Ebook: pHealth 2022

pHealth 2022 is the 19th conference in a series of scientific events which brings together expertise from medical, technological, political, administrative, and social domains, and even from philosophy and linguistics. It opens a new chapter in the success story of this series of international conferences on wearable or implantable micro and nano technologies for personalised medicine.

Started in 2003 as a dissemination activity in the framework of a European project on wearable micro and nano technologies for personalised health with personal health management systems, pHealth conferences have evolved to become truly interdisciplinary and global events. All aspects of pHealth are comprehensively represented in the conference series, which also covers technological and biomedical facilities, legal, ethical, social, and organisational requirements and impacts, as well as the basic research necessary to enable future-proof care paradigms. It has advanced from P medicine as personalised medicine through P2 medicine, which also addresses prevention, P3 medicine with the inclusion of prediction, P4 medicine, where the patient is included as an active participant in the process, up to the current P5 medicine: personalised, participative, preventive, predictive, precision medicine. In that context, the conference series attracts experts from many scientific domains, including mathematics, data sciences, system sciences, philosophy, ethics and social sciences, as well as developers and practitioners from various technologies, medical and health disciplines, legal affairs, politics, and administration from all over the world. The 2022 conference brought together health-service vendor and provider institutions, payer organisations, government departments, academic institutions, professional bodies, as well as patients and citizen representatives.

Smart mobile systems, such as microsystems, smart textiles, smart implants, sensor-controlled medical devices, and innovative sensor and actuator principles and techniques, as well as related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and ubiquitous pervasive health as the next-generation health services, while social media and gamification have added another dimension to pHealth as an eco-system.

The OECD has defined four basic areas which must be managed in the new care model: address the big data challenges; foster meaningful innovation; understand and address the potential new risks; and support concerted effort to un-silo communities for a virtual care future. The benefits of pHealth technologies for all stakeholder communities, including patients, citizens, health professionals, politicians, healthcare establishments, and companies from biomedical technology, pharmaceutical, and telecommunications domains, offers enormous potential, not only for the improvement of medical quality and industrial competitiveness, but also for managing healthcare costs.

