A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol

Raquel Lemos, Sofia Areias-Marques, Pedro Ferreira, Philip O’Brien, María Eugenia Beltrán-Jaunsarás, Gabriela Ribeiro, Miguel Martín, María del Monte-Millán, Sara López-Tarruella, Tatiana Massarrah, Fernando Luís-Ferreira, Giuseppe Frau, Stefanos Venios, Gary McManus, Albino J. Oliveira-Maia

Research output: Contribution to journalArticlepeer-review

2 Downloads (Pure)

Abstract

Background: Depression is a common condition among cancer patients, across several points in the disease trajectory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technologies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal prospective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. Trial registration: Trial ID: ISRCTN10423782. Date registered: 21/03/2022.

Original languageEnglish
Article number817
JournalBMC Psychiatry
Volume22
Issue number1
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Artificial intelligence
  • Cancer
  • Depression
  • Federated learning
  • Quality of life
  • Remote assessment
  • Survivorship
  • Wearables

Fingerprint

Dive into the research topics of 'A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol'. Together they form a unique fingerprint.

Cite this