How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Integrating Technology to Support and Maintain Glycemic Control in People With Diabetes

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCEType II diabetes is a chronic disease state that leads to increased morbidity and mortality and impacts the lives of millions of Americans. This quality improvement project explored the use of a free smartphone application, Glucose Buddy, in aiding people with Type II diabetes to achieve and maintain glycemic control. The project was conducted through the involvement of patients at the Creekside Family Health Clinic in Ketchikan, Alaska over a three month time period. Pre-intervention hemoglobin A1c (HA1c) was compared with post-intervention HA1c. The project, due to the small sample size and high withdraw rate, was not statistically significant. However, there was clinical significance as it showed a decrease in HA1c levels in 60% of the participants.Abstract / Introduction / Literature Review and Synthesis / Problem Statement / Research Question / Methodology / Results / Limitations / Conclusions / Outcomes / Impact on Practice / Dissemination / References / Appendix A / Appendix B / Appendix C / Appendix

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Using a gamified monitoring app to change adolescents' snack intake : the development of the REWARD app and evaluation design

Background: As the snacking pattern of European adolescents is of great concern, effective interventions are necessary. Till now health promotion efforts in children and adolescents have had only limited success in changing adolescents' eating patterns and anthropometrics. Therefore, the present study proposes an innovative approach to influence dietary behaviors in youth based on new insights on effective behavior change strategies and attractive intervention channels to engage adolescents. This article describes the rationale, the development, and evaluation design of the 'Snack Track School' app. The aim of the app is to improve the snacking patterns of Flemish 14- to 16-year olds. Methods: The development of the app was informed by the systematic, stepwise, iterative, and collaborative principles of the Intervention Mapping protocol. A four week mHealth intervention was developed based on the dual-system model with behavioral change strategies targeting both the reflective (i.e., active learning, advance organizers, mere exposure, goal-setting, monitoring, and feedback) and automatic processes (i.e., rewards and positive reinforcement). This intervention will be evaluated via a controlled pre-post design in Flemish schools among 1400 adolescents. Discussion: When this intervention including strategies focused on both the reflective and automatic pathway proves to be effective, it will offer a new scientifically-based vision, guidelines and practical tools for public health and health promotion (i.e., incorporation of learning theories in intervention programs)

Intelligent Medicine Kit for Healthcare Monitoring, An IOT Based Solution

Increase in chronic diseases worldwide demands efficient healthcare solutions for maintaining well-beingof people. Treatment requires timely in-take of medicines and strict adherence to routine. This lack of adherenceis estimated to cause many deaths and hospitalizations. If we can get people to take their medications regularly,they won’t develop complications. In addition to improving patient outcomes, medication adherence will reducehealth care costs associated with these conditions. Healthcare monitoring solutions based on Internet of Things(IoT) technology has drawn significant research attention. This paper proposes an IoT based user configurablecustomized intelligent medicine kit augmented with Wi-Fi and Bluetooth Low Energy technologies. It hascapability to detect whether patient is taking all prescribed medicines on fix schedule and intelligentlycommunicates the same to patient and their close relatives using uniquely created Four Tier Notification System(FTNS), thus helping patient to live a healthy life. The paper discusses a novel theme on the functioning of amedical grade device which would consume information from sensor and send it to the central server with amaximum possibility of success using Four Tier Notification System (FTNS). This novel approach discusseshandshaking of connection to the central serve with a fallback mechanism to achieve maximum success of datasynchronization. This paper also discusses how this useful data from medicine kit can help healthcare sector toclosely track patient's physical activities and helps to influence the way healthcare sector operates in future