Personal Health and Wellness Management with Technologies
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ItemThe Digital Divide in Healthcare: A Socio-Cultural Perspective of Digital Literacy( 2022-01-04)As the use of IT for health management increases, digital health literacy becomes an important factor. Based on semi-structured interviews, this study presents experts’ perceptions about patients’ abilities to use digital health technologies for health management purposes and their perspectives on the importance of digital health literacy. This sociologically inspired paper will contribute with insights and a multidimensional perspective on the processes of digital health management (field), the importance of patient’s abilities (capital) in improving their health (habitus) from the expert’s perspectives.
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ItemPatient Generated Health Data: Framework for Decision Making( 2022-01-04)Patient information is a major part of healthcare decision making. Although currently scattered due to multiple sources and diverse formats, decision making can be improved if the patient information is readily available in a unified manner. Mobile technologies can improve decision making by integrating patient information from multiple sources. This study explores how patient generated health data (PGHD) from multiple sources can lead to improved healthcare decision making. A semi-systematic review is conducted to analyze research articles for transparency, clarity, and complete reporting. We conceptualize the data generated by healthcare professional as primarily from EHR/EMR and the data generated by patient as primarily from mobile apps and wearables. Eight themes led to the development of Convergence Model for Patient Data (CMPD). A framework was developed to illustrate several scenarios, to identify quality and timeliness requirements in mobile healthcare environment, and to provide necessary decision support.
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ItemMy data, my choice?! The difference between fitness and stress data monitoring on employees’ perception of privacy( 2022-01-04)Besides the vast distribution in the private sector, employers begin to integrate wearables in occupational health management (OHM). Through the implementation of 'stress' and 'fitness monitoring', organizations are able to invest in employees' health and well-being. While employees' consent is mandatory for the implementation, these, in turn, might perceive monitoring as a risk instead of realizing the benefits going along. By conducting an experimental study, we compare employees' perceived privacy risks/costs (PRC) and benefits (PBE) regarding the two monitoring cases. According to our results, employees interpret their stress data as rather sensitive while rating the PBE of fitness monitoring higher. Further, fair communication practices towards employees plays an essential role in the successful implementation of OHM. The research article provides theoretical and practical implications and sheds light on paths for further research regarding actual use behavior, international aspects, and employers' interests.
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ItemCurse or Blessing? Combining Personality Traits and Technology Acceptance to Investigate the Intention to Use of Digital Contact Tracing in Germany( 2022-01-04)In order to trace the transmission of COVID-19, digital contact tracing (DCT) provides an enormous value for the public health. However, the acceptance of the German contact tracing app, the Corona-Warn-App (CWA), falls short of the expected coverage in the general public. Accordingly, this study focuses on investigating the influencing factors on the CWA’s acceptance to demystify the missing puzzle and to face future pandemics. To assess this objective comprehensively, we investigate personality traits (guiding perception and behavior), subjective norm (expressing social influence), and trust in technology on acceptance variables. Our empirical results emphasize that besides the personality traits conscientiousness and agreeableness, perceived usefulness, subjective norm, and trust in technology play a vital role for engagement with the CWA. Our research offers starting points for the use of mobile health solutions, particularly in early epidemic stages.
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ItemChanging Personal Healthcare IT Use during the COVID-19 Pandemic( 2022-01-04)We examine post-adoptive IT use of fitness tracking technologies longitudinally using three data sets gathered before, during, and after the COVID-19 lockdowns in the United States. Using adaptive structuration theory (AST) as a meta-theory, we model post-adoptive IT use as having two fundamental types (continued and novel), each having distinct psychological and sociological antecedents. Sociological antecedents are further broken down into those coming from society and those coming from the technology. Findings indicate there are strong correlations between antecedents and the two types of use in all three data sets. Post-hoc analysis indicates continued and novel use vary across time. These variations are not static and appear to be non-linear. Implications and future research directions are also discussed.
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ItemAutomated Affect and Emotion Recognition from Cardiovascular Signals - A Systematic Overview Of The Field( 2022-01-04)Currently, artificial intelligence is increasingly used to recognize and differentiate emotions. Through the action of the nervous system, the heart and vascular system can respond differently depending on the type of arousal. With the growing popularity of wearable devices able to measure such signals, people may monitor their states and manage their wellness. Our goal was to explore and summarize the field of automated emotion and affect recognition from cardiovascular signals. According to our protocol, we searched electronic sources (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Explore, arXiv and medRxiv) up to 31 August 2020. In the case of all identified studies, two independent reviewers were involved at each stage: screening, full-text assessment, data extraction, and quality evaluation. All conflicts were resolved during the discussion. The credibility of included studies was evaluated using a proprietary tool based on QUADAS, PROBAST. After screening 4649 references, we identified 195 eligible studies. From artificial intelligence most used methods in emotion or affect recognition were Support Vector Machines (42.86%), Neural Network (21.43%), and k-Nearest Neighbors (11.67%). Among the most explored datasets were DEAP (10.26%), MAHNOB-HCI (10.26%), AMIGOS (6.67%) and DREAMER (2.56%). The most frequent cardiovascular signals involved electrocardiogram (63.16%), photoplethysmogram (15.79%), blood volume pressure (13.16%) and heart rate (6.58%). Sadness, fear, and anger were the most examined emotions. However, there is no standard set of investigated internal feelings. On average, authors explore 4.50 states (range from 4 to 24 feelings). Research using artificial intelligence in recognizing emotions or affect using cardiovascular signals shows an upward trend. There are significant variations in the quality of the datasets, the choice of states to detect, and the classifiers used for analysis. Research project supported by program Excellence initiative - research university for the University of Science and Technology. The authors declare that they have no conflict of interest.
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ItemAssessing the Benefits of a Teleassessment Solution Using a FVM Perspective( 2022-01-04)The recent COVID-19 pandemic has served to highlight the benefits of digital health in general and telehealth in particular. One area of telehealth that is particularly important is that of teleassessment. Currently, we are witnessing an exponential growth in total knee and total hip replacements (TKR) (THR) due to an aging population coupled with longer life expectancy which is leading to a high likelihood of an unsustainable burden for healthcare delivery in Australia. To address this imminent challenge, the following proffers a tele-assessment solution, ARIADNE (Assist foR hIp AnD kNEe), that can provide high quality care, with access for all and support for high value outcomes. A fit viability assessment is provided to demonstrate benefits of the proffered solution.
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ItemActing Egoistically in a Crisis: How Emotions Shape Data Donations( 2022-01-04)The spread of COVID-19 has affected all of us, be it financially, socially, or even physically. It has caused uncertainty and anxiety, which has put people into a "hot" mental state. Referred to as an empathy gap, people are assumed to make emotion-driven decisions in "hot" states compared to "cold" states, which contrasts with the normative assumption of rational decision-making in privacy research. Based on an experimental survey study among 445 participants, we investigate whether people's mental state interacts with individuals' information disclosure decision-making. We measure our research model in the context of actual health data donation, which constitutes a critical surveillance factor in the COVID-19 crisis. Thereby, we contribute to research by (1) investigating data donation behavior amid a crisis and (2) helping to explain further nuances of privacy decision-making and the importance of trust as a context-dependent driver of data donation.
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