The WHO atlas for female-genital schistosomiasis: Co-design of a practicable diagnostic guide, digital support and training.

Up to 56 million young and adult women of African origin suffer from Female Genital Schistosomiasis (FGS). The transmission of schistosomiasis happens through contact with schistosomiasis infested fresh water in rivers and lakes. The transmission vector is the snail that releases immature worms capable of penetrating the human skin. The worm then matures and mates in the blood vessels and deposits its eggs in tissues, causing urogenital disease. There is currently no gold standard for FGS diagnosis. Reliable diagnostics are challenging due to the lack of appropriate instruments and clinical skills. The World Health Organisation (WHO) recommends "screen-and-treat" cervical cancer management, by means of visual inspection of characteristic lesions on the cervix and point-of-care treatment as per the findings. FGS may be mistaken for cervical cancer or sexually transmitted diseases. Misdiagnosis may lead to the wrong treatment, increased risk of exposure to other infectious diseases (human immunodeficiency virus and human papilloma virus), infertility and stigmatisation. The necessary clinical knowledge is only available to a few experts in the world. For an appropriate diagnosis, this knowledge needs to be transferred to health professionals who have minimal or non-existing laboratory support. Co-design workshops were held with stakeholders (WHO representative, national health authority, FGS experts and researchers, gynaecologists, nurses, medical doctors, public health experts, technical experts, and members of the public) to make prototypes for the WHO Pocket Atlas for FGS, a mobile diagnostic support tool and an e-learning tool for health professionals. The dissemination targeted health facilities, including remote areas across the 51 anglophone, francophone and lusophone African countries. Outcomes were endorsed by the WHO and comprise a practical diagnostic guide for FGS in low-resource environments.

Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study.

The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30-45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.

Virtual prenatal care: A systematic review of pregnant women's and healthcare professionals' experiences, needs, and preferences for quality care.

INTRODUCTION: Exploitation of telehealth in prenatal care has the potential to reduce the access barrier to care and empower women to participate in their own care. This review aims to assess the practical implications of virtual prenatal care and identify the needs and experiences associated with it. METHODS: A systematic literature review was conducted in four electronic databases: PubMed, Web of Science, Scopus, and Cochrane. The keywords used were "pregnancy", "virtual visit", "prenatal", and others. The search included all relevant studies published from 2011 to 2021 written in English. Articles mentioning virtual prenatal care incorporating synchronous communication between pregnant women and health care professionals were included. Those unrelated to prenatal care or employing asynchronous means of virtual care were excluded. The review was structured following the PRISMA guidelines. Different quality appraisal methods such as JBI, CASP, NOS, and Cochrane were used to assess the methodological quality of the literature. The data were then analyzed based on the categorization of the studies. RESULTS: Overall, 2863 articles were identified, of which 19 met the inclusion criteria after removing duplicates, screening of abstracts, and full text-four articles identified from hand-searching were incorporated, making a total of 23 eligible articles for the review. The studies' findings revealed the preference for implementing cost-effective virtual care based on the resource set, technological literacy, and consistent accessibility. Further, no significant differences in clinical outcomes were observed between two modes of care, virtual and in-person. The higher satisfaction by pregnant women and healthcare professionals indicated the continuity of the care. In addition, the hybrid model of virtual prenatal care integrated with traditional in-person care was acceptable to both low-risk and high-risk pregnant women. Virtual prenatal care substantially reduced travel time and absences from work, drops in clinic wait time and no-show rate, limited the risk of exposure during a pandemic, and increased self-accountability. CONCLUSION: Virtual prenatal care offers predominant advantages over in-person when it is carefully designed with the inclusion of pregnant women and healthcare professionals' needs. Evidence showed that providing adequate technology training, proper instruction, and guidelines for initial setup and assurance of a reliable and accessible system is vital in increasing access to care.

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations.

BACKGROUND: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. METHODS: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting different phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user's context, specifying user requirements, designing and developing an initial low-fidelity eCoach prototype. In design workshop 2, we focused on maturing the low-fidelity solution design and development for the visualization of continuous and discrete data, artificial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. RESULTS: The iterative design process helped to develop a working prototype of eCoach system that meets end-user's requirements and expectations towards an effective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the "Google Material Design" guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. CONCLUSIONS: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working research prototype to improve the fit between technology, end-user, and researchers. Furthermore, we performed a technological readiness study of ProHealth eCoach against standard levels set by European Union (EU).

Digital Interventions on Healthy Lifestyle Management: Systematic Review.

