared in their everyday living environments and they can live independently for longer. This article presents ACTIVAGE IoT Ecosystem Suite (AIoTES), a concrete reference architecture and its implementation process that addresses these issues and that was designed within the first European Large Scale Pilot, ACTIVAGE, a H2020 funded project by the European Commission with the objective of creating sustainable ecosystems for Active and Healthy Ageing (AHA) based on Internet of Things and big data technologies. AIoTES offers platform level semantic interoperability, with security and privacy, as well as Big Data and Ecosystem tools. AIoTES enables and promotes the creation, exchange and adoption of cross-platform services and applications for AHA. The number of existing AHA services and solutions are quite large, especially when state-of-the-art technology is introduced, however a concrete architecture such as AIoTES gains more importance and relevance by providing a vision for establishing a complete ecosystem, that looks for supporting a larger variety of AHA services, rather than claiming to be a unique solution for all the AHA domain problems. AIoTES has been successfully validated by testing all of its components, individually, integrated, and in real-world environments with 4345 direct users. Each validation is contextualized in 11 Deployment Sites (DS) with 13 Validation Scenarios covering the heterogeneity of the AHA-IoT needs. These results also show a clear path for improvement, as well as the importance for standardization efforts in the ever-evolving AHA-IoT domain.

Sustainability is a very complex concept made up of a multitude of interacting aspects that do not necessarily work synergistically with each other. The consequential outcome of cross-cutting drivers, such as digitalisation, is often difficult to assess, as the achievement of certain targets may also inadvertently hinder progress towards others. This investigation describes a comprehensive and systematic country-based analysis of statistical associations between digitalization and sustainability indicators operating at three different levels (i.e., index, goal and indicators). Results showed strong correlations between the composite indices for digitalization (IDI Development Index), sustainability (SDG Index from) and economic growth (GCI and GDP). However, the analysis of lower-level indicators provides a more ambiguous picture, with 2 of the sustainability goals and 22 % of the sustainability indicators included in the SDG Index showing negative associations with digitalisation. It appears that while synergies are generated in aspects related to economic and social sustainability, trade-offs occur in areas related to environmental protection such as climate change, depletion of natural resources and waste generation due to their negative associations with existing economic development models. These structural obstacles need to be acknowledged and adequately managed in order to ensuring harmonious and integral progress towards effective sustainability.

Health education is essential for type 1 diabetic patients to actively participate in the decision-making process about their disease. Under the framework of the INCAP project, a mobile application has been designed and developed with an easy-to-use interface for type 1 diabetic patients to improve their empowerment, activation and thus their self-control and improvement of their treatment adherence.

This paper describes the framework for the creation of a Living Labs network based on the experience of the setting up, growth and further consolidation of the European Living Labs and Test Beds Network focused on Health. The manuscript presents how to create an open innovation ecosystem through a network of Living Labs and Test Beds, introducing its value proposition and current status.

Background and aims Population ageing is a typical phenomenon of developed countries with a great influence in their economy and society, with an increment on age-related expenditures. Disruptive solutions are needed to deploy new cost-effective and sustainable solutions for aging well and independent living of our seniors. In this sense, new technological paradigms as IoT technologies and smart cities have the potential to become main drivers for innovation uptake. The purpose of this study is to describe a longitudinal cohort study in smart cities for assessing early frailty symptoms deploying an unobtrusive IoT-based system in the Madrid city. Methods A system was deployed in the Madrid city with the participation of 45 elderly users for an average of 71 weeks. Metrics were assessed by the available sensors in combination with the open data infrastructure of Madrid. Metrics include activity of the user, weekly visits pattern and transport daily usage pattern. System engagement was also monitored. Participants are assessed bimonthly with health and functional questionnaires. Results 45 older adults with a mean age of 79.1 years. Participants activity patterns monitor detected changes during potentially risky situations that usually were not reported by traditional assessment tools. Analysis of data collected enabled to identify absence of frailty (robust or post-robust status) Discussion and conclusions The results demonstrate the feasibility of engaging older adults with an IoT-based system and the successful collection of their activity metrics. Variation in the activity patterns may be a first sign of functional decline and enables to identify potential areas of early intervention.

