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Capturing expert knowledge for the personalization of cognitive rehabilitation: study combining computational modeling and a participatory design strategy

2018Journal article Open access
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dc.contributor.authorFaria, Ana Lúcia
dc.contributor.authorPinho, Maria Salomé
dc.contributor.authorBermúdez i Badia, Sergi
dc.date.accessioned2019-12-11T10:27:47Z
dc.date.available2019-12-11T10:27:47Z
dc.date.issued2018
dc.description.abstractBackground: Cognitive impairments after stroke are not always given sufficient attention despite the critical limitations they impose on activities of daily living (ADLs). Although there is substantial evidence on cognitive rehabilitation benefits, its implementation is limited because of time and human resource’s demands. Moreover, many cognitive rehabilitation interventions lack a robust theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and-pencil tasks, parameterized according to patients' needs. Objective: In this study, we aimed to present a framework for the creation of personalized cognitive rehabilitation tasks based on a participatory design strategy. Methods: We selected 11 paper-and-pencil tasks from standard clinical practice and parameterized them with multiple configurations. A total of 67 tasks were assessed according to their cognitive demands (attention, memory, language, and executive functions) and overall difficulty by 20 rehabilitation professionals. Results: After assessing the internal consistency of the data—that is, alpha values from .918 to .997—we identified the parameters that significantly affected cognitive functions and proposed specific models for each task. Through computational modeling, we operationalized the tasks into their intrinsic parameters and developed a Web tool that generates personalized paper-and-pencil tasks—the Task Generator (TG). Conclusions: Our framework proposes an objective and quantitative personalization strategy tailored to each patient in multiple cognitive domains (attention, memory, language, and executive functions) derived from expert knowledge and materialized in the TG app, a cognitive rehabilitation Web tool.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFaria, A. L., Pinho, M. S., & Bermúdez i Badia, S. (2018). Capturing expert knowledge for the personalization of cognitive rehabilitation: study combining computational modeling and a participatory design strategy. JMIR rehabilitation and assistive technologies, 5(2), e10714.pt_PT
dc.identifier.doi10.2196/10714pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/2644
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherJMIR Publicationspt_PT
dc.subjectStroke rehabilitationpt_PT
dc.subjectAttentionpt_PT
dc.subjectMemorypt_PT
dc.subjectExecutive functionpt_PT
dc.subjectLanguagept_PT
dc.subjectCognitionpt_PT
dc.subjectCommunity-based participatory researchpt_PT
dc.subjectPatient-specific modelingpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleCapturing expert knowledge for the personalization of cognitive rehabilitation: study combining computational modeling and a participatory design strategypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F50009%2F2013/PT
oaire.citation.startPagee10714pt_PT
oaire.citation.titleJMIR Rehabilitation and Assistive Technologiespt_PT
oaire.citation.volume5(2)pt_PT
oaire.fundingStream5876
person.familyNamedos Santos Faria
person.familyNamePinho
person.familyNameBermúdez i Badia
person.givenNameAna Lúcia
person.givenNameMaria Salomé
person.givenNameSergi
person.identifier239789
person.identifier.ciencia-idF617-BFEE-77E0
person.identifier.ciencia-idF914-D116-0923
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.orcid0000-0001-5904-0304
person.identifier.orcid0000-0001-9786-2819
person.identifier.orcid0000-0003-4452-0414
person.identifier.ridC-8681-2018
person.identifier.scopus-author-id6506360007
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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