Personal Health and Wellness Management with Technologies
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ItemMachine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis( 2019-01-08)Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD.
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ItemBrowsing to Breathe: Social Media for Stress Reduction( 2019-01-08)In a pressurized world, it is important that research continually works towards discovering new ways to improve the mental and physical wellness of society. Traditional approaches for measuring stress have been vastly explored, however rising concerns for chronic stress calls for new methodologies to sense stress on Social Media, which, as a tool, could provide valuable insight into wellness. Over a period of two-weeks, a rigorous mixed methods approach (daily surveys, Social Media data collection and post-study interviews) aided the discovery that Social Media, particularly browsing, can improve the wellness of placement students, as it helped them to cope with stress. The adoption of an established coping survey for stress helped in the identification of behavioral differences between participants. This paper explores the positive impact that Social Media can have on stress and highlights the potential of digital coping mechanisms.
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ItemDYNECOM: Augmenting Empathy in VR with Dyadic Synchrony Neurofeedback( 2019-01-08)In a novel experimental setting, we augmented a variation of traditional compassion meditation with our custom built VR environment for multiple concurrent users. The system incorporates respiration and brainwave based biofeedback that enables responsiveness to the shared physiological states of the users. The presence of another user’s avatar in the shared virtual space supported low level social interactions and provided active targets for evoked compassion. We enhanced interoception and the deep empathetic processes involved in compassion meditation with real time visualizations of breathing rates and the level of approach motivation assessed from EEG frontal asymmetry, and the dyadic synchrony of those signals between the two users. We found how the different biofeedback types increased both the amount of physiological synchrony between the users and their self-reported empathy, illustrating how dyadic synchrony biofeedback can expand the possibilities of biofeedback in affective computing and VR solutions for health and wellness.
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ItemLocation-based Mobile Games in mHealth: A Preliminary Study of Pokémon Go in Promoting Health Exercising( 2019-01-08)Location-based mobile games such as Pokémon Go might improve players’ physical activities (e.g., walking) and social interactions. With a limited research on mobile exergaming activities, this study examined relationships among Pokémon Go players’ gaming activities, willingness to communicate, and the likelihood of engaging in exercises. Our study showed that the longer participants had been playing the game, the higher the likelihood that they would engage in exercises. Our findings revealed a positive relationship between exercise during gameplay and willingness to communicate with other players. Our study provides implications to the use of location-based mobile games to promote health campaigns and improve the general health of the population.
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ItemUsing On-line Guided-Self Determination to Provide High Quality Diabetes Self-Management: An analysis with Activity Theory( 2019-01-08)This paper presents and evaluation of the use of a developed on line guided Self-Determination (GSD) solution for young adults with Type 1 diabetes. Activity theory is proffered as a suitable analysis lens to highlight and unpack key social interactions. An exploratory descriptive design with four stages that involved: (1) developing the GSD program online; (2) training diabetes educators to use the GSD program in an online format; (3) implementing and pilot testing the GSD program; and (d) evaluating the online version formed the adopted methodology.
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ItemIncorporating Personalization Features in a Hospital-Stay Summary Generation System( 2019-01-08)Most of the currently available health resources contain vast amount of information that are created by keeping the “general” population in mind, which in reality, might not be useful for anyone. One approach to providing comprehensible health information to patients is to generate summaries that are personalized to each individual. This paper details the design of a personalized hospital-stay summary generation system that tailors its content to the patient’s understanding of medical terminologies and their level of engagement in improving their own health. Our summaries were found to cover around 80% of the health concepts that were considered as important by a doctor or a nurse. An online survey conducted on 150 participants verified that our algorithm’s interpretation of the personalization parameters is representative of that of a larger population.
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ItemPersonal Health Technology: CPN based Modeling of Coordinated Neighborhood Care Environments (Hubs) and Personal Care Device Ecosystems( 2019-01-08)Healthcare supported by mobile devices, or “mHealth,” has rapidly emerged as a very broad ecosystem that can empower safer, more affordable, and more comfortable independent living environments and assist residents to age in place with a variety of well-understood chronic diseases. mHealth ecosystems leverage every available type of regulated medical and consumer-grade Patient Care Devices (or PCDs). mHealth technologies can also support innovative care and reimbursement models like the Patient-Centered Medical Home (PCMH) and Accountable Care Organizations (ACOs). Although consumer-grade PCDs are becoming ubiquitous, they typically do not provide a large variety of integrated system options for care coordination beyond single individuals. Understanding how to safely implement and use those devices to support heterogeneous mixes of patients, illnesses, devices, medications, and situations in neighborhood contexts is still a case-by-case challenge. By utilizing a well-formalized Colored Petri Nets (CPNs) based approach, this paper provides a proof-of-concept simulation framework for modeling and designing coordinated community care hubs.
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ItemAnalysis of Business Intelligence Applications in Healthcare Organizations( 2019-01-08)In today’s healthcare (HC) market there are lots of pressures on HC organizations (Os). Besides, many challenges including; demographic changes and the need to manage vastly increasing data volumes in HC, have motivated these organizations to adopt business intelligence (BI) solutions. Through a systematic review of the literature, this study establishes the patterns of BI adoption in the HC domain by examining the nature of BI solutions in use, expected outcomes from BI use, specific types of BI capabilities deployed, and aspects of HCOs directly impacted. Findings from our study provide a foundation for future research agenda on BI in Healthcare. We conclude by highlighting the shortcomings of current BI practice in the HC domain in the context of the emerging value-based (VB) HC delivery model and the need for research in this direction.
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ItemTrends in Patient Generated Data – An Initial Review( 2019-01-08)In recent years, patient-centered care has gained significant momentum in healthcare and the patient is more involved as an active participant in data generation. In this state of the art review we identify trends in patient generated data (PGD) and areas in need of further research by reviewing papers published in the health tracks of five high-ranked IS conferences. Our results suggest that research is mostly empirically grounded and primarily focuses on sickness rather than wellness issues. There is an emphasis on chronic diseases and self-management, dealing with user motivation, and a focus mostly on mobile apps. Though technology plays an important part, there is scarce problematization of and theorization on PGD. Further studies are needed that investigate the effects of PGD on patients and healthcare providers, include a wider range of issues and incorporate wearable devices more comprehensively.