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Internet is widely regarded as a necessary resource for economic and social development. The availability of this technology requires the deployment of ICT networks which have implications not only on the natural surroundings but also on the human environment. Understanding the impact generated on key areas of socio-ecological concern such as carbon footprint and employment generation is essential to ensure well-informed decision-making. This investigation utilized multiregional environmentally extended input-output (MRIO) methodology to evaluate the carbon footprint and the employment generation associated with providing internet to six geo-demographic scenarios, from urban to remote rural, using Peru as a reference site. The results showed that most of the carbon emissions (289-340 kg CO 2 eq./subs./yr.) and employment (7-8 jobs/subs./yr.) associated with larger ICT networks intended for urban and suburban areas was attributable to the manufacturing of end-user devices. The sustainability of smaller ICT networks for rural areas was dominated by the construction of shared infrastructures (mainly 4G LTE equipment), while the contribution of the energy consumed was in all cases limited. Although the analysis considered ICT networks deployed in Peru, most impacts were generated in China, where the manufacturing of the electronic components was assumed to take place. This analysis highlights the value of econometric methods for sustainability assessment such as MR-EEIO, which are complementary to the more conventional process-based E-LCA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Brigitte Banales reports financial support was provided by Meta Platforms Inc., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge., Objective: This work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology)., Methods: First, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians., Results: HeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management., Conclusion: HeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: In the process of growing societies, and especially in the digital era we live in, there is a need for a strong push for innovation that puts citizens at the center of the process from the beginning to build more resilient, cooperative and flexible communities. Different collaborative design approaches have emerged in recent decades, one of the most interesting being Living Labs, which involves user-centered design and co-creative innovation that bring together different actors and roles. However, although these new methodologies are harnessing creativity, some aspects of this new, more ecosystemic and complex vision are not clearly understood: possible barriers, how to facilitate local and operational solutions, overcoming institutional blockage, integrating new roles, etc., Methods: The incorporation of the Quintuple Helix as a driver to ensure greater coordinated participation of local actors has proven its usefulness and impact during the re-adaptation of LifeSpace (previously named Smart House Living Lab), managed by the Polytechnic University of Madrid (Spain), a transformation based on the experiences and lessons learned during the large-scale ACTIVAGE pilot funded by the European Commission, more specifically at the Madrid Deployment Site. It involved more than 350 older adult people and other stakeholders from different areas, including family members, formal and informal caregivers, hospital service managers, third-age associations, and public service providers, forming a sense of community, which was called MAHA., Results: The living lab infrastructure evolved from a single multi-purpose environment to incorporate three harmoniously competing environments: (1) THE LAB: Headquarters for planning, demonstration, initial design phases and entry point for newcomers to the process, (2) THE CLUB: Controlled interaction environment where returning users validate solutions, focusing mainly on AHA services (MAHA CLUB), such as exergames, social interaction applications, brain training activities, etc. (3) THE NEIGHBOURHOOD: Real-life environments for free and open interaction between actors and implementation of previously validated and tested solutions., Conclusion: The Quintuple Helix model applied in LifeSpace's new vision allows a coordinated involvement of a more diverse set of actors, beyond the end-users and especially those who are not traditionally part of research and innovation processes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Merino-Barbancho, Abril Jiménez, Mallo, Lombroni, Cea, López Nebreda, Cabrera, Fico and Arredondo.)

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., (©Jordi de Batlle, Ivan D Benítez, Anna Moncusí-Moix, Odysseas Androutsos, Rosana Angles Barbastro, Alessio Antonini, Eunate Arana, Maria Fernanda Cabrera-Umpierrez, Gloria Cea, George Ε Dafoulas, Frans Folkvord, Ane Fullaondo, Francesco Giuliani, Hsiao-Ling Huang, Pasquale F Innominato, Przemyslaw Kardas, Vivian W Q Lou, Yannis Manios, Maria Matsangidou, Franco Mercalli, Mounir Mokhtari, Silvio Pagliara, Julia Schellong, Lisa Stieler, Konstantinos Votis, Paula Currás, Maria Teresa Arredondo, Jorge Posada, Sergio Guillén, Leandro Pecchia, Ferran Barbé, Gerard Torres, Giuseppe Fico, the GATEKEEPER project. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.06.2023.)

