Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

Holistic System Design for Distributed National eHealth Services

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IOT Service Utilisation in Healthcare

Utilising the new trend technologies in healthcare sector could offer alternative ways in managing the patients’ health records and also improve the healthcare quality. As such, this chapter provides an overview of utilising the Internet of Things (IoT) technology in healthcare sector as an emerging research and practical trend nowadays. The main benefits and advantages have been discussed in this chapter. On the other hand, it has been found that most of the hospitals in different countries are still facing many issues regarding their health information exchange. Recently, various studies in the area of healthcare information system mentioned that the fragmentations of the health information are one of the most important challenges with the distribution of patient information records. Therefore, in this chapter, we gave an in detail overview regarding the current issues facing the health sector in line with the IoT technologies. Additionally, a full description of advantages and disadvantages has been highlighted for using IoT in healthcare that can be considered as solutions for the mentioned issues

Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction

Peer reviewe

Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution

A framework for guiding the interdisciplinary design of mHealth intervention apps for physical activity behaviour change

The global pandemic of noncommunicable diseases and its associated premature mortality rates and socioeconomic burden have led to increasingly intensified efforts towards designing and delivering health promotion interventions aimed at addressing the leading modifiable health risk behaviours, such as physical inactivity. Developing physical activity behaviour change interventions that target individuals at the dual intra-interpersonal socioecological levels of health promotion has become a key objective worldwide. Digital and mobile technology is revolutionising the ways in which health behaviour change interventions are delivered to individuals across the world, with mobile health applications (mHealth apps) increasingly recognised as a powerful means of promoting physical activity behaviour change. However, with the growth and opportunities of mHealth apps, come several design challenges. Key design challenges concern the integration of theory, the incorporation of evidence-based behaviour change techniques, the application of persuasive systems design principles, and the importance of multi- and interdisciplinary collaborative design, development and evaluation approaches. These key challenges influence the output product design and effectiveness of mHealth physical activity behaviour change intervention apps. There exists a paucity of approaches for guiding and supporting the multi- and interdisciplinary collaborative design, development and evaluation of mHealth physical activity behaviour change intervention apps. To address this gap, this research study proposes an Interdisciplinary mHealth App Design Framework, framed by a novel boundary object view. This view considers the diverse communities of practice, boundary objects and supporting artefacts, process activities, and knowledge sharing practices necessary and relevant to the design of effective mHealth physical activity behaviour change intervention apps. The framework’s development is guided by a Design Science Research (DSR) approach. Its core components are based on the findings of a critical theoretical analysis of twenty existing multi- and interdisciplinary digital health development approaches. Once developed, the framework is evaluated using a qualitative DSR linguistic interpretivist approach, with semi-structured interviews as the research instrument. The thematic analysis findings from interviews with thirty-one international academic researchers and industry practitioners informs the iterative modification and revision of an enhanced Interdisciplinary mHealth App Design Framework, constituting the main DSR artefact contribution of the research study. In addition, four theoretical contributions are made to the mHealth intervention app design body of knowledge, and a practical contribution is made through the provision of guideline recommendations for academics and industry practitioners. Methodological contributions are also made in terms of applying DSR, adopting a hybrid cognitive reasoning strategy, and employing a qualitative linguistic interpretivist approach to evaluation within a DSR project.Thesis (PhD) -- Faculty of Commerce, Information Systems, 202

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section