The pHealth 2022 conference benefits from the experience and lessons learned by the organising committees of previous pHealth events, particularly 2009 in Oslo, 2010 in Berlin, 2011 in Lyon, 2012 in Porto, 2013 in Tallinn, 2014 in Vienna, 2015 in Västerås, 2016 in Heraklion, 2017 in Eindhoven, 2018 in Gjøvik, 2019 in Genoa, 2020 in Prague, and 2021, again in Genoa. The 2009 conference introduced the idea of having special sessions focusing on a particular topic and organised by a mentor or moderator. The Berlin event in 2010 initiated pre-conference workshops on particular topics prior to the main event. Lyon, in 2011, launched so-called dynamic demonstrations, allowing participants to show software and hardware solutions on the fly without the need for a booth. Implementing pre-conference events, pHealth 2012 in Porto gave attendees a platform for presenting and discussing recent developments and provocative ideas, which helped to animate the sessions. The highlights of pHealth 2013 in Tallinn were the special session on the success stories of European projects, and also the presentations on the newest paradigm changes and challenges associated with Big Data, analytics, translational and nano medicine, etc. Vienna in 2014 focused on lessons learned from international and national R&D activities, and practical solutions, particularly those from Horizon 2020, the new EU Framework Programme for Research and Innovation. Besides reports about technology transfer support and building ecosystems and value chains to ensure better time to market and higher impact of knowledge-based technologies, the acceptability of solutions, especially with regard to security and privacy aspects, were presented and discussed in depth. pHealth 2015, in Västerås, addressed mobile technologies, knowledge-driven applications and computer-assisted decision support, and also apps designed to support the elderly and chronic patients in their daily, and possibly independent, living. The fundamental scientific and methodological challenges of adaptive, autonomous, and intelligent pHealth approaches, the new role of patients as consumers and an active party with growing autonomy and related responsibilities, as well as requirements and solutions for mHealth in low- and medium-income countries have been also considered. The pHealth 2016 conference in Heraklion aimed at the integration of biological and medical data and the deployment of mobile technologies through the development of micro-nano-bio smart systems. The emphasis was on personalised health, virtual care, precision medicine, big bio-data management and analytics. The pHealth 2017 event in Eindhoven provided an inventory of the former conferences, summarising requirements and solutions for pHealth systems, highlighting the importance of trust, and the new focus on behavioural aspects in the design and use of pHealth systems. One specific aspect addressed was the need for flexible, adaptive and knowledge-based systems, as well as decision intelligence. pHealth 2018 in Gjøvik established national and European satellite workshops, complementing the more theoretical consideration of the majority of the papers with organisational and practical experiences. Borrowing from the good experiences of former events, pHealth 2018 responded to the national and regional needs to advance healthcare systems and their services to citizens and health professionals. pHealth 2019, in Genoa, put a special emphasis on artificial intelligence (AI) and machine learning (ML) and their deployment for decision support. In that context, ethical challenges and related international manifests were discussed in depth. pHealth 2020, organised in Prague as virtual event, addressed AI and robots, bio-data management and analytics for health and social care, security, privacy and safety challenges, integrated care, and also the intelligent management of specific diseases including the Covid-19 pandemic. pHealth 2021, in Genoa, once again organised as a virtual event focused on digital health ecosystems in transformation. Thereby, the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, as well as the Health Internet of Things (HIoT) for personalised health, systems medicine, public health and virtual care especially have been considered. The 2022 edition of the pHealth conference series combines the former organisational schemes towards a hybrid event in Oslo. The conference marks the conclusion of the ten year period during which Bernd Blobel has served as Chair of the pHealth SPC and the pHealth Steering Committee, and summarises the evolution that has occurred during this time. Consequently, pHealth 2022 focuses on personalised, preventive, predictive, participative precision (P5) medicine and the integration and interoperability between health informatics standards, but also on practical experiences with the deployment of HL7 FHIR. The conference also addresses new potential risks for security and privacy, as well as safety opportunities and challenges, trustworthiness of partners and processes, and the motivation and empowerment of patients in the care processes. The multilateral benefits of pHealth technologies – including artificial intelligence, learning systems, intelligent robots, etc. – for all stakeholder communities offer enormous potential, not only for the improvement of medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, for improving patient experiences.

The conference is organised under the patronage of the Norwegian University of Science and Technology (NTNU), Gjøvik, and in particular the eHealth and Welfare Security group at the Center for Cyber and Information Security (CCIS) and the Department of Information Security and Communication Technology (IIK), which, together with the Research Council of Norway, also partially funds this event. In addition, the Norwegian Research Center in Oslo provides the venue for the meeting. Following a long-standing tradition, the Working Groups “Electronic Health Records (EHR)”, “Personal Portable Devices (PPD)”, “Security, Safety and Ethics (SSE)”, and “Translational Health Informatics” of the European Federation for Medical Informatics (EFMI) have also been actively involved in the preparation and realisation of the pHealth 2022 event.

This proceedings volume covers 2 Keynote Papers, 10 Invited Papers, 20 Full Papers, and 4 Poster Papers from 113 authors from 23 countries all around the world. All submissions have been carefully and critically reviewed by at least two independent experts from a country other than the author’s home country, and additionally by at least one member of the Scientific Programme Committee. This very selective process guaranteed the high scientific level of the accepted and ultimately published papers. The editors are indebted to the internationally acknowledged and highly experienced reviewers for having essentially contributed to the quality of the conference and the book at hand.

Neither the pHealth 2022 Conference nor the publication of the pHealth 2022 Proceedings by IOS Press would have been possible without the pecuniary and spiritual supporters and sponsors. These also include the Research Council of Norway and the Center for Cyber and Information Security (CCIS) of the Norwegian University of Science and Technology (NTNU) and the European Federation for Medical Informatics (EFMI) and standards-developing organisations such as HL7 International, ISO/TC215 or CEN/TC251.

