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

2022Journal article Open access
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dc.contributor.authorLemos, Raquel
dc.contributor.authorAreias-Marques, Sofia
dc.contributor.authorFerreira, Pedro
dc.contributor.authorO’Brien, Philip
dc.contributor.authorBeltrán-Jaunsarás, María Eugenia
dc.contributor.authorRibeiro, Gabriela
dc.contributor.authorMartín, Miguel
dc.contributor.authordel Monte-Millán, María
dc.contributor.authorLópez-Tarruella, Sara
dc.contributor.authorMassarrah, Tatiana
dc.contributor.authorLuís-Ferreira, Fernando
dc.contributor.authorFrau, Giuseppe
dc.contributor.authorVenios, Stefanos
dc.contributor.authorMcManus, Gary
dc.contributor.authorOliveira-Maia, Albino J.
dc.date.accessioned2023-01-17T16:03:51Z
dc.date.available2023-01-17T16:03:51Z
dc.date.issued2022
dc.description.abstractBackground: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. 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 technolo‑ gies. 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 pro‑ spective 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 fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion 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 ispt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLemos, R., Areias-Marques, S., Ferreira, P., O’Brien, P., Beltrán-Jaunsarás, M. E., Ribeiro, G., Martín, M., del Monte-Millán, M., López-Tarruella, S., Massarrah, T., Luís-Ferreira, F., Frau, G., Venios, S., McManus, G., & Oliveira-Maia, A. J. (2022). A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol. BMC Psychiatry, 22(1), 1–13. https://doi.org/10.1186/s12888-022-04446-5pt_PT
dc.identifier.doi10.1186/s12888-022-04446-5pt_PT
dc.identifier.issn1471244X
dc.identifier.urihttp://hdl.handle.net/10400.12/8918
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBioMed Central Ltd.pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCancerpt_PT
dc.subjectDepressionpt_PT
dc.subjectSurvivorshippt_PT
dc.subjectFederated learningpt_PT
dc.subjectArtifcial intelligencept_PT
dc.subjectWearablespt_PT
dc.subjectRemote assessmentpt_PT
dc.subjectQuality of lifept_PT
dc.titleA prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocolpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceUnited Kingdompt_PT
oaire.citation.issue1pt_PT
oaire.citation.titleBMC Psychiatrypt_PT
oaire.citation.volume22pt_PT
person.familyNameBaptista de Lemos Guerra de Oliveira
person.familyNameRibeiro
person.familyNameFrau
person.familyNameMcManus
person.givenNameRaquel Maria
person.givenNameGabriela
person.givenNameGiuseppe
person.givenNameGary
person.identifier.ciencia-id4C1D-F1CC-B987
person.identifier.ciencia-idE615-EE57-0F84
person.identifier.orcid0000-0003-1221-915X
person.identifier.orcid0000-0001-6205-0714
person.identifier.orcid0000-0003-3862-4815
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationbfa05e5c-6e5c-4d39-bc72-bf1150e9c67e
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relation.isAuthorOfPublication.latestForDiscovery3abb4ab0-229d-4e16-9e46-351cff61b635

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