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

Purpose Hospitalization at Home (HaH) has proven to be more efficient and effective than conventional one, but it also requires a higher number of resources and specialised personnel. Information technologies can make this process scalable and allow physicians and nurses to deliver remote healthcare services for patients hospitalized at home. However, a correct and satisfactory usage of technology requires an adequate training of professionals and patients. This paper describes a new model for training healthcare professionals on managing remote ICT-based services for Hospitalization at Home. Methods The model was defined based on mix-method that combined the PICO model and a User Centred Design methodology, oriented to identify and discover the healthcare professionals needs and the training instruments in the literature that directly involved these professionals. These aspects were used in the definition and development of the assessment framework of the proposed training model. Results A training model for healthcare professionals focused on achieving an effective uptake of complex digital interventions such as Hospitalization at Home was defined. The selected mix-method led to the identification of four different blocks, that were considered as the main areas to include in a training programme. The model identifies measurable elements for assessing acceptability, workability increment and integration into daily clinical practice outcomes, as well as for evaluating the proposed training content and its outcomes. Conclusions The proposed training model highlights the key aspects of training health professionals to favour an effective and successful implementation of complex technological healthcare interventions in the context of ICT-based HaH ICT.

Implementing emerging technologies is a complex task which requires time, precision, and organization. The definition of a logical structure for classifying and organizing complex information during the design process of a technological solution provides flexibility while the process becomes more prescriptive. One of the most used methods in the ICT field is the user-centered design methodology (UCD). UCD methodology situates the final user as the cornerstone of the research and development process since the success or failure of a technological solution will depend on users’ acceptance. The following chapter presents experiences, best practices and lessons learned applying UCD methodology in different European projects from several years of work conducted at LifeSTech group from UPM, in areas such as: chronic diseases management, accessibility, and cognitive rehabilitation. Specifically, the chapter explains how the UCD methodology was applied during the different stages of the design and development process for different domains and use cases.

The aim of this work is to quantify, assess, and identify hotspots in the environmental sustainability of newly constructed ICT networks designed to provide internet access (4 G LTE mobile technology) to regions still lacking this service. The analysis has been carried out on six demographic areas, from high-density urban and peri‑urban to remote rural, using ISO 14,040. A Dynamic Inventory Model (DIM) relating demographic/connectivity features with foreground material and energy inventories was validated using real data from Peru. The results showed carbon footprints between 81 and 103 kg CO2 eq./subscription/year, equivalent to 1.35 – 1.73 kg CO2 eq./Gb. Most of this (between 68 and 86%) correspond to end user devices, primarily in the form of embodied emissions. Operational emissions account for about one-third of the total and derive primarily from the electricity consumed by end user devices, and to a lower extent by access networks and data centers. Linear correlations were observed between operational - embodied carbon emissions and the number of subscribers. This trend was overturned in very small ICT networks designed to serve sparsely populated rural areas, due to higher energy consumption and carbon emissions per functional unit generated by access and IP network components. The robustness of these results was studied through sensitivity and uncertainty analyses.

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿, This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Introduction Home-based programmes for cardiac rehabilitation play a key role in the recovery of patients with coronary artery disease. However, their necessary educational and motivational components have been rarely implemented with the help of modern mobile technologies. We developed a mobile health system designed for motivating patients to adhere to their rehabilitation programme by providing exercise monitoring, guidance, motivational feedback, and educational content. Methods Our multi-disciplinary approach is based on mapping “desired behaviours” into specific system’s specifications, borrowing concepts from Fogg’s Persuasive Systems Design principles. A randomised controlled trial was conducted to compare mobile-based rehabilitation (55 patients) versus standard care (63 patients). Results Some technical issues related to connectivity, usability and exercise sessions interrupted by safety algorithms affected the trial. For those who completed the rehabilitation (19 of 55), results show high levels of both user acceptance and perceived usefulness. Adherence in terms of started exercise sessions was high, but not in terms of total time of performed exercise or drop-outs. Educational level about heart-related health improved more in the intervention group than the control. Exercise habits at 6 months follow-up also improved, although without statistical significance. Discussion Results indicate that the adopted design methodology is promising for creating applications that help improve education and foster better exercise habits, but further studies would be needed to confirm these indications.

Background: Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability. Methods: The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients. Results: The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach. Conclusions: Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.

Cancer is the second leading cause of death worldwide. In order to reduce this burden, new strategies need to be implemented. With the evolution of computational techniques, the amount of clinical data available has increased considerably. However, these data are not always easily accessible, with some barriers of different nature preventing their retrieval and analysis. With the aim of better understanding these barriers and eventually support decision making through digital tools, a literature search has been conducted and, according to the analysis of the resulting bibliography, a survey to validate the literature findings has been distributed to clinical and computer science experts who work on cancer research in different countries. The answers received allow us to identify the main issues that need to be addressed, which are analyzed and presented in this paper. This work is carried out in the context of BD2Decide European Research project.

