MATER · Monitoring mentAl healTh in brEast canceR

Monitoring mentAl healTh in brEast canceR

In 2020, in Europe (EU-27), 355,457 women were newly diagnosed with breast cancer and 91,826 women died from it. With an estimated lifetime risk of developing breast cancer of 1 in 7, breast cancer is a public health issue with a heavy impact on patients’ mental health due to the diagnosis implications, treatment or quality-of-life (QoL) interference of the pathology. Patients thus have the double burden of both cancer treatments and/or after-effects, and mental health disorders. In order to enhance patients’ and survivors’ QoL – which is one of the listed priorities of the Europe’s Beating Cancer Plan, to measure adherence to their treatment, and to prevent relapse or the emergence of co-morbidity, these patients require a personalized and regular medical follow-up. In line with the EU4Health Programme and the e-health European policy, an innovative way to collect these symptoms under a patients’ usual living conditions is the Ecological Momentary Assessment (EMA). EMA consists in measuring parameters related to these symptoms on a very regular basis and under ecological conditions. This collection can be done through questionnaires, applied e.g. via smartphone applications or animated virtual assistants, but also using voice. Indeed, voice is related to the physiological state of the speaker; can be implemented in passive data collection (i.e., data collection that does not require the active participation of the subject – e.g. during a phone call); does not require large computational resources; and is robust to noise, allowing its implementation in diverse environments. Moreover, voice is easily captured through smartphones, and it is estimated that 80% of the world population has access to it : the deployment of this measurement tool is already effective in the general population. In addition, in order to model the interactions between the different symptoms related to mental health impairment associated with cancer, recent works have used symptom networks. Introduced in 2013 by Borsboom , symptom networks allow to very efficiently visualize the relations between symptoms (e.g. partial correlation) using graphs and to identify the symptoms responsible for the maintenance of a healthy or pathological state (central symptom). Putting together symptoms from different syndromes or disorders into the same graph allows the identification of bridge symptoms , implied in the development of comorbidities, which are of particular interest for EMA. An example of such a network is proposed in Figure 1A. The objective of the MATER project is to take benefits of both the use of voice as a means of measuring symptoms and symptom networks as tools for modeling their interactions, in order to automatically estimate mental health issues in women with breast cancer.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз