Search

Abstract The young adult can experience psychological difficulties related to the delicate phase of life he/she is going through. Many of them show psychological difficulties but only a small portion receive the needed care. In this regard, internet-based interventions represent an important resource. MindBlooming is a seven-week intervention delivered through a mobile application for university students with mild to moderate psychological difficulties. The application is the result of the interdisciplinary work between the Department of Psychology and the Department of Informatics, Systems and Communication of the University of Milan-Bicocca. The intervention focused on symptoms of depression, anxiety, sleep problems, self-destructive thoughts, job- and study-related stress and burnout, and chronic pain. It is based on different approaches (multi-approach), primarily psychoeducation, Cognitive-Behavioral Treatment (CBT), and third-wave CBT. During the first pilot study, the intervention consisted of a 7-week treatment on two problematic areas according to each students’ personal needs, identified through an initial assessment. The achieved results are promising especially in terms of interest shown by students. However, we encountered technical problems that hampered the pilot study. A second pilot will be performed to further test the application. The MindBlooming project will be discussed in light of the need for further multidisciplinary research that confirms how biomarkers can be sensitive to non-pharmacological internet-based interventions. Keywords cognitive-behavioral treatment; internet-based intervention; university students. Disclosure of Interest None Declared

To date, at least 2.41 billion people with Non-Communicable Diseases (NCDs) are in need of rehabilitation. Rehabilitation care through innovative technologies is the ideal candidate to reach all people with NCDs in need. To obtain these innovative solutions available in the public health system calls for a rigorous multidimensional evaluation that, with an articulated approach, is carried out through the Health Technology Assessment (HTA) methodology. In this context, the aim of the present paper is to illustrate how the Smart&TouchID (STID) model addresses the need to incorporate patients’ evaluations into a multidimensional technology assessment framework by presenting a feasibility study of model application with regard to the rehabilitation experiences of people living with NCDs. After sketching out the STID model’s vision and operational process, preliminary evidence on the experiences and attitudes of patients and citizens on rehabilitation care will be described and discussed, showing how they operate, enabling the co-design of technological solutions with a multi-stakeholder approach. Implications for public health are discussed including the view on the STID model as a tool to be integrated into public health governance strategies aimed at tuning the agenda-setting of innovation in rehabilitation care through a participatory methodology.

Field monitoring techniques can collect data about the behavior of software applications while running in the field, with real users and real data. Developers can exploit the information extracted from the field to timely improve, tune, and fix their systems, anticipating feedback by users. It is, however, challenging to extract a relevant amount of information from field executions without introducing significant overhead. This paper addresses this challenge by studying how to inexpensively trace data in-memory while postponing save operations to idle time, so that the operations requested by users are exposed to a negligible overhead only. In particular, this paper presents delayed saving, a technique that efficiently traces references to objects, opportunistically saving information only when the monitored application is not serving any user request. Storing references and postponing save operations may introduce inaccuracy in the collected data, that is, a lately saved object might be in a different state compared to the state of the object at the time it was traced. Our evaluation shows that the level of inaccuracy introduced by delayed saving is limited compared to its efficiency and low intrusiveness.

In recent years, research on techniques to identify and classify activities of daily living (ADLs) has significantly grown. This is justified by the many application domains that benefit from the application of these techniques, which span from entertainment to health support. Usually, human activities are classified by analyzing signals that have been acquired from sensors. Inertial sensors are the most commonly employed, as they are not intrusive, are generally inexpensive and highly accurate, and are already available to the user because they are mounted on widely used devices such as fitness trackers, smartphones, and smartwatches. To be effective, classification techniques should be tested and trained with datasets of samples. However, the availability of publicly available datasets is limited. This implies that it is difficult to make comparative evaluations of the techniques and, in addition, that researchers are required to waste time developing ad hoc applications to sample and label data to be used for the validation of their technique. The aim of our work is to provide the scientific community with a suite of applications that eases both the acquisition of signals from sensors in a controlled environment and the labeling tasks required when building a dataset. The suite includes two Android applications that are able to adapt to both the running environment and the activities the subject wishes to execute. Because of its simplicity and the accuracy of the labeling process, our suite can increase the number of publicly available datasets.