Humanitcare

Vitalera has implemented a cloud platform for the telemonitoring of chronic patients through portable medical devices and an alarm-based system that issues health alerts when a patient's biomedical measurement is outside a predefined clinical range. The platform frees doctors and caregivers from reviewing individual patient data for abnormalities, speeding up the decision-making process and reducing hospital visits. With this study we intend to validate the efficacy of the app for patients and digital platform for medical professionals, evaluating the increase in the quality of life of patients and measuring the reduction in the incidence of the main critical events of HF. In addition, the study will validate the new API interoperability standards and platform architecture and will assess the usability of the platform by delivering satisfaction questionnaires to patients and professionals at the end of the study.
This study is being carried out within the framework of a European project promoted by the European Innovation Council (EIC).

In the present project, we propose to run an observational study in order to create a huge dataset with telemonitoring data from heart failure (HF) patients. The dataset will contain physiological measurements, socio-demographic data, risk factor information, medication tracking, symptomatology, clinical events and health-related questionnaire answers from each patient. Furthermore, health-related alarms will be delivered to the medical professionals whenever a measure from a patient is out of a predefined clinical range. These alarms and its defined level of relevance (indicated by the medical professionals) will also be Included in the dataset. With the annotated dataset we will be able to implement and train Machine Learning (ML) models that will improve the alarm-based system by making it more robust, trustworthy and reliable.

The objective of this study is to analyze the physiological patterns of two groups of patients, one control and one with anxiety disorder and alcoholic abuse disorder using sensor data from mobile devices and wearables. This data will be compared to the data presented by three clinical questionnaires: State-trait Anxiety Inventory (STAI), the Alcohol Use Disorders Identification Test (AUDIT), and the Beck's Depression Inventory (BDI-II) in order to determine the feasibility of remote collected data.