Capturing Expert Knowledge for the Personalization of Cognitive Rehabilitation: Study Combining Computational Modeling and a Participatory Design Strategy

Ana Lúcia Faria; Maria Salomé Pinho; Sergi Bermúdez I Badia

doi:10.2196/10714

Capturing Expert Knowledge for the Personalization of Cognitive Rehabilitation: Study Combining Computational Modeling and a Participatory Design Strategy

JMIR Rehabil Assist Technol. 2018 Dec 6;5(2):e10714. doi: 10.2196/10714.

Authors

Ana Lúcia Faria^{1

2}, Maria Salomé Pinho^{2

3}, Sergi Bermúdez I Badia^{1

4}

Affiliations

¹ Madeira Interactive Technologies Institute, Funchal, Portugal.
² Faculdade de Psicologia e de Ciências da Educação, Universidade de Coimbra, Coimbra, Portugal.
³ Laboratório de Memória, Linguagem e Funções Executivas, Coimbra, Portugal.
⁴ Centro de Ciências Exatas e da Engenharia, Universidade da Madeira, Funchal, Portugal.

PMID: 30522994
PMCID: PMC6318149
DOI: 10.2196/10714

Abstract

Background: 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.

Keywords: attention; cognition; community-based participatory research; executive function; language; memory; patient-specific modeling; stroke rehabilitation.

©Ana Lúcia Faria, Maria Salomé Pinho, Sergi Bermúdez i Badia. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 06.12.2018.