Descripció del projecte
Behavioural addictions are behaviours adopted which provide short-term reward which persist, despite knowledge of adverse consequence. Existing therapies are standardised ones which do not provide extra support for patients outside regular psychotherapy sessions. If so, therapies are standardised and are not able to model patient’s behaviour. Different solutions are presented on the market providing psychological support; nonetheless, there are no tools which involve the use of artificial intelligence (AI) nor machine learning (ML) in order to model patient’s behaviour.This allows better personalisation of the app and of the therapy and a more controlled and therapeutic path. Moreover, the existence of backoffice data allows a better management of the patient’s symptoms, from the patient, therapist and caregiver.
We aim to adapt and expand upon our already developed TCApp, currently used for providing psychological support for mental health conditions. Our current digital platform consists of daily records of activities, emotions and meals, based on which a ML algorithm which has been developed to provide a personalised CBT therapy based on positive thinking and reinforcement learning. Our app allows self-management and monitoring of the patient’s psychological therapy from therapists, caregivers and parents. Furthermore, engagement and effectiveness is enhanced, due to positive reinforcement. We aim at integrating biofeedback data extracted from wearable devices. Furthermore, we aim at utilising Brainfit app in order to collect neurofeedback data such as neurotiming and synchronisation of electric signals and brain waves. This will allow the tracking of brain electrical activity within a temporal resolution and understand patient’s brain regulation. Based on this, we plan to develop a simple unsupervised algorithm of either a regressive or clustering nature. Once a considerable amount of data will be collected, we plan to shift to the development of a labelled, supervised algorithm which will label our data with classifers. This algorithm will be trained to detect specific patient’s needs and therefore will provide immediate personalised psychological support to the patient. Clinical efficacy will be tested and provided by neuroscientific (MRI) data.
The aim of the project is clinically validating HealthApp product through and backed up by psychological and neuroscientific clinical trials and test possible commercialisation options in healthcare facilities both at a national and international level. This will be reached through the development ofs state-of-the-art technology which aims at providing and monitoring psychological support through clinically proven psychological therapies.
