ArticleJMIR rehabilitation and assistive technologies2018
Capturing Expert Knowledge for the Personalization of Cognitive Rehabilitation: Study Combining Computational Modeling and a Participatory Design Strategy.
Article in JMIR rehabilitation and assistive technologies, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A comparison of two personalization and adaptive cognitive rehabilitation approaches: a randomized controlled trial with chronic stroke patients.Journal of neuroengineering and rehabilitation · 2020Trial
- Efficacy of Technology-Based Cognitive Rehabilitation Tools for Cancer-Related Cognitive Impairment in Non-CNS Cancer Patients: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Virtual Reality-Based Cognitive and Physical Interventions in Cognitive Impairment: A Network Meta-Analysis of Immersion Level Effects.Behavioral sciences (Basel, Switzerland) · 2025Review
- Ecologically valid virtual reality-based technologies for assessment and rehabilitation of acquired brain injury: a systematic review.Frontiers in psychology · 2023Review
- NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.Frontiers in neurology · 2023Article
- Toward Personalized Web-Based Cognitive Rehabilitation for Patients With Ischemic Stroke: Elo Rating Approach.JMIR medical informatics · 2021Article
- Comparing adaptive tablet-based cognitive training and paper-and-pencil cognitive training: a pilot randomized controlled trial with community-dwelling stroke survivors.International journal of clinical and health psychology : IJCHPArticle
- Enhancing treatment outcomes of Alcohol Use Disorder patients through ICT-based cognitive training tools: a randomized controlled trial.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCognitive 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.
objectiveIn this study, we aimed to present a framework for the creation of personalized cognitive rehabilitation tasks based on a participatory design strategy.
methodsWe 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.
resultsAfter 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).
conclusionsOur 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.