The editors are also grateful to the members of the international Scientific Programme Committee, and especially the dedicated efforts of the Local Organising Committee members and their supporters for their careful preparation and smooth operation of the conference.

Bernd Blobel, Bian Yang, Mauro Giacomini

(Editors)

Health and social care ecosystems are currently a matter of foundational organizational, methodological and technological paradigm changes towards personalized, preventive, predictive, participative precision (5P) medicine. For designing and implementing such advanced ecosystems, an understanding and correct representation of structure, function and relations of their components is inevitable. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be internationally standardized. Summarizing the first author’s Keynotes over the last 15 years of pHealth conferences, the paper discusses concepts, standards and principles of 5P medicine ecosystems including their design and implementation. Furthermore, a guidance to find and to deploy corresponding international standards in practical projects is provided.

In this paper, we present on ongoing research to use a socio-technical privacy, information security and cybersecurity model to support the design, development and delivery processes of health care services for aging in place. The current research in gerontechnological services development is reviewed, and experiences from the use of serious games to evaluate the model are outlined.

eHealth applications and tools have the potential to improve coordination, knowledge, and information sharing between health professionals as well as continuity of care. One of the main obstacles hindering its full integration and use, particularly in the healthcare sector in developing and low and middle-income countries is the lack of qualified staff and healthcare personnel. To explore obstacles that hinder capacity and innovation promotion initiatives, a survey was conducted among BETTEReHEALTH partners. A questionnaire was used to collect quantitative data from 37 organizations. Although there are different buckets of capacity-building and innovation promotion activities going on, the findings showed very few targeting policymakers and eHealth specialists. The findings found that obstacles to capacity building and innovation promotion include lack of finance, poor infrastructure, poor leadership, and governance, and these obstacles are context or region specific. Findings from our study concur with those from previous research on the need to identify practical solutions and simple interventions to address eHealth obstacles to capacity building in developing countries. As measures to mitigate these obstacles, our study proposed the need for adequate policies, strong political commitment, the development of academic modules to be integrated into existing educational programs, and the creation of more in-country and on-site capacity-building activities. While this study contributes to the discourse on eHealth capacity-building and innovation promotion initiatives among healthcare and public health professionals, the study has a limitation as data was collected only from BETTEReHEALTH partners.

The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperability must go beyond integrating data from different sources and include the understanding of the meaning of the data in the context of concepts and contexts they represent for a specific use case. In other words, we have to advance from data sharing through sharing semantics up to sharing clinical and medical knowledge. According to the Good Modeling Best Practices, we have to start with describing the real-world business system by domain experts using Domain Ontologies before transforming it into an information and communication technology (ICT) system, thereafter specifying the informational components and then transforming the system into an implementable solution. Any representation style – in the system development process acc. to ISO 10746 called system view – is defined by a related ontology, to be distinguished from real-world domain ontologies representing the knowledge spaces of involved disciplines. The system enabling such representational transformation shall also support versioning as well as the management of historical evolutions. One of such systems is the Common Terminology Service Release 2 (CTS2), which is a standard that allows the complete management of terminological contents. The main objective of this work is to present the choices we made to transform an ontology, written in the standard Ontology Web Language (OWL), into the CTS2 objects. We tested our transformation approach with the Alzheimer’s Disease Ontology. We managed to map all the elements of the considered ontology to CTS2 terminological resources, except for a subset of elements such as the equivalentClass derived from restrictions on other classes.

