Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioTlDM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.

MyDiabetes is a mobile application targeted for the management of type I diabetes. A considerable challenge faced by diabetes’ management applications is user adherence and motivation. In this article we will describe the different gamification techniques implemented in the MyDiabetes mobile application in order to tackle the mentioned challenges and increase the number of daily user records. To evaluate the success of the implemented gamification techniques, a survey was conducted at the endocrinology service of S. João’s Hospital. Twenty three participants, diabetics type I, participated in the survey. Different types of users’ motivations resulted in contrasting opinions about particular gamification elements. Nonetheless, the implemented gamification was, in general, well accepted. The gamification elements with the lowest classification were streaks and social comparison of points. Even so, these elements obtained an acceptance rate of 83%. There was a common preference for elements that promote better glycaemic management. Classic gamification elements that promote points, levels and objective completion were not disregarded by the inquired patients, but were given a secondary roll in usage motivation. Although promising, it was not possible to determine the long-term effectiveness of the implemented gamification. Further trials are planned to verify the impact of gamification on long-term user adherence.

Covidmonitor is a crowdsensing tool to support epidemologists and public health authorities in monitoring the covid-19 pandemic. The tool collects data to support transdisciplinary studies aiming at improving the knowledge of the pandemic evolution as well as monitor the citizens' behaviour and mental health. Covidmonitor leverages a previously existing mobile crowdsensing platform, SenseMyCity, adapted in collaboration with epidemology, public health and psychology researchers. Our biggest challenge was to identify the relevant metrics for the target trans-disciplinary studies and map them to collectable data. Covidmonitor explores the concept of citizens as probes to sample collective behaviour. The mobile application launches questionnaires about hygiene practices, use of personal protection equipment, health and emotional state. The questionnaires are triggered by different logic, adequate to the multi-dimensional perspectives of the target studies. Covidmonitor also seamlessly collects relevant mobility data without significant battery consumption. Finally, it enables voluntary sharing of location and symptom history, to facilitate tracing in case of infection. The tool considers user privacy and data minimisation by design, and is currently under preliminary scrutiny of the data protection regulator in Portugal.

Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android.