Europe is currently the oldest continent having the “old-age dependency ratio” - persons over 65 compared to the active population. To cope with this situation, the deployment of Active and Healthy Aging (AHA) services through IoT is becoming increasingly relevant, to make this population as independent as possible within their environment. This study presents the whole process to design and implement the “Public Evaluation Website”, a tool that will present the results and achievements generated in ACTIVAGE, a Large-Scale Pilot that implements Internet of Things solutions for AHA in 9 European regions and cities.

In recent years, the evolution of the technology and its adoption by the citizens has made possible the inclusion of new tools in the innovation processes. These tools have allowed the development of better products and services and the reduction of the time to market of these solutions for the benefit of all end users. In this context, the concept of Living Lab has emerged, as an open innovation ecosystem where stakeholders, including professionals, developers and end users, can cooperate on solutions to address relevant challenges using co-creation and evaluation methodologies. Living Labs can support and enhance the innovation process throughout the different phases of the value chain, and they can also act as the connectors between the needs (the users) and the offer (the suppliers). This chapter presents the Living Labs as the novel instruments for evaluating, assessing and validating innovative products, solutions or services in the particular domain of smart living environments.

The present work seizes the opportunity to counter IoT technology fragmentation that the World Wide Web Consortium is offering with the upcoming standard Web Of Things. A gateway service based on this standard and an air quality manager sensor were designed and implemented in a Smart Home with the aim to develop a tool to manage this critical parameter and improve people's quality of life. The system proved to be able to report and manage the Smart Home devices to reduce Fine Particulate Matter concentration on demand and in real time, also allowing to record data to be consulted by the Smart Home users. The technology produced is agnostic, scalable and replicable in other environments and contexts such as industry, healthcare and Smart Cities among others.

The constructivist approach is interested in creating knowledge through active engagement and encourages students to build their knowledge from their experiences in the world. Learning through digital game making is a constructivist approach that allows students to learn by developing their own games, enhancing problem-solving skills and fostering creativity. In this context two tools, Create@School App and the Project Management Dashboard (PMD), were developed to enable students from different countries to be able to adapt their learning material by programming and designing games for their academic subjects, therefore integrating the game mechanics, dynamics, and aesthetics into the academic curriculum. This paper focuses on presenting the validation context as well as the evaluation of these tools. The Hassenzahl model and AttrakDiff survey were used for measuring users&rsquo, experience and satisfaction, and for understanding emotional responses, thus providing information that enables testing of the acceptability and usability of the developed apps. After two years of usage of code-making apps (i.e., Create@School and its pre-design version Pocket Code), the pupils processed knowledge from their academic subjects spontaneously as game-based embedded knowledge. The students demonstrated creativity, a practical approach, and enthusiasm regarding making games focused on academic content that led them to learning, using mobile devices, sensors, images, and contextual information. This approach was widely accepted by students and teachers as part of their everyday class routines.

Recent studies showed that Parkinson’s disease (PD) patients improved their gait parameters while walking with rhythmic auditory stimulation (RAS). They achieved a longer stride length, a reduced stride time variability and a higher walking speed. Combining RAS with mobile gait analysis would allow continuous monitoring of RAS effects and gait in natural environments. This paper proposes a mobile solution for home-based assessment of RAS by combining RAS gait training and a mobile system for data acquisition. Existing datasets were used to investigate the cadence of PD patients and to propose suitable frequencies for RAS gait training. The cadence calculation was implemented using a peak detection algorithm, which uses the time difference between two mid-swing events as stride time values. We validated our system as a whole using a cohort of 13 PD patients who performed RAS gait training. The algorithms were also validated against the eGaIT system, a state-of-the-art system, and achieved a mean F 1 score for detected strides of 97.57 % ± 0.86 % and a mean absolute error for the cadence of 0.16 spm ± 0.09 spm. This study lays the ground work for further clinical studies investigating the effectiveness of mobile RAS within a home environment.

[EN] Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes., This study was funded by European Commission under the 7th Framework Program grant agreement number 216270.3.

The paper presents the Pulse project, a European project founded under the Horizon 2020 program.

The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and development of a novel participatory citizen science-based application and data ecosystem model. These developments are imperative and scientifically designed to gather and process perceptual sensing of urban, environmental, and health data. This data acquisition approach allows citizens to gather and generate environment- and health-related data through mobile devices. The sum of all citizens&rsquo, data will continuously enrich and increase the volumes of data coming from the city sensors and sources across geographical locations. These scientifically generated data, coupled with data from the city sensors and sources, will enable specialized predictive analytic solutions to empower citizens with urban, environmental, and health recommendations, while enabling new data-driven policies. Although it is difficult for citizens to relate their personal behaviour to large-scale problems such as climate change, pollution, or public health, the developed ecosystem provides the necessary tools to enable a greener and healthier lifestyle, improve quality of life, and contribute towards a more sustainable local environment.

