Exploiting the quantified-self for developing a mobile health platform

The use of sensitive, personal medical data presents a key challenge in conducting exper- iment for research in healthcare, especially when they involve cooperation with profes- sional medical staff as well as real patients. It is required that mHealth trials which collect sensitive health data need professional medical staff and clinical approval. Many ethical, medical and pastoral concerns that are complex need to be managed. For example, what would happen if during the experiment, a software developer during software testing dis- covers that a volunteer has a serious illness that was previously undiagnosed? The trials in [10], [18], [147] and [62] are good examples of mHealth monitoring experiments which use sensitive health data, conducted in high-risk environments and require clinicians to be involved.

Of course, before systems are commissioned for use, such trials are required. However, at early stages, such clinical involvement is risky for clinical reasons and cumbersome for researchers. As wellbeing information is already shared by many users, and has a strong contextual link to a medical scenario, with suitable interaction models for users, it has the potential to act as an excellent proxy for mHealth monitoring. So, for a pre-clinical setting, to investigate technology and systems interaction, such complexity from clinical

involvement could be removed without losing context and relevance to the eventual clin- ical application by the use of a wellbeing scenario [78]. As described in [78], we have proposed the use of a wellbeing experiment as a proxy for an mHealth study without the risk of using real health data.

Chapter 4

Remote monitoring application

In this chapter, we describe a user study for using a wellbeing monitoring scenario as a proxy of an mHealth monitoring scenario. We have designed and built an RMA using an open-source OSMP, the popular Fitbit device for monitoring personal wellbeing data, and a simple Android/iOS remote notification application. The open-source OSMP Diaspora was modified with RMA functionality, to demonstrate remote monitoring, asynchronous user interaction, the implementation of multiple actors in a healthcare regime, and the im- plementation of appropriate security and privacy mechanisms. We have used wellbeing data and the self-measurement device Fitbit ChargeHR, and exploited the interest in the quantified-self to create a proxy for an mHealth scenario, as an experiment in pre-clinical stage without involving sensitive medical data from real patients, but without loss of con- text. (More details will be presented in Chapter 5, 6 and 7.) The use of wellbeing data in this manner is particularly valuable to researchers and systems developers, as key devel- opment work can be completed within a realistic scenario, but without risk to sensitive patient medical data.

4.1

Application outline

We describe the design of a user study using the Diaspora [36] open-source OSMP and Fitbit [43] activity trackers. Figure 4.1 shows our implementation consisting of two parts:

Figure 4.1: A design of a wellbeing experiment used as a proxy for mHealth monitoring using an online social network (OSN). Fitbit was used as a measurement system. An open- source online social media platform (OSMP) – Diaspora – provided access to the stored Fitbit data for different actor viewpoints. The conventional actors incarer network(patient, family, carer and doctor) was replaced with parallel roles (client, fitness buddy, fitness coach and personal trainer) in our wellbeing scenario. The dashed (red) outline shows the scope of our RMA built on Diaspora.

ameasurement systemandremote monitoring application.

As will be presented later in Chapter 7, the use of an open-source OSMP – Diaspora – platform can enable personal health monitoring while enabling flexible application de- velopment and allowing fine-grained control of security & privacy [77]. In our application development, the Diaspora platform was modified to provide the RMA functions, as well as the interaction between the actors (patient, family, carer and doctor) in thecarer networks

via an online social network. According to Section 3.4.3, the portal must be able to deal with a complex set of security and privacy issues and enable users to share health inform- ation with people involved in their healthcare. Our RMA (built on Diaspora) provided basic security and privacy mechanisms for access control and authentication. Allowing fine-grained control, the appropriate access can be granted for actors to access the applic- ation according to their respective viewpoints.

The mapping from the carer network in the mHealth sceanario to the fitness network

(trainer, coach, buddy and client) in the wellbeing scenario, can be realised via an on- line social network. How the relationships modelled in the system relate to an mHealth

scenario will be shown in Section 5.4.2. We choose to investigate our implemented OSMP platform using Fitbit activity tracker devices [43] as a measurement system in our proxy wellbeing scenario for convenience, but a real medical application would use a different measurement system (e.g. wearable sensors collecting health bio-data). Our RMA used the Fitbit API to access the data from the Fitbit server and stored the data on the server locally. Each actor had a different viewpoint of monitored data depending on their roles in the fitness/carer network, as well as a different level of control over the application. It should be noted that in an mHealth scenario, the viewpoint is controlled by an access control system configured by the healthcare provider, with appropriate consideration of patients personal privacy preferences, national laws, etc. Definitely, there would be a com- plexity from considering ethics, regulations and laws, but this would have to be considered within the context of each medical scenario in which such a system was applied.

Themeasurement systemconsists of sensors and systems to monitor and store personal bio-data. The Fitbit Charge HR activity tracker (Figure 4.2 and Table 4.1) was used as a measurement system in our study. Fitbit is a commercial activity tracker, consisting of a wearable device and a web portal. The device measured the steps walked, sleep data and heart rate for a wearer, and uploaded to the Fitbit server.

Figure 4.2: Fitbit Charge HR wristband device used as a measurement system in our study to measure steps walked, sleep data and heart rate. (Image from http://www.fitbit.com/) Since our application had web-based access, an additional mobile application was im- plemented in order to receive mobile push notifications in addition to in-platform RMA notifications. This was becuase mobile OSes today (iOS and Android) do not support directly web-based notifications, and so platform-specific notification systems had to be created.

Radio transceiver Bluetooth 4.0 Battery 5 days

Water resistance sweat, rain and splash proof Sensors Optical heart rate monitor

3-axis accelerometer Altimeter

Track Heart rate Workouts Distance Calories burned Floors climbed Active minutes Steps

Automatic sleep monitor Table 4.1: Summary of Fitbit Charge HR specification.