The population aging has facilitated a growing number of welfare technologies and smart home solutions. These technologies enable clinical staff and health care professionals to provide health services in an intelligent way with the trend of patient-centric digital health platforms. As one of the health services, response center service is facing new challenges when connected with welfare technologies, such as false alarms, security threats, privacy leakage, etc. This paper introduces the mechanism of the response center and the role it plays in healthcare. We conduct an exploratory study to find out the benefits and challenges of the response center service from the results of a structured interview. Based on the findings, we identify the required services to improve the intelligent response center mechanism

Introduction:

COVID-19 has affected people in several countries around the world. They experience respiratory symptoms that can be mild, moderate, or severe. Several reviews that characterize the risk factors of COVID-19 have been performed, but most address only risk factors associated with medical conditions, ignoring environmental and sociodemographic-socioeconomic factors.

Objective:

This study aims at characterizing different risk factors in the published literature that influence contagion by COVID-19.

Methods:

The review consists of three stages, including a systematic mapping with studies found in the Scopus database, an analysis of results, and finally the identification of relevant COVID-19 risk factors.

Results:

A map of studies id provided considering two main groups: the type of research and context. Most studies consider risk factors associated with medical conditions, while research on other factors is scarce.

Conclusions:

Medical conditions such as diabetes, obesity, cardiovascular disease, hypertension, and factors such as age and sex, appear to be the ones that increase the risk of contracting COVID-19. Further research is needed on environmental, sociodemographic, and socioeconomic risk factors.

The explosion of interest in exploiting machine learning techniques in healthcare has brought the issue of inferring causation from observational data to centre stage. In our work in supporting the health decisions of the individual person/patient-as-person at the point of care, we cannot avoid making decisions about which options are to be included or excluded in a decision support tool. Should the researcher’s routine injunction to use their findings ‘with caution’, because of methodological limitations, lead to inclusion or exclusion? The task is one of deciding, first on causal plausibility, and then on causality. Like all decisions these are both sensitive to error preferences (trade-offs). We engage selectively with the Artificial Intelligence (AI) literature on the causality challenge and on the closely associated issue of the ‘explainability’ now demanded of ‘black box’ AI. Our commitment to embracing ‘lifestyle’ as well as ‘medical’ options for the individual person, leads us to highlight the key issue as that of who is to make the preference- sensitive decisions on causal plausibility and causality.

Diagnostics accuracy and usability of symptom checkers have been researched in several studies. Their ability to set a correct diagnosis especially in the urgent cases is questionable. There is one aspect of symptom checkers that has not been deeply studied yet. It is their ability to motivate patients to follow up after receiving a direct recommendation and to decrease a load on the health care professionals. The goal of this research is to study how patients behave after receiving a recommendation from a symptom checker and motivation of this behavior. We studied how patients react on the symptom checker recommendations and the motivation behind this behavior. In total we invited 3615 patients to have a symptom checker screening; 2374 of them agreed to run a symptom checker screening; 867 of them agreed to participate in the study. The proportion of the patients who agreed to have a symptom checker screening. So, we can clearly see that symptom checker screening doesn’t result in a significant decrease of the load on healthcare professionals. This is supported by the quantitative study results. The patients emphasized the ease of use of the tool and clearness of the recommendations it gives. However, they perceived it as rather a second opinion tool or a tool that helps to prepare to the doctor’s visit.

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

From beginning to today, pHealth has been a data driven service that collects and uses personal health information (PHI) for personal health services and personalized healthcare. As a result, pHealth services use intensively ICT technology, sensors, computers and mathematical algorithms. In past, pHealth applications were focused to certain health or sickness related problem, but in today they use mobile devices, wireless networks, Web-technology and Cloud platforms. In future, pHealth uses information systems that are highly distributed, dynamic, increasingly autonomous, multi-stakeholder data driven eco-system having ability to monitor anywhere person’s regular life, movements and health related behaviours. Because privacy and trust are pre-requirements for successful pHealth, this development raises huge privacy and trust challenges to be solved. Researchers have shown that current privacy approaches and solutions used in pHealth do not offer acceptable level of privacy, and trust is only an illusion. This indicates, that today’s privacy models and technology shall not be moved to the future pHealth. The authors have analysed interesting new privacy and trust ideas published in journals, and found that they seem to be effective but offer only a partial solution. To solve this weakness, the authors used a holistic system view to aspects impacting privacy and trust in pHealth, and created a template that can be used in planning and development future pHealth services. The authors also propose a tentative solution for future trustworthy pHealth. It combines privacy as personal property and trust as legal binding fiducial duty approaches, and uses a Blockchain-based smart contract solution to store person’s privacy and trust requirements and service providers’ promises.

