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

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed

Analysis and design of individual information systems to support health behavior change

As a wide-ranging socio-technical transformation, the digitalization has significantly influenced the world, bringing opportunities and challenges to our lives. Despite numerous benefits like the possibility to stay connected with people around the world, the increasing dispersion and use of digital technologies and media (DTM) pose risks to individuals’ well-being and health. Rising demands emerging from the digital world have been linked to digital stress, that is, stress directly or indirectly resulting from DTM (Ayyagari et al. 2011; Ragu-Nathan et al. 2008; Tarafdar et al. 2019; Weil and Rosen 1997), potentially intensifying individuals’ overall exposure to stress. Individuals experiencing this adverse consequence of digitalization are at elevated risk of developing severe mental health impairments (Alhassan et al. 2018; Haidt and Allen 2020; Scott et al. 2017), which is why various scholars emphasize that research should place a stronger focus on analyzing and shaping the role of the individual in a digital world, pursuing instrumental as well as humanistic objectives (Ameen et al. 2021; Baskerville 2011b). Information Systems (IS) research has long placed emphasis on the use of information and communication technology (ICT) in organizations, viewing an information system as the socio-technical system that emerges from individuals’ interaction with DTM in organizations. However, socio-technical information systems, as the essence of the IS discipline (Lee 2004; Sarker et al. 2019), are also present in different social contexts from private life. Acknowledging the increasing private use of DTM, such as smartphones and social networks, IS scholars have recently intensified their efforts to understand the human factor of IS (Avison and Fitzgerald 1991; Turel et al. 2021). A framework recently proposed by Matt et al. (2019) suggests three research angles: analyzing individuals’ behavior associated with their DTM use, analyzing what consequences arise from their DTM use behavior, and designing new technologies that promote positive or mitigate negative effects of individuals’ DTM use. Various recent studies suggest that individuals’ behavior seems to be an important lever influencing the outcomes of their DTM use (Salo et al. 2017; Salo et al. 2020; Weinstein et al. 2016). Therefore, this dissertation aims to contribute to IS research targeting the facilitation of a healthy DTM use behavior. It explores the use behavior, consequences, and design of DTM for individuals' use with the objective to deliver humanistic value by increasing individuals' health through supporting a behavior change related to their DTM use. The dissertation combines behavioral science and design science perspectives and applies pluralistic methodological approaches from qualitative (e.g., interviews, prototyping) and quantitative research (e.g., survey research, field studies), including mixed-methods approaches mixing both. Following the framework from Matt et al. (2019), the dissertation takes three perspectives therein: analyzing individuals’ behavior, analyzing individuals’ responses to consequences of DTM use, and designing information systems assisting DTM users. First, the dissertation presents new descriptive knowledge on individuals’ behavior related to their use of DTM. Specifically, it investigates how individuals behave when interacting with DTM, why they behave the way they do, and how their behavior can be influenced. Today, a variety of digital workplace technologies offer employees different ways of pursuing their goals or performing their tasks (Köffer 2015). As a result, individuals exhibit different behaviors when interacting with these technologies. The dissertation analyzes what interactional roles DTM users can take at the digital workplace and what may influence their behavior. It uses a mixed-methods approach and combines a quantitative study building on trace data from a popular digital workplace suite and qualitative interviews with users of this digital workplace suite. The empirical analysis yields eight user roles that advance the understanding of users’ behavior at the digital workplace and first insights into what factors may influence this behavior. A second study adds another perspective and investigates how habitual behavior can be changed by means of DTM design elements. Real-time feedback has been discussed as a promising way to do so (Schibuola et al. 2016; Weinmann et al. 2016). In a field experiment, employees working at the digital workplace are provided with an external display that presents real-time feedback on their office’s indoor environmental quality. The experiment examines if and to what extent the feedback influences their ventilation behavior to understand the effect of feedback as a means of influencing individuals’ behavior. The results suggest that real-time feedback can effectively alter individuals’ behavior, yet the feedback’s effectiveness reduces over time, possibly as a result of habituation to the feedback. Second, the dissertation presents new descriptive and prescriptive knowledge on individuals’ ways to mitigate adverse consequences arising from the digitalization of individuals. A frequently discussed consequence that digitalization has on individuals is digital stress. Although research efforts strive to determine what measures individuals can take to effectively cope with digital stress (Salo et al. 2017; Salo et al. 2020; Weinert 2018), further understanding of individuals’ coping behavior is needed (Weinert 2018). A group at high risk of suffering from the adverse effects of digital stress is adolescents because they grow up using DTM daily and are still developing their identity, acquiring mental strength, and adopting essential social skills. To facilitate a healthy DTM use, the dissertation explores what strategies adolescents use to cope with the demands of their DTM use. Combining a qualitative and a quantitative study, it presents 30 coping responses used by adolescents, develops five factors underlying adolescents’ activation of coping responses, and identifies gender- and age-related differences in their coping behavior. Third, the dissertation presents new prescriptive knowledge on the design of individual information systems supporting individuals in understanding and mitigating their perceived stress. Facilitated by the sensing capabilities of modern mobile devices, it explores the design and development of mobile systems that assess stress and support individuals in coping with stress by initiating a change of stress-related behavior. Since there is currently limited understanding of how to develop such systems, this dissertation explores various facets of their design and development. As a first step, it presents the development of a prototype aiming for life-integrated stress assessment, that is, the mobile sensor-based assessment of an individual’s stress without interfering with their daily routines. Data collected with the prototype yields a stress model relating sensor data to individuals’ perception of stress. To deliver a more generalized perspective on mobile stress assessment, the dissertation further presents a literature- and experience-based design theory comprising a design blueprint, design requirements, design principles, design features, and a discussion of potentially required trade-offs. Mobile stress assessment may be used for the development of mobile coping assistants. Aiming to assist individuals in effectively coping with stress and preventing future stress, a mobile coping assistant should recommend adequate coping strategies to the stressed individual in real-time or execute targeted actions within a defined scope of action automatically. While the implementation of a mobile coping assistant is yet up to future research, the dissertation presents an abstract design and algorithm for selecting appropriate coping strategies. To sum up, this dissertation contributes new knowledge on the digitalization of individuals to the IS knowledge bases, expanding both descriptive and prescriptive knowledge. Through the combination of diverse methodological approaches, it delivers knowledge on individuals’ behavior when using DTM, on the mitigation of consequences that may arise from individuals’ use of DTM, and on the design of individual information systems with the goal of facilitating a behavior change, specifically, regarding individuals’ coping with stress. Overall, the research contained in this dissertation may promote the development of digital assistants that support individuals’ in adopting a healthy DTM use behavior and thereby contribute to shaping a socio-technical environment that creates more benefit than harm for all individuals