Age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty, and cognitive impairment represent challenges for drug treatments. Moreover, older adults are commonly exposed to polypharmacy, leading to increased risk of drug interactions and related adverse events, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in poly-medicated older adults and assesses the usability and acceptability of the system in health care professionals, patients, and caregivers. The results of the study demonstrate that the miniQ system was useful for Primary Care physicians in significantly improving prescription, thereby reducing potentially inappropriate medication prescriptions for elderly patients. Additionally, the system was found to be beneficial for patients and their caregivers in understanding their medications, as well as usable and acceptable among healthcare professionals, patients, and caregivers, highlighting the potential to improve the prescription process and reduce errors, and enhancing the quality of care for elderly patients with polypharmacy, reducing adverse drug events, and improving medication management., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children., Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices., Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective., Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement-This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.

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., Competing Interests: Declaration of competing interest All author reports financial support was provided by Facebook Connectivity Laboratory., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress., Competing Interests: Author JG-R was employed by Omya Clariana S.L.U. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 García Pagès, Arza, Lazaro, Puig, Castro, Ottaviano, Arredondo, Bernal, López-Antón, Cámara, Gil, Laguna, Bailón, Aguiló and Garzón-Rey.)

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.

Head and neck cancer is the seventh most common cancer worldwide. The incidence of this cancer is increasing, but at the same time, the cancer-related mortality rate has decreased over time, leaving more head and neck cancer survivors. More emphasis is needed on quality-of-life research in the head and neck cancer field to improve their daily lives and reduce the disease and treatment response burden. To achieve this, we conducted a scoping review to find and learn which predictors and data analysis techniques have been used in previous studies. This work is undertaken in the context of the BD4QoL EU Research project.

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.

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., (© The Author(s) 2020.)

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.

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.

Phytophthora cinnamomi , responsible for "root rot" or "dieback" plant disease, causes a significant amount of economic and environmental impact. In this work, the fungicide action of nanocomposites based on silver nanoparticles and polyphenol inclusion compounds, which feature enhanced bioavailability and water solubility, was assayed for the control of this soil-borne water mold. Inclusion compounds were prepared by an aqueous two-phase system separation method through extraction, either in an hydroalcoholic solution with chitosan oligomers (COS) or in a choline chloride:urea:glycerol deep eutectic solvent (DES). The new inclusion compounds were synthesized from stevioside and various polyphenols (gallic acid, silymarin, ferulic acid and curcumin), in a [6:1] ratio in the COS medium and in a [3:1] ratio in the DES medium, respectively. Their in vitro response against Phytophthora cinnamomi isolate MYC43 (at concentrations of 125, 250 and 500 µg·mL -1 ) was tested, which found a significant mycelial growth inhibition, particularly high for the composites prepared using DES. Therefore, these nanocomposites hold promise as an alternative to fosetyl-Al and metalaxyl conventional systemic fungicides.

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.

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 < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center., Conclusion: Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

This article presents, in the form of an analytic narrative review, a complete picture of the state-of-the-art, challenges, and perspectives in the field of information and communication technology (ICT)-based neurocognitive interventions for older adults. The narrative particularly focuses on applications aimed at mild cognitive impairment and similar age-related cognitive deficits, which are analyzed in the context of the brain training controversy. Clarifying considerations are provided about the nature and present extent of the brain training debate, regarding the possible influence it has on the support received by research and development initiatives dealing with innovative computerized neurocognitive interventions. It is recommended that, because of the preliminary nature of most data currently available in this area, further research initiatives must be supported in the quest for better effectiveness of computer-based interventions intended for age-related cognitive impairment. The conclusion suggests that advanced ICT-based tools, such as virtual and augmented reality technologies, are the most fitting platforms for applying nonpharmacological computerized neurocognitive interventions.

PD&#95;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.