Background:

Telemedicine can provide a solution for disease management during the COVID-19 pandemic. This literature review aims to explore the role of telemedicine during the COVID-19 pandemic for management of cancer patients.

Method:

A comprehensive systematic search was conducted in PubMed, Science Direct, EMBASE, and Web of Science databases for the papers published until April 2021. Studies were included in case they had practically used telemedicine in the management of cancer patients during the COVID-19 crisis.

Results:

After screening 2614 titles and abstracts and reviewing 305 full-texts, 16 studies were found to be eligible. The results indicated that most of the patients contacted by telemedicine services mostly used to intract with patients breast cancer (n=4, 25%). The most common use of telemedicine was the provision of virtual visit services (n=10, 62.25%). Besides, communication was most frequently provided by live video conferences (n=11, 68.75%).

Conclusion:

Telemedicine can provide continued access to necessary health services in oncology care and serve as an important role in pandemic planning and response.

The Internet of Medical Things (IoMT) emerges with new trendsetter device applications, where it defines the incorporation of medical devices with the Internet of Things (IoT). The healthcare sector continues to encounter challenging obstacles that have an impact on the quality of treatment provided to patients. To get rid of this problem, IoMT is being deployed to achieve the high reliability and efficiency of the health system. The IoMT devices are superimposed with clinical information as they contain the details of patient health data, address, and other patient identifiers. By containing such amount of sensitive information, it becomes cumbersome to preserve data privacy and security. Due to inadequate security and privacy precautions, patient health data is susceptible to leakage, which has a direct impact on the patient’s life. In addition, the majority of medical devices are susceptible to cyberattacks, putting patient information at risk. Inadequate control of life-support equipment can have a devastating effect on patient outcomes. Thus, this survey intends to review the various security models of IoMT devices using standard techniques to support health care systems. It provides a wide range of literature reviews regarding IoMT systems and compares them with traditional methodologies. This review work exhibits the motivation for current technologies to maintain the security and privacy of patients’ data with IoMT devices. The systematic review entails background on security in IoMT devices, techniques for security, usage of diverse validation measures, and also discusses the problems and motivation for future research work.

In the midst of a global pandemic, perspectives on how digital can enhance healthcare service delivery and workflow to address the global crisis is underway. Action plans collating existing digital transformation programs are being scrutinized to set in place core infrastructure and foundations for sustainable healthcare solutions. Reforming health and social care to personalize the home care setting can for example assist in avoiding treatment in a crowed acute hospital setting and improve the experience and impact on both health care professionals and service users alike. In this information intensive domain addressing the interoperability challenge through standards based roadmaps is the lynchpin to enable health and social care services to connect effectively. Thus facilitating safe and trustworthy data workflow from one healthcare systems provider to another. In this paper we showcase a methodology on how we can extract, transform and load data in a semi-automated process using a Common Semantic Standardized Data Model (CSSDM) to generate personalized healthcare knowledge graph (KG). CSSDM is based on formal ontology of ISO 13940:2015 ContSys for conceptual grounding and FHIR based specification to accommodate structural attributes to generate KG. CSSDM we suggest enables data harmonization and data linking. The goal of CSSDM is to offer an alternative pathway to speak about interoperability by supporting a different kind of collaboration between a company creating a health information system and a cloud enabled health service. This pathway of communication provides access to multiple stakeholders for sharing high quality data and information.

