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HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms.ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes.MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability.ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program.ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.

Stress resilience plays a key role in task performance during emergencies, especially in occupations like military special forces, with a routine consisting of unexpected events. Nevertheless, reliable and applicable measurements of resilience in predicting task performance in stressful conditions are still researched. This study aimed to explore the stress response in the Hellenic Navy SEALs (HN-SEALs), using a cognitive–physiological approach. Eighteen candidates under intense preparation for their enlistment in the HN-SEALs and 16 healthy controls (HCs) underwent Stroop tests, along with mental-state and personality examination. Simultaneously, electrodermal activity (EDA) was assessed during each one of cognitive testing procedures. Compared to healthy control values, multiple components of EDA values were found decreased (p &lt, 0.05) in the HN-SEALs group. These results were associated with an increase in resilience level in the HN-SEALs group, since a restricted sympathetic reactivity according to the reduced EDA values was observed during the stressful cognitive testing. This is the first report providing physiological measurements of the sympathetic response of HN-SEALs to a stressful situation and suggests that EDA turns out to be a simple and objective tool of sympathetic activation and it may be used as a complementary index of resilience in HN-SEALs candidates.

Internet of Things (IoT) is the logical further development of today's Internet, enabling a huge amount of devices to communicate, compute, sense and act. IoT sensors placed in Ambient Assisted Living (AAL) environments, enable the context awareness and allow the support of the elderly in their daily routines, ultimately allowing an independent and safe lifestyle. The vast amount of data that are generated and exchanged between the IoT nodes require innovative context modeling approaches that go beyond currently used models. Current paper presents and evaluates an open interoperable platform architecture in order to utilize the technical characteristics of IoT and handle the large amount of generated data, as a solution to the technical requirements of AAL applications.

Chronic diseases are the leading cause of mortality and morbidity. A significant contribution to the burden of chronic diseases is the concurrence of co-morbidities. Heart failure (HF) is a complex, chronic medical condition frequently associated with co-morbidities. The current care approach for HF patients with co-morbidities is neither capable to deliver personalised care nor to halt the on-going increase of its socio-economic burden. Our approach aims to improve the complete care process for HF patients and related co-morbidities to improve outcome and quality of life. This will be achieved by the proposed standardised yet personalised patient-oriented ICT system that supports evidence-based clinical decision making as well as interaction and communication between all stakeholders with focus on the patients and their relatives to improve self-management. We propose that such a system should be build upon a novel European-wide data standard for clinical input and outcome and that it should facilitate decision making and outcome tracking by new collective intelligence algorithms.

Ambient-assisted living (AAL) is currently one of the important research and development areas, where accessibility, usability, and learning play a major role and where future interfaces are an important concern for applied engineering. The general goal of AAL solutions is to apply ambient intelligence technology to enable people with specific demands, e.g., handicapped or elderly, to live in their preferred environment longer. The term “Internet of Things” (IoT) is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. In this context, we propose a new methodology based on self organizing maps (SOMs) and fuzzy C-means (FCM) algorithms for profile generation as regards the activities of the user and their correlation with the available sensors. Moreover, we utilize the provided context to assign the generated profiles to more contextually complex activities. Our methodology is being evaluated into an AAL structure equipped with several sensors. More precisely, we assess the proposed method in a data set generated by accelerometers and its performance over a number of everyday activities.

Personalized medicine (PM) is a rapidly growing field of healthcare and medicine. The advantage of a personalized medicine is the availability of each person’s unique genetic and genomic print. The healthcare that incorporates personalized medicine provides coordinated, continuous patient-specific data. The goal of personalized medicine is to promote health wellness, satisfaction, and to increase the likelihood of a successful disease prevention, detection and treatment. This form of medicine, apart from patient’s personal data and medicine-biological measurements, uses genomic information data to understand the molecular structure of the disease and to optimize health care strategies and drug therapies. Clinical trials that investigate personalized approaches are subject to special rules, for example, pertain the selection of participating patients. In personalized medicine, a certain genetic profile must be identified so that the treatment can work. This is why potential participants are first screened and selected accordingly for clinical trials. The special design of such clinical trials has an impact on the evaluation of data collected during the given study.

Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are two non-invasive techniques that are increasingly being used to identify and quantify biochemical markers associated with certain diseases, e.g., choline in the case of cancer. The associating of MRI/MRS images, patient’s electronic health record, genome information, and environmental factors increase the precision of diagnosis and treatment. The authors present a collaboration framework based on Cloud Computing which allows analysis of MRI/MRS data based on advanced mathematical tools, advanced combination, and link discovery between different data types, so as to increase the precision and consequently avoid non-appropriate therapy and treatment plans.

In most cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities present in cytology via the test Papanicolaou (Pap test). Although it is the sole test that has proved its value for cancer prevention, Pap test is prone to human errors as it is performed via the microscope and requires skilled personnel. Consequently, molecular techniques have emerged in the recent years, aiming to replace Pap test or support the diagnosis as ancillary tests. These include DNA micro-arrays identifying Human Papillomavirus (HPV) subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. These techniques are either highly sensitive or highly specific, but not both at the same time, thus no perfect method is available today. Moreover, most of the published studies compare two tests against each other, which, however, are using different population study groups, therefore it is questionable if the results can be used to perform a direct comparison between the available tests. In this paper, we adopt a sophisticated approach to assess the value of several well-known and widely applied CxCa tests in detecting high-grade cervical intraepithelial neoplasia and CxCa (CIN2+), by employing an Ensemble Feature Ranking framework. Applying the proposed framework we managed to directly compare and rank the studied CxCa tests and several HPV genotypes according to their efficacy in detecting CIN2+ lesions. The results suggest that HPV mRNA tests perform better than HPV DNA testing for the triage of abnormal Pap tests. Such findings may guide cytopathologists in a step by step basis to decide which tests to perform following an abnormal Pap test, in order to decrease uncertainty, improve accuracy and reduce time to diagnosis and resources.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.

Medical devices play a vital role in the delivery of high quality healthcare with their numbers increasing exponentially during the past fifty years. Although recent technological advancements have led to much more reliable and safer Medical Devices (MDs), potential risks of failure and the associated adverse incidents cannot be ignored. To create a safer environment for the patient in terms of technology, it is necessary to provide rapid and accurate information on adverse events, in order to avoid their repetition. The traditional approach of MD Vigilance based only on official user reports of adverse incidents has been proven inadequate today. A modern approach, also used in this work, to the problem is based on the introduction of additional means, such as data mining, extraction, standardization and codification of the information, from different direct and indirect sources worldwide, and its systematic classification and archiving in dedicated databases. The aim is to timely extract information on potentially hazardous MDs and to make it available where appropriate.

Falls are prominent among the causes of unintentional injury, particularly for the elderly, bringing serious socioeconomic impact to ageing societies. To address these challenges, ICT have recently demonstrated their potential in providing fall prevention solutions to the elderly. KINOPTIM project contributes towards this direction by developing a homecare solution that addresses falls in a preventive manner through training of seniors with the support of an interactive gaming platform. On top of the home care solution, KINOPTIM will implement a telemonitoring system that will follow the person's mobility status over time, and a Medical Business Intelligence (MBI) system will assist medical experts to diagnose impairments and related fall risks, to decide for proactive or active interventions. This paper presents the high level architecture of the KINOPTIM platform, comprising the game technologies as well as the sensing and decision support functionalities provided by the platform.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.

In the majority of cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities in the Pap test. Nowadays, there are ancillary molecular biology techniques providing important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA test, HPV mRNA tests and immunocytochemistry tests. However, these techniques have their own performance, advantages and limitations, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a risk assessment model based on a Bayesian Network which, by combining the results of Pap test and ancillary tests, may identify women at true risk of developing cervical cancer and support the management of patients with ASCUS or LSIL cytology. The model, following the paradigm of other implemented systems, can be integrated into existing platforms and be available on mobile terminals for anytime/anyplace medical consultation.

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.