The Strategic Implementation Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) proposed six Action Groups. After almost three years of activity, many achievements have been obtained through commitments or collaborative work of the Action Groups. However, they have often worked in silos and, consequently, synergies between Action Groups have been proposed to strengthen the triple win of the EIP on AHA. The paper presents the methodology and current status of the Task Force on EIP on AHA synergies. Synergies are in line with the Action Groups' new Renovated Action Plan (2016-2018) to ensure that their future objectives are coherent and fully connected. The outcomes and impact of synergies are using the Monitoring and Assessment Framework for the EIP on AHA (MAFEIP). Eight proposals for synergies have been approved by the Task Force: Five cross-cutting synergies which can be used for all current and future synergies as they consider overarching domains (appropriate polypharmacy, citizen empowerment, teaching and coaching on AHA, deployment of synergies to EU regions, Responsible Research and Innovation), and three cross-cutting synergies focussing on current Action Group activities (falls, frailty, integrated care and chronic respiratory diseases).

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

Despite the speedy evolution of Information and Computer Technology (ICT), and the growing recognition of the importance of the concept of universal design in all domains of daily living, mainstream ICT-based product designers and developers still work without any truly structured tools, guidance or support to effectively adapt their products and services to users' real needs. This paper presents the approach used to define and evaluate parametric cognitive models that describe interaction and usage of ICT by people with aging- and disability-derived functional impairments. A multisensorial training platform was used to train, based on real user measurements in real conditions, the virtual parameterized user models that act as subjects of the test-bed during all stages of simulated disabilities-friendly ICT-based products design. An analytical study was carried out to identify the relevant cognitive functions involved, together with their corresponding parameters as related to aging- and disability-derived functional impairments. Evaluation of the final cognitive virtual user models in a real application has confirmed that the use of these models produce concrete valuable benefits to the design and testing process of accessible ICT-based applications and services. Parameterization of cognitive virtual user models allows incorporating cognitive and perceptual aspects during the design process.

The availability of new tools able to support patient monitoring and personalized care may substantially improve the quality of chronic disease management. A personalized healthcare pathway (PHP) has been developed for diabetes disease management and integrated into an information and communication technology system to accomplish a shift from organization-centered care to patient-centered care. A small-scale exploratory study was conducted to test the platform. Preliminary results are presented that shed light on how the PHP influences system usage and performance outcomes.

The present work presents the comparative assessment of four glucose prediction models for patients with type 1 diabetes mellitus (T1DM) using data from sensors monitoring blood glucose concentration. The four models are based on a feedforward neural network (FNN), a self-organizing map (SOM), a neuro-fuzzy network with wavelets as activation functions (WFNN), and a linear regression model (LRM), respectively. For the development and evaluation of the models, data from 10 patients with T1DM for a 6-day observation period have been used. The models' predictive performance is evaluated considering a 30-, 60- and 120-min prediction horizon, using both mathematical and clinical criteria. Furthermore, the addition of input data from sensors monitoring physical activity is considered and its effect on the models' predictive performance is investigated. The continuous glucose-error grid analysis indicates that the models' predictive performance benefits mainly in the hypoglycemic range when additional information related to physical activity is fed into the models. The obtained results demonstrate the superiority of SOM over FNN, WFNN, and LRM with SOM leading to better predictive performance in terms of both mathematical and clinical evaluation criteria.

Mobile health systems aiming to promote adherence may cost-effectively improve the self-management of chronic diseases like diabetes, enhancing the compliance to the medical prescription, encouraging and stimulating patients to adopt healthy life styles and promoting empowerment. This paper presents a strategy for m-health applications in diabetes self-management that is based on automatic generation of feedback messages. A feedback assistant, representing the core of architecture, delivers dynamic and automatically updated text messages set up on clinical guideline and patient's lifestyle. Based on this strategy, an m-health adherence system was designed, developed and tested in a small-scale exploratory study with T1DM and T2DM patients. The results indicate that the system could be feasible and well accepted and that its usage increased along with adherence to prescriptions during the 4 weeks of the study. A more extensive research is pending to corroborate these outcomes and to establish a clear benefit of the proposed solution.