Sharing of personal health data could facilitate and enhance the quality of care and the conduction of further research studies. However, these data are still underutilized due to legal, technical, and interoperability challenges, whereas the data subjects are not able to manage, interact, and decide on what to share, with whom, and for what purposes. This barrier obstacles continuity of care across in the European Union (EU), and neither healthcare providers nor data researchers nor the citizens are benefiting through efficient healthcare treatment and research. Despite several national-level EU studies and research activities, cross-border health data exchange and sharing is still a challenging task, which is addressed only under specific cases and scenarios. This manuscript presents the InteropEHRate research project along with its key innovations, aiming to offer Electronic Health Records (EHRs) at peoples’ hands across the EU, via the exploitation of three (3) different protocol families, namely the Device-to-Device (D2D), Remote-to-Device (R2D), and Research Data Sharing (RDS) protocols. These protocols facilitate efficient, secure, privacy preserving, and General Data Protection Regulation (GDPR) compliant health data sharing across the EU, covering different real-world use cases.

Improving the interoperability of healthcare information systems is a crucial clinical care issue involving disparate but coexisting information systems. However, healthcare organizations are also facing the dilemma of choosing the right ETL tool and architecture pattern as data warehouse enterprises. This article gives an overview of current ETL tools for healthcare data integration. In addition, we demonstrate three ETL processes for clinical data integration using different ETL tools and architecture patterns, which map data from various data sources (e.g. MEONA and ORBIS) to diverse standards (e.g. FHIR and openEHR). Depending on the project’s technical requirements, we choose our ETL tool and software architecture pattern to boost team efficiency.

Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.

Welfare technology is expected to become a larger and more important part of the healthcare sector. This creates a need to understand, which information security risks welfare technology and affiliated devices are exposed to. In a scoping review, we present an extensive overview of relevant threats. Furthermore, some key vulnerabilities in health technologies like IoMTs and welfare technology devices are highlighted. In the conclusions, the risks relevant for welfare technology is discussed, where four top risks are emphasized as a result of the findings.

Medical data describe patient health information, both in healthy and disease conditions. In any case, health institutions need to ask for patient consent in order to provide their services. Patients usually give consent on a one-time basis, for a specific usage. Afterwards, if medical data usage is research, original patient consent does not apply and further consents should be required. On the other hand, provenance of medical data to verify the origin of health procedures is desirable, as digital health is increasing. We propose HIPAMS modular architecture to provide both provenance and dynamic consents for medical data as described in this paper.

Empowerment is a process through which people acquire the necessary knowledge and self-awareness to understand their health conditions and treatment options, self-manage them, and make informed choices. Currently, few stand-alone applications for patient empowerment exist and people/patients often go on the Web to search for health information. Such information is mainly obtained through generic search engines and it is often overwhelming, too generic, and of poor quality. Intelligent Empowering Agents (IEA) can filter such information and assist the user in the understanding of health information about specific complaints or health in general. We have designed and developed a first prototype of an IEA that dialogues with the user in simple language, collects health information from the Web, and provides tailored, easily understood, and trusted information. It empowers users to create their own comprehensive and objective opinion on health matters that concern them. The paper describes the IEA main characteristics and presents the results of subjective tests carried out to assess the effectiveness of the IEA. Twenty-eight Master students in Digital Health filled an online survey presenting questions on usability, user experience and perceived value. Most respondents found the IEA easy to use and helpful. They also felt that it would improve communication with their doctors.

Security awareness training has long been considered a critical element in organizational cybersecurity preparedness and a mandatory activity in many laws and regulations as well as cybersecurity management standards such as ISO 27001. Organizations approach the issue with different methods, but many rely on online training as a cost-effective way to reach a large number of employees at a low cost. When this training is delivered as a voluntary measure, it is essential to have knowledge about the factors contributing to motivation to participate. This study uses the theoretical concepts from Protection motivation theory (PMT) as well as looking into how individual personality traits might affect the willingness to participate. A survey was conducted in a large Norwegian municipality and the data analyzed with PLS-SEM. The study found support for the concepts of Cost and Effectiveness affecting Motivation, but not Vulnerability and Severity. The personality traits of Extroversion and Agreeableness was found to have some moderating effect