Background and Aims

The use of an online study-management system can help to ease the burden of both participation in, and administration of, mHealth interventions. We describe the benefits and challenges of using such a platform to manage an intervention (FullFlow Project) involving both patients and their providers in the testing of an mHealth data-sharing system.

Methods

Our remote study-management platform consists of: a website used to monitor status and message the participants, a local server for automatic data-collection and analysis (Piwik, now Matomo) and an open source survey tool (LimeSurvey). Patient recruitment was initiated through health providers and continued through the platform. Two researchers and one developer administrated the study.

Results

The benefits of this platform included security and efficiency in distributing study-information and messages, as well as supporting participants from a single platform, based on open-source systems. For example, if a participant was not actively engaged in the intervention, we could then send messages specific to their situation. In the platforms’ current implementation, we have experienced three main challenges: 1-Participant follow-up requires manual tracking and initiation of messaging; 2-Data-collection requires manual review of data and interaction logs, from separate sources; and 3-Data-analysis requires specific programming to combine the differently structured output from each data source.

Conclusions

Future improvements to the system can include automation of tasks and additional software that can facilitate the organization of these data for analysis. For example, automatic merging of data-sources and generation of simple reports would make the system more efficient, which is especially important for mHealth interventions.

Background and Aims

A teamwork including parents and people involved in the child’s life is often required in the management of diabetes for children with type 1 diabetes. In particular, family has to pay close attention to the child’s blood glucose level and insulin injections and teach the child to manage his/her diabetes.

To assist in this process, we developed a Furby toy-based system to motivate, educate and keep track of the child’s diabetes.

Methods

The Furby toy-based system has been designed using personas and different scenarios. The main issues were represented: 1) educational part, that teaches children when to take insulin or eat with parental supervision; 2) remote control of blood glucose by parents/babysitter; 3) ease the psychological discomfort of having diabetes by using the toy to play with his/her friends.

Results

The original Furby’s toy electronics were replaced so it could be controlled wirelessly by a Raspberry Pi 3 (Figure 1).

Based on blood glucose values received from Nightscout, Furby expresses different emotions (e.g., happiness, sadness) and announces its change.

Blood glucose values monitoring, text-based games (e.g., play hide and seek, learning the numbers) and control of the toy are possible using a smartphone via the chatbot component.

The Furby toy-based system includes a voice-based interaction/conversation using Alexa and the integrated microphone.

Conclusions

The implemented solution enables future expandability and modularity. It represents an example of how technological solution can be used to ease daily diabetes challenges in families.