Diabetes mellitus has high prevalence in the ageing population and is often accompanied by other comorbidities, such as Alzheimer's disease, and general disabilities, such as poor eyesight. These comorbidities have redefined ways in which patients use mHealth apps, including diabetes apps. The latter have proven benefits for monitoring blood glucose levels and insulin tracking in the general population. In this paper, we analyse a diabetes monitoring app DeStress Assistant (DeSA), which was developed as a part of an EU project and tested in a hospital setting. Due to the increasing number of older adults, we wanted to ensure the app was suitable for that demographic. Based on a number of supervised tests, we show that the app, which was developed with the help of workshops and feedback from tech-savvy patients and clinicians, is difficult to use by elderly users. We demonstrate that with a small number of changes it is possible to raise the usability of the app in a number of categories. We summarise the lessons learned in the discussion. Our findings demonstrate that special care needs to be taken when developing mHealth apps for the elderly population.
Clustering in vehicular ad hoc networks is an effective approach to make dynamic wireless vehicular sensor networks more manageable and stable. To make vehicle clustering applicable everywhere regardless of the provided infrastructure, vehicles must rely only on themselves and must not take any supporting services, such as location or external communication services, for granted. In this paper, we propose a new clustering metric and a clustering algorithm with multihoming support. It relies only on the vehicle's ability to send and receive wireless packets which identify the vehicle relationship. Clusters are created with redundant connections between nodes to increase the communication reliability in case of topological changes and the cluster creation process is also inverted compared to other algorithms. The presented solution is verified and compared to MOBIC with the use of ns-3 and SUMO simulation tools. Simulation results have confirmed the expected behavior and show that our algorithm achieves better node connectivity and cluster stability than the former.
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