Successful management of health conditions in older population is determined by strategic involvement of a professional team of careers and by empowering patients and their caregivers to take over a central role and responsibility in the daily management of condition. Identifying, structuring and ranking the most important needs related to these aspects could pave the way for improved strategies in designing systems and technological solutions supporting user empowerment. This paper presents the preliminary results of a study aiming to elicit these needs. Healthcare professionals, working together in the European and Innovation Partnership on Active and Healthy Ageing (EIP-AHA) initiative, have defined a set of needs and factors that have been organized in two hierarchies around the concepts of patient activation and proactive and prepared care team, defined in the Chronic Care Model. The two hierarchies have been mapped, by a team of experts in computer science, with technologies and solutions that could facilitate the achievement of the identified needs.

Background: Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D)., Methods: A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance., Results: A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D., Conclusions: Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease., (© 2015 Diabetes Technology Society.)

There is a pressing demand for improving the quality and efficacy of health care and social support services needed by the world's growing elderly population, especially by those affected by mild cognitive impairment (MCI) and Alzheimer's disease (AD)-type early-stage dementia. Meeting that demand can significantly benefit from the deployment of innovative, computer-based applications capable of addressing specific needs, particularly in the area of cognitive impairment mitigation and rehabilitation. In that context, we present here our perspective viewpoint on the use of virtual reality (VR) tools for cognitive rehabilitation training, intended to assist medical personnel, health care workers, and other caregivers in improving the quality of daily life activities of people with MCI and AD. We discuss some effective design criteria and developmental strategies and suggest some possibly useful protocols and procedures. The particular innovative supportive advantages offered by the immersive interactive characteristics inherent to VR technology are discussed., (© The Author(s) 2014.)

One of the most important challenges of designing eHealth tools for Chronic Disease Management is to understand how transforming cutting-edge innovations in something that can impact the current clinical practice and improve the performance of the health care systems. The adoption of User Centered Design techniques is fundamental in order to integrate these systems in an effective and successful way. The work presented in this paper describe the methodologies used in the context of two multidisciplinary research projects, METABO and MOSAIC. The adoption of the methodologies have been driven by the CeHRes Roamap, a holistic framework that support participatory development of eHealth. The work reported in this paper describes the results of the first two (out of the five) phases in eliciting user needs.

This chapter provides an overview of how healthcare institution could benefit from the usage of technologies and personal health systems. Clinical, Usage and Technical data are mined in different ways and with different methods to support users (patients, health professionals and informal caregivers) in taking decisions. As a case study, the solutions and the techniques adopted in a research project focused on the delivery of technologies to improve diabetes management are described.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson's disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance.

The integration of rehabilitation systems in an ambient assisted living environment can provide a powerful and versatile tool for long-term stroke rehabilitation goals. This paper introduces a novel concept of a personalized cognitive rehabilitation system in a naturalistic setting. The proposed platform was developed within the CogWatch project, with the intent of fostering independence in activities of daily living in patients with apraxia and action disorganization syndrome. Technical usability was evaluated in a series of pilot experiments, which illustrate how this approach may help to retrain patients in activities of daily living. The first system prototype has been tested with 36 participants divided into three groups, providing an exploratory evaluation of the usability of this solution and its acceptability. The technical solutions used within the CogWatch project are targeted to meet both the end users' needs from the interaction and usability point of views and the clinical requirements associated with the use of such systems. The challenges behind the development of ambient assisted living systems for cognitive rehabilitation are discussed.

Ambient assisted living (AAL) is a complex field, where different technologies are integrated to offer solutions for the benefit of different stakeholders. Several evaluation techniques are commonly applied that tackle specific aspects of AAL; however, holistic evaluation approaches are lacking when addressing the needs of both developers and end-users. Living labs have been often used as real-life test and experimentation environments for co-designing AAL technologies and validating them with relevant stakeholders. During the last five years, we have been evaluating AAL systems and services in the framework of various research projects. This paper presents the lessons learned in this experience and proposes a set of harmonized guidelines to conduct evaluations in living labs.