BACKGROUND: Digital interventions have tremendous potential to improve well-being and health care conveyance by improving adequacy, proficiency, availability, and personalization. They have gained acknowledgment in interventions for the management of a healthy lifestyle. Therefore, we are reviewing existing conceptual frameworks, digital intervention approaches, and associated methods to identify the impact of digital intervention on adopting a healthier lifestyle. OBJECTIVE: This study aims to evaluate the impact of digital interventions on weight management in maintaining a healthy lifestyle (eg, regular physical activity, healthy habits, and proper dietary patterns). METHODS: We conducted a systematic literature review to search the scientific databases (Nature, SpringerLink, Elsevier, IEEE Xplore, and PubMed) that included digital interventions on healthy lifestyle, focusing on preventing obesity and being overweight as a prime objective. Peer-reviewed articles published between 2015 and 2020 were included. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a framework for an evidence-based systematic review. Furthermore, we improved the review process by adopting the Rayyan tool and the Scale for the Assessment of Narrative Review Articles. RESULTS: Our initial searches identified 780 potential studies through electronic and manual searches; however, 107 articles in the final stage were cited following the specified inclusion and exclusion criteria. The identified methods for a successful digital intervention to promote a healthy lifestyle are self-monitoring, self-motivation, goal setting, personalized feedback, participant engagement, psychological empowerment, persuasion, digital literacy, efficacy, and credibility. In this study, we identified existing conceptual frameworks for digital interventions, different approaches to provide digital interventions, associated methods, and execution challenges and their impact on the promotion of healthy lifestyle management. CONCLUSIONS: This systematic literature review selected intervention principles (rules), theories, design features, ways to determine efficient interventions, and weaknesses in healthy lifestyle management from established digital intervention approaches. The results help us understand how digital interventions influence lifestyle management and overcome the existing shortcomings. It serves as a basis for further research with a focus on designing, developing, testing, and evaluating the generation of personalized lifestyle recommendations as a part of digital health interventions.

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study.

BACKGROUND: Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This "proof-of-concept" study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. OBJECTIVE: The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. METHODS: We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of "Semantic Sensor Network Ontology" and "Systematized Nomenclature of Medicine-Clinical Terms" to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based "Jena Framework" (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the "HermiT 1.4.3.x" ontology reasoner available in Protégé 5.x. RESULTS: The proposed ontology has been implemented for the study case "obesity." However, it can be extended further to other lifestyle diseases. "UiA eHealth Ontology" has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with "Owl Viz," and the formal representation has been used to infer a participant's health status using the "HermiT" reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. CONCLUSIONS: This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review.

BACKGROUND: We systematically reviewed the literature on human coaching to identify different coaching processes as behavioral interventions and methods within those processes. We then reviewed how those identified coaching processes and the used methods can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technology (ICT). OBJECTIVE: This study aimed to identify coaching and eCoaching processes as behavioral interventions and the methods behind these processes. Here, we mainly looked at processes (and corresponding models that describe coaching as certain processes) and the methods that were used within the different processes. Several methods will be part of multiple processes. Certain processes (or the corresponding models) will be applicable for both human coaching and eCoaching. METHODS: We performed a systematic literature review to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019) and persuasive eCoaching as behavioral interventions for a healthy lifestyle (from 2014 to 2019). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used for the evidence-based systematic review and meta-analysis. RESULTS: The systematic search resulted in 79 publications, including 72 papers and seven books. Of these, 53 were related to behavioral interventions by eCoaching and the remaining 26 were related to human coaching. The most utilized persuasive eCoaching methods were personalization (n=19), interaction and cocreation (n=17), technology adoption for behavior change (n= 17), goal setting and evaluation (n=16), persuasion (n=15), automation (n=14), and lifestyle change (n=14). The most relevant methods for human coaching were behavior (n=23), methodology (n=10), psychology (n=9), and mentoring (n=6). Here, "n" signifies the total number of articles where the respective method was identified. In this study, we focused on different coaching methods to understand the psychology, behavioral science, coaching philosophy, and essential coaching processes for effective coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. We identified that knowledge, coaching skills, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation of coaching processes are relevant for eCoaching. CONCLUSIONS: This systematic literature review selected processes, associated methods, strengths, and limitations for behavioral interventions from established coaching models. The identified methods of coaching point toward integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome the existing limitations of human coaching.

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death.