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Smart workplaces: a system proposal for stress management

Over the past last decades of contemporary society, workplaces have become the primary source of many health issues, leading to mental problems such as stress, depression, and anxiety. Among the others, environmental aspects have shown to be the causes of stress, illness, and lack of productivity. With the arrival of new technologies, especially in the smart workplaces field, most studies have focused on investigating the building energy efficiency models and human thermal comfort. However, little has been applied to occupants’ stress recognition and well-being overall. Due to this fact, this present study aims to propose a stress management solution for an interactive design system that allows the adapting of comfortable environmental conditions according to the user preferences by measuring in real-time the environmental and biological characteristics, thereby helping to prevent stress, as well as to enable users to cope stress when being stressed. The secondary objective will focus on evaluating one part of the system: the mobile application. The proposed system uses several usability methods to identify users’ needs, behavior, and expectations from the user-centered design approach. Applied methods, such as User Research, Card Sorting, and Expert Review, allowed us to evaluate the design system according to Heuristics Analysis, resulting in improved usability of interfaces and experience. The study presents the research results, the design interface, and usability tests. According to the User Research results, temperature and noise are the most common environmental stressors among the users causing stress and uncomfortable conditions to work in, and the preference for physical activities over the digital solutions for coping with stress. Additionally, the System Usability Scale (SUS) results identified that the system’s usability was measured as “excellent” and “acceptable” with a final score of 88 points out of the 100. It is expected that these conclusions can contribute to future investigations in the smart workplaces study field and their interaction with the people placed there.Nas últimas décadas da sociedade contemporânea, o local de trabalho tem se tornado principal fonte de muitos problemas de saúde mental, como o stress, depressão e ansiedade. Os aspetos ambientais têm se revelado como as causas de stress, doenças, falta de produtividade, entre outros. Atualmente, com a chegada de novas tecnologias, principalmente na área de locais de trabalho inteligentes, a maioria dos estudos tem se concentrado na investigação de modelos de eficiência energética de edifícios e conforto térmico humano. No entanto, pouco foi aplicado ao reconhecimento do stress dos ocupantes e ao bem-estar geral das pessoas. Diante disso, o objetivo principal é propor um sistema de design de gestão do stress para um sistema de design interativo que permita adaptar as condições ambientais de acordo com as preferências de utilizador, medindo em tempo real as características ambientais e biológicas, auxiliando assim na prevenção de stress, bem como ajuda os utilizadores a lidar com o stress quando estão sob o mesmo. O segundo objetivo é desenhar e avaliar uma parte do projeto — o protótipo da aplicação móvel através da realização de testes de usabilidade. O sistema proposto resulta da abordagem de design centrado no utilizador, utilizando diversos métodos de usabilidade para identificar as necessidades, comportamentos e as expectativas dos utilizadores. Métodos aplicados, como Pesquisa de Usuário, Card Sorting e Revisão de Especialistas, permitiram avaliar o sistema de design de acordo com a análise heurística, resultando numa melhoria na usabilidade das interfaces e experiência. O estudo apresenta os resultados da pesquisa, a interface do design e os testes de usabilidade. De acordo com os resultados de User Research, a temperatura e o ruído são os stressores ambientais mais comuns entre os utilizadores, causando stresse e condições menos favoráveis para trabalhar, igualmente existe uma preferência por atividades físicas sobre as soluções digitais na gestão do stresse. Adicionalmente, os resultados de System Usability Scale (SUS) identificaram a usabilidade do sistema de design como “excelente” e “aceitável” com pontuação final de 88 pontos em 100. É esperado que essas conclusões possam contribuir para futuras investigações no campo de estudo dos smart workplaces e sua interação com os utilizadores

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders

Biomedical Sensing - A Sensor Fusion Approach for Improved Medical Detection and Monitoring

Enhanced technological advancement in computation, communication, and sensing has dramatically changed the dynamics of modern medicine. Advancing preventive medicine is paramount to a sustainable improvement in the quality of life and life expectancy. On-body sensors provide continuous measurements for healthy and ailing individuals leading to faster recovery and more timely detection of illnesses. Novel sensor designs and sensor fusion for preventive monitoring can provide extensible benefits, including a better understanding of ailment progression, treatment optimization, and patient feedback through data analytics and visualization. However, existing research does not thoroughly investigate sensor fusion approaches in biomedical sensing, as well as spot sensing, which can provide better information through more accurate detection of specific tissues instead of secondary measurements. This article presents the development of an ex-vivo sensor fusion system to track a person\u27s muscular condition. The embedded system provides a significant benefit by notifying users of particular muscle events in real-time

Early diagnosis of disorders based on behavioural shifts and biomedical signals

There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du