Parkinson's disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments.

Recent works have proved that Parkinson's disease (PD) patients can be largely benefit by performing rehabilitation exercises based on audio cueing and music therapy. Specially, gait can benefit from repetitive sessions of exercises using auditory cues. Nevertheless, all the experiments are based on the use of a metronome as auditory stimuli. Within this work, Human-Computer Interaction methodologies have been used to design new cues that could benefit the long-term engagement of PD patients in these repetitive routines. The study has been also extended to commercial music and musical pieces by analyzing features and characteristics that could benefit the engagement of PD patients to rehabilitation tasks.

It has been proved that audio and visual cueing can improve the motor performance of Parkinson's disease patients. Specially, gait can benefit from repetitive sessions of exercises using cues. Nevertheless, these effects are not permanent and fade away with time, in that sense, home game systems can be an excellent platform for patients to perform daily exercises, as well as to coach and guide them in a smarter way. Within this work a method to track the walking movement is proposed based on the signals coming from the Kinect sensor of Microsoft. At the same time, different setups have been tested in order to study the feasibility of using this sensor to build a game platform for gait rehabilitation for Parkinson's disease patients.

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7%) and worst (87%) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

Cardiovascular Diseases are the most prevalent and serious chronic conditions existing nowadays. They are the primary cause of death in the world and generate enormous expenditures to the health systems. Tele-monitoring and personal health systems have proven to be good options for tackling this situation; however they are still lacking many functionalities. It is necessary to find solutions that allow health professionals to follow up patients more closely and efficiently, while reducing the non-adherence of patients to the treatment regime. HeartCycle research project (partially funded by the European Commission) has developed a personal health system for cardiovascular diseases management with the aim to address this problem. This paper describes the Patient Loop of this solution, including the different components, the adopted user interaction, and the implemented patients' education and coaching strategy.

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson's disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson's disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

The work presented in this paper comprises the methodology and results of a pilot study on the feasibility of a wireless health monitoring system designed under main EU challenges for the promotion of healthy and active ageing. The system is focused on health assessment, prevention and lifestyle promotion of elderly people. Over a hundred participants including elderly users and caregivers tested the system in four pilot sites across Europe. Tests covered several scenarios in senior centers and real home environments, including performance and usability assessment. Results indicated strong satisfactoriness on usability, usefulness and user friendliness, and the acceptable level of reliability obtained supports future investigation on the same direction for further improvement and transfer of conclusions to the real world in the healthcare delivery.

In a nonstrenuous exercise, the heart rate (HR) shows a linear relationship with the maximum volume of oxygen consumption VO(2Max) and serves as an indicator of performance of the cardiovascular system. The HR replaces the %VO(2Max) in exercise program prescription to improve aerobic endurance. In order to achieve an optimal effect in an endurance training, the athlete needs to work out at an HR high enough to trigger the aerobic metabolism, while avoiding the very high HRs that bring along significant risks of myocardial infarction. The minimal and optimal base training programs, followed by stretching exercises to prevent injuries, are adequate programs to maximize benefits and minimize health risks for the cardiovascular system during single session training. In this paper, we have defined a functional model for an ambient intelligence system that monitors, evaluates, and trains the aerobic endurance. It is based on the Android operating system and the Gow Running smart shirt. The system has been evaluated during functional assessment stress testing of aerobic endurance in the Stress Physiology Laboratory (SPL) of the Technical University of Madrid. Furthermore, a voice system designed to guide the user through minimal and optimal base training programs has been evaluated. The results obtained fully confirm the model with a high correlation between the data collected by the system and the by SPL. There is also a high hit rate between training sessions of the users and the objective training functions defined in the training programs.

Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3 ± 8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4 ± 14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.

This paper describes the design and development of a system for cardio rehabilitation of patients that suffered a myocardial infarction. The proposed solution focuses on exercise prescriptions and the encouragement of healthy behaviors. The innovative strategy of the design takes into account health promotion models to provide safe, assistive exercise training sessions, personalized feedbacks, and educational contents.