"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and "obesity/overweight" is one of the consequences. "Obesity and overweight" are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The "World Health Organization" (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in "Kaggle" and "University of California, Irvine (UCI) database", and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case in the future? (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

[Morbidity and Mortality of Single Incision Cholecystectomy: Results of a 3-Year Register Analysis]. / Morbidität und Letalität der Single-Incision-Cholezystektomie: eine 3-Jahres-Registeranalyse.

INTRODUCTION: Two decades ago, single-incision surgery was established as a new concept in minimally invasive surgery. Single incision cholecystectomy is the most frequently performed procedure in clinical routine. Most of the results have been based on randomised trials. Large prospective multicentre observational datasets from clinical routine do not exist. This analysis of clinical health service research is based on the SILAP study (single-incision multiport/single port laparoscopic abdominal surgery study). PATIENTS AND METHODS: The data of the register were collected in 47 hospitals in the period of 2012 to 2014. Overall morbidity and mortality were the primary outcome. Multiple linear and logistic regression analyses were performed. Statistical significance was set at p < 0.05. RESULTS: Data from 975 patients in clinical routine with single incision cholecystectomy were collected. Intraoperative complications were recorded in 3.2% of cases. Bile duct injuries were registered in 0.1% of cases. Postoperative complications were detected in 3.7% of cases. The mortality rate was 0.2%.The median operating time dropped from 60.0 to 51.5 min (p < 0.001) during the study. The use of an extra trocar was necessary in 10.3% of cases. Conversion to open surgery was performed in 0.7% of cases. Body mass index (p = 0.024), male gender (p = 0.012) and operating time (p < 0.001) had a significant effect on intraoperative complications in multivariate analysis. Classification of ASA III (p = 0.001) and modification or conversion of single incision technique (p = 0.001) were significantly associated with postoperative complications. CONCLUSION: The register analysis of the prospective multicentre data shows that single incision cholecystectomy is feasible in clinical routine even outside the selective criteria of randomised studies. The only limitation is a BMI > 30 kg/m2 which has a significant influence on the intraoperative rate of complications in mild adverse events.

The EU project "United4Health": Results and experiences from automatic health status assessment in a Norwegian telemedicine trial system.

INTRODUCTION: Patients with chronic obstructive pulmonary disease require help in daily life situations to increase their individual perception of security, especially under worsened medical conditions. Unnecessary hospital (re-)admissions and home visits by doctors or nurses shall be avoided. This study evaluates the results from a two-year telemedicine field trial for automatic health status assessment based on remote monitoring and analysis of a long time series of vital signs data from patients at home over periods of weeks or months. METHODS: After discharge from hospital treatment for acute exacerbations, 94 patients were recruited for follow-up by the trial system. The system supported daily measurements of pulse and transdermal peripheral capillary oxygen saturation at patients' homes, a symptom-specific questionnaire, and provided nurses trained to use telemedicine ("telenurses") with an automatically generated health status overview of all monitored patients. A colour code (green/yellow/red) indicated whether the patient was stable or had a notable deterioration, while red alerts highlighted those in most urgent need of follow-up. The telenurses could manually overwrite the status level based on the patients' conditions observed through video consultation. RESULTS: Health status evaluation in 4970 telemonitor datasets were assessed retrospectively. The automatic health status determination (subgroup of 33 patients) showed green status at 46% of the days during a one-month monitoring period, 28% yellow status, and 19% red status (no data reported at 7% of the days). The telenurses manually downrated approximately 10% of the red or yellow alerts. DISCUSSION: The evaluation of the defined real-time health status assessment algorithms, which involve static rules with personally adapted elements, shows limitations to adapt long-term home monitoring with adequate interpretation of day-to-day changes in the patient's condition. Thus, due to the given sensitivity and specificity of such algorithms, it seems challenging to avoid false high alerts.

Curriculum Development of a Health Informatics Master Course in Understanding Technology.

Education in health informatics is an important contribution to the digital transformation in health care services, and there is a need to combine health and technology competencies. Traditional ways with separation of health sciences and computer science have shortcomings, and there is need for individuals with a holistic view and relevant education background for improving the technology-supported clinical work processes. This paper presents how the curriculum was organised in a course in "Understanding Technology", which was a part of a Master's Programme in Health Informatics. The course targeted different aspects of technology for students with a diverse background, and it was organised with the aim to provide the students with theoretical technical insights and apply the knowledge through problem-solving and practical use. The course was decentralised and had a student-centred teaching approach over one semester.

Warfarin Guide: Co-Design of a Mobile Computer-Assisted Anticoagulant Therapy.

