Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

Sport Psychology App lication: NCAA Coaches\u27 Preferences for a Mental Training Mobile App

This study utilized a consumer marketing approach to investigate National Collegiate Athletic Association (NCAA) head coaches\u27 preferences for a mental training mobile application (mobile map) using a conjoint market analysis. Head coaches\u27 preferences for a mental training mobile app were compared based on price, ability to track athlete use of the app, recommendation sources, the inclusion of daily functions, coaches\u27 awareness of the app being used by other teams, and the credibility of the mobile app content creators. Price and tracking athlete use were the two most important characteristics to coaches. Considering all characteristics, coaches preferred mobile apps that cost less than {dollar}200, provided comprehensive tracking of athlete use, came with an internal recommendation, included daily functions, were used by other teams, and were created by content creators who work with other successful programs. Based on market simulations, more than two-thirds of coaches would purchase a mental training mobile app with the characteristics presented in this study if given the chance. The present findings are evidence that the use of mental training at the NCAA level may rely more on the delivery method and cost of services than previously thought

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter

Designing Healthy Consumption Support: Mobile application use added to (e)Coach Solution

Healthy living is an increasingly important topic on the agenda of policy makers. Containment of health care cost through public health and specific prevention programs is seen as a key element of the current social-economic policies in the western world. mHealth technology holds the promise to make healthy living more effective than traditional prevention programs. As part of a broad healthy living support program (including food, physical activity, stress management, social support and smoking cessation), we extended web-based and coach-based ‘healthy consumption’ support with smart phone application (mApp) assistance. This paper focuses on the design analysis phase, following a design research cycle. We start from a user needs analysis, then proceed to solution analysis and service development. The result is a design solution, using an mApp for the support of healthy food consumption, together with practical ‘optimal diet’ guidelines. This solution is embedded in a health coach relationship. For the future, we anticipate more personal and intelligent mobile applications for health behavior tracking and feedback, plus an increasing role in health provider processes

User Engagement In Finnish Mobile Health Applications: Use of Gamification and Social Elements

Objectives The main objectives of this study were to analyze how gamification and social elements are used to engage the users of Finnish mobile health applications, as well as the possible connection between these two aspects. Additionally, the relationship between theory and practice is explored through the most relevant consumer psychology frameworks and models. Concerning user engagement, the study focuses particularly on motivation creation. Summary In the rapidly growing field of mobile health, successful consumer engagement is critical. In this thesis, diverse means for motivation creation are covered to answer the above-mentioned objectives. Four Finnish health applications were chosen for the analysis according to their diverse purposes and features. Qualitative interviews were conducted for four implementers and three active users of these applications. Both viewpoints were explored to understand the big picture; tools used to build motivation and outcomes in the mind of the consumer. Conclusion The research indicates that there is an intertwined connection between gamification, social elements, and user engagement. The results propose that social elements are required to make game elements fully motivational. Means and tools used for motivation creation vary according to the nature and purpose of the application. Experience-driven applications create motivation through social and game elements, whereas more data-driven ones use more functional tools to motivate. Theories considered in health application development mainly consist of social-centered models