This paper presents the concept of a participatory mobile app for citizens to assess health risks and exposure to air pollution in the city.

Head and Neck Cancer, the seventh cancer in incidence worldwide, is a heterogeneous disease that encompasses different molecular entities and subgroups with variable risk and potential discriminative treatment options that influence disease outcome. Treatment choice depends mainly on a staging system that has limits on advances cases (Stages III and IV). Because of that, it is needed to find more prognostic factors that can enhance this current classification. Population data contribute on the identification of risk and prognostic factors. Therefore, in the era of precision medicine, the integration of population data with patient clinical data is expected to contribute to a better patient stratification. In this work, carried out in the context of the European Research project “Big Data to Decide” (BD2Decide), the use of publicly available data sources to improve decision making in Head and Neck Cancer, and their integration in a computerized decision support system have been studied with the contribution of oncologists, epidemiologists and bio-statisticians. The conceptual framework design presented in this paper pretends to support the discovery of prognostic factors, improving risk stratification in Head and Neck Cancer.

Mild Cognitive Impairment (MICI) symptoms are one of the main issues that contribute, in older people, to the difficulty to live independently, social isolation and loss of autonomy. INFINITy solution provides a set of services aimed to reinforce and support the daily routines of people with MCI for both indoor and outdoor scenarios. A co-design session with end-users were performed in order to better adapt the INFINITy solution to the needs and characteristics of the target beneficiaries. Results show the feedback received form end-users regarding different aspects of the solution such as: functionalities, use cases, and interfaces. The results were useful to improve the INFINITy solution to better address user’s needs and preferences.

Parkinson’s is a complicated, chronic disease that most people live with for many years/decades. For this reason, a multidisciplinary disease management, involving several professions working together (neurologists, physiotherapists, speech and language therapists, occupational therapists, dieticians), is important to ensure that the patient retains his/her independence and continues to enjoy the best quality of life possible. To address these needs we describe an mhealth ecosystem for Parkinson’s disease (PD) management facilitating the collaboration of experts and empowering the patients to self-manage their condition.

This article describes the procedure of definition and design of a process for the continuity care unit to improve the attention to the patient and his/her ecosystem providing a novel alternative to the conventional methods. This work was done under the framework of the MiniQ project, funded by EIT Health to improve the management of poly-medicated patients.

© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated., Background: Depressive symptoms are common in individuals suffering from severe somatic conditions. There is a lack of interventions and evidence-based interventions aiming to reduce depressive symptoms in patients with severe somatic conditions. The aim of the NEVERMIND project is to address these issues and provide evidence by testing the NEVERMIND system, designed to reduce and prevent depressive symptoms in comparison to treatment as usual. Methods: The NEVERMIND study is a parallel-groups, pragmatic randomised controlled trial to assess the effectiveness of the NEVERMIND system in reducing depressive symptoms among individuals with severe somatic conditions. The NEVERMIND system comprises a smart shirt and a user interface, in the form of a mobile application. The system is a real-time decision support system, aiming to predict the severity and onset of depressive symptoms by modelling the well-being condition of patients based on physiological data, body movement, and the recurrence of social interactions. The study includes 330 patients who have a diagnosis of myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants are randomised in blocks of ten to either the NEVERMIND intervention or treatment as usual as the control group. Clinical interviews and structured questionnaires are administered at baseline, at 12 weeks, and 24 weeks to assess whether the NEVERMIND system is superior to treatment as usual. The endpoint of primary interest is Beck Depression Inventory II (BDI-II) at 12 weeks defined as (i) the severity of depressive symptoms as measured by the BDI-II. Secondary outcomes include prevention of the onset of depressive symptoms, changes in quality of life, perceived stigma, and self-efficacy. Discussion: There is a lack of evidence-based interventions aiming to reduce and prevent depressive symptoms in patients with severe somatic conditions. If the NEVERMIND system is effective, it will provide healthcare systems with a novel and innovative method to attend to depressive symptoms in patients with severe somatic conditions., The NEVERMIND project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 689691. The funding body had no role in the design of the study and collection as well as in writing the manuscript. Open access funding provided by Karolinska Institute.

[EN] Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care., The research leading to these results has received funding from the European Commission under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement no 600914.