Patients with some types of cardiovascular disease are prescribed anticoagulation therapy with Warfarin in order to control the ability of blood clotting. This work presents a co-designed mobile application, called Warfarin Guide, for a computer-assisted anticoagulant therapy. The application addresses the challenges that unexperienced patients may find when having to remember to regularly check their INR values and make temporary adjustments for INR value fluctuations that are not easy to interpret without direct medical advice.

The EU-project United4Health: User-centred design of an information system for a Norwegian telemedicine service.

INTRODUCTION: Organizational changes of health care services in Norway brought to light a need for new clinical pathways. This study presents the design and evaluation of an information system for a new telemedicine service for chronic obstructive pulmonary disease patients after hospital discharge. METHODS: A user-centred design approach was employed composed of a workshop with end-users, two user tests and a field trial. For data collection, qualitative methods such as observations, semi-structured interviews and a questionnaire were used. RESULTS: User workshop's outcome informed the implementation of the system initial prototype, evaluated by end-users in a usability laboratory. Several usability and functionality issues were identified and solved, such as the interface between the initial colour scheme and the triage colours. Iterative refinements were made and a second user evaluation showed that the main issues were solved. The responses to a questionnaire presented a high score of user satisfaction. In the final phase, a field trial showed satisfactory use of the system. DISCUSSION: This study showed how the target end-users groups were actively involved in identifying the needs, suggestions and preferences. These aspects were addressed in the development of an information system through a user-centred design process. The process efficiently enabled users to give feedback about design and functionality. Continuous refinement of the system was the key to full development and suitability for the telemedicine service. This research was a result of the international cooperation between partners within the project United4Health, a part of the Seventh Framework Programme for Research of the European Union.

Future Telehealth and Telecare Reference Design based on IoT Technologies: From Remote Monitoring to Smart Collaborative Services with Decision Support.

The demographic changes are producing aging societies across the world, resulting in greater demands on the health and care systems due to age-related disabilities and chronic diseases. Efficient telehealth and telecare services are needed to control the corresponding expenditures, by supporting increased collaboration between different professional and involving informal health care providers, and by empowering the patients to manage their health and well-being. Emerging trial systems for remote patient monitoring present preliminary solutions not exempt of certain limitations. We propose a future eHealth reference system architecture and core components, aiming at secure, smarter and more collaborative telehealth and telecare services. The implicit cooperation between the so-far separated domains of consumer well-being services and public telehealth and telecare services will be beneficial for all parties.

The EU-project United4Health: User-Centred Design and Evaluation of a Collaborative Information System for a Norwegian Telehealth Service.

This study presents the user-centred design and evaluation process of a Collaborative Information System (CIS), developed for a new telehealth service for remote monitoring of chronic obstructive pulmonary disease patients after hospital discharge. The CIS was designed based on the information gathered in a workshop, where target end-users described the context of use, a telehealth workflow and their preferred ways of interaction with the solution. Evaluation of the iterative refinements were made through user tests, semi-structured interviews and a questionnaire. A field trial reported results on the ease of use and user satisfaction during the interaction with the fully developed system. The implemented CIS was successfully deployed within the secured Norwegian Health Network. The research was a result of cooperation between international partners within the EU FP7 project United4Health.

Telemedicine and Cooperative Remote Healthcare Services: COPD Field Trial.

The introduction of sustainable telemedicine solutions throughout Europe requires the development of secure, flexible and expandable systems and the evaluation of their operation in real-world settings such as field trials. This paper describes a system for a remote monitoring and care support field trial with Chronic Obstructive Pulmonary Disease (COPD) patients. By following a user-centred-development and Privacy by Design approach, the needs of all involved user groups could be addressed, while fulfilling, at the same time, national requirements with emphasis in security and privacy protection. The solution covers specific applications and services for COPD patients and their remote care takers, but allows the generalization of its applicability to other patient groups.

Usability Evaluation of a COPD Remote Monitoring Application.

Telemedicine applications have the potential to enhance patient's safety at home by remote monitoring of chronic diseases. Telemedicine involves the interaction between multiple user groups through a system, making the usability aspect of such system crucial for the continuous, efficient and satisfactory use of the application. The main objective of this study was to carry out a usability evaluation in the field of a telemedicine application for remote monitoring of chronic obstructive pulmonary disease (COPD) patients to improve the application's user interface before system deployment. A field trial was performed with six COPD patients at their homes, continuously using the system's application on a tablet for seven days. The usability evaluation identified 23 usability problems related to users' interactions and system's functionality. These problems were solved with the refinement of the system through an iterative application development process. The outcome of the study was the improved telemedicine application that was adopted by the partners of the FP7 EU project United4Health.