Improving user engagement by aggregating and analysing health and fitness data on a mobile app

© Springer International Publishing Switzerland 2015. Nowadays, health, fitness and contextual data can be ubiquitously collected using wearable devices, sensors and smart phones and be stored in various servers and devices. However, to engage users in active monitoring of their health and fitness, it is essential to personalise the monitoring and have all the relevant data in one place. It is also important to give users control on how their data is collected, analysed, presented and stored. This paper presents how those important features are integrated in myFitnessCompanion®, an Android Health and fitness app developed by our team. The app is able to aggregate data from multiple sources, keep it on the phone or export it to servers or Electronic Health Records (EHR). It can also present the aggregated data in a personalised manner. A mobile app such as myFitnessCompanion® is a solution to the personalisation, interoperability and control issues that are key to user engagement

Bringing health and fitness data together for connected health care: Mobile apps as enablers of interoperability

Background: A transformation is underway regarding how we deal with our health. Mobile devices make it possible to have continuous access to personal health information. Wearable devices, such as Fitbit and Apple's smartwatch, can collect data continuously and provide insights into our health and fitness. However, lack of interoperability and the presence of data silos prevent users and health professionals from getting an integrated view of health and fitness data. To provide better health outcomes, a complete picture is needed which combines informal health and fitness data collected by the user together with official health records collected by health professionals. Mobile apps are well positioned to play an important role in the aggregation since they can tap into these official and informal health and data silos. Objective: The objective of this paper is to demonstrate that a mobile app can be used to aggregate health and fitness data and can enable interoperability. It discusses various technical interoperability challenges encountered while integrating data into one place. Methods: For 8 years, we have worked with third-party partners, including wearable device manufacturers, electronic health record providers, and app developers, to connect an Android app to their (wearable) devices, back-end servers, and systems. Results: The result of this research is a health and fitness app called myFitnessCompanion, which enables users to aggregate their data in one place. Over 6000 users use the app worldwide to aggregate their health and fitness data. It demonstrates that mobile apps can be used to enable interoperability. Challenges encountered in the research process included the different wireless protocols and standards used to communicate with wireless devices, the diversity of security and authorization protocols used to be able to exchange data with servers, and lack of standards usage, such as Health Level Seven, for medical information exchange. Conclusions: By limiting the negative effects of health data silos, mobile apps can offer a better holistic view of health and fitness data. Data can then be analyzed to offer better and more personalized advice and care

Book review: Subversive pedagogies: radical possibility in the academy edited by Kate Schick and Claire Timperley

In Subversive Pedagogies: Radical Possibility in the Academy, Kate Schick and Claire Timperley bring together contributors to explore teaching as a subversive space of radical possibility, drawing attention to pedagogies that are situated, embodied, caring and decidedly political. Judith Leijdekkers and Sander Hölsgens offer a conversation around the book, reading the collection as an invitation for fellow pedagogues to scrutinise, transform and resist the ready-made roles of teachers

Personal heart monitoring and rehabilitation system using smart phones

This paper discusses a personalized heart monitoring system using smart phones and wireless (bio) sensors. Based on several scenarios we present the functionality of a prototype we have built. The application is capable of monitoring the health of high risk cardiac patients. The smart phone application analyses in real-time sensor and environmental data and can automatically alert the ambulance and pre assigned caregivers when a heart patient is in danger. It also transmits sensor data to a healthcare centre for remote monitoring by a nurse or cardiologist. The system can be personalized and rehabilitation programs can monitor the progress of a patient. Rehabilitation programs can be used to give advice (e.g. exercise more) or to reassure the patient. © 2006 IEEE

Personalised mobile services supporting the implementation of clinical guidelines

Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence

Personalized service and network adaptation for smart devices

The availability of smart devices with integrated GSM/GPRS/WiFi and the rollout of public hotspots allow users to be always online at reasonable costs. Personalised and context aware applications will become available in the forthcoming years due to the wide availability of smart devices and the interest of telecom operators and service providers to provide personalised services. For the user to access his preferred network and services in a particular context we need to have some mechanisms in place and an infrastructure that reacts autonomously on behalf of the user. This paper proposes a solution based on context-aware user profiles and their associated user preferences. It describes a smart device centered solution and a prototype has been built for Microsoft Windows Mobile™ Pocket PCs to validate the ideas. © 2005 IEEE

Design of emotion-aware mobile apps for autistic children

Sensor technologies and facial expression recognition are now widely used by mobile devices to sense our environment and our own physical and mental state. With these technologies today, we have the ability to sense emotions and create emotion-aware apps. One target group that would benefit from emotion-aware Apps are autistic children as they have difficulty understanding and expressing emotions and they are keen mobile device users. However, current mobile apps aimed at autistic children are not emotion-aware. This led our team to design a suite of Apps, called CaptureMyEmotion, that uses wireless sensors to capture physiological data together with facial expression recognition to provide a very personalised way to help autistic children and their carers understanding and managing their emotions. This paper describes how we designed CaptureMyEmotion and it discusses our experience while using sensors and facial expression recognition to detect emotion. It presents in more details the first App we developed for Android phone and tablets, called MyMedia. MyMedia enables children to take photos, videos or sounds, and simultaneously attach emotion data to them. The photos can then be reviewed together with a carer providing them a new way to understand emotions and discussing their daily activities. © 2013 IUPESM and Springer-Verlag

Helping Autistic Children Understand Their Emotions Using Facial Expression Recognition and Mobile Technologies

One of the main challenges for autistic children is to identify and express emotions. Many emotion-learning apps are available for smartphones and tablets to assist autistic children and their carers. However, they do not use the full potential offered by mobile technology, such as using facial expression recognition and wireless biosensors to recognise and sense emotions. To fill this gap we developed CaptureMyEmotion, an Android App that uses wireless sensors to capture physiological data together with facial expression recognition to provide a very personalised way to help autistic children learn about their emotions. The App enables children to capture photos, videos or sounds, and simultaneously attach emotion data and a self-portrait photo. The material can then be reviewed and discussed together with a carer at a later stage. CaptureMyEmotion has the potential to help autistic children integrate better in the society by providing a new way for them to understand their emotions