Patient-Centered Serious Games for Remote Cognitive Training in Older Adults with Mild Cognitive Impairment
Patient-Centered Serious Games for Remote Cognitive Training in Older Adults with Mild Cognitive Impairment
批准号:
10190616
负责人:
Jean Francois Daneault
金额:
$43.68万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-04-15 至 2025-03-31
关键词:
Activities of Daily LivingAdoptedAlgorithmsAreaAttentionCaregiversClinicClinicalCognitiveComplementControl GroupsDataData SetDiseaseDisease ManagementDisease ProgressionElderlyEnvironmentFeedbackFrequenciesFutureGoalsHealthHealth TechnologyHealth care facilityHealth systemHealthcareHomeHumanImpaired cognitionIndividualIndustrializationInformaticsInterventionInvestigationLeadLong-Term CareMachine LearningMeasuresOutcomeParticipantPatient EducationPatientsPerformancePersonsPhysical activityPlayProtocols documentationRandomizedRandomized Controlled TrialsRegimenRehabilitation therapyResearchResearch PersonnelRoleSelf AdministrationShort-Term MemorySingle-Blind StudySymptomsSystemTabletsTechnologyTelephoneTherapeuticTimeTrainingVideo Gamesbasebehavior changecaregiver educationchronic strokeclinical investigationclinical outcome measurescognitive functioncognitive performancecognitive trainingcommunity settingcompliance behaviorcostdesigndigitalfunctional disabilitygroup interventionhemiparesisimprovedinsightinterestmHealthmild cognitive impairmentnormal agingnovelpatient engagementpatient orientedpersonalized managementprogramsprototypepsychological distressrecruitrural areaselective attentionside effectstroke survivortooltranslational impacttreatment as usualunderserved areausability
中文摘要
项目总结/文摘
英文摘要
Project Summary/Abstract
Mild Cognitive Impairment (MCI) is characterized by cognitive decline greater than expected from normal aging,
which does not yet significantly interfere with the performance of activities of daily living. The therapeutic goal
for MCI is to slow down the course of the disease progress and reduce the impact of clinical symptoms. There
has also been a particular emphasis on non-pharmacological interventions because they can slow disease
progression and are low cost, non-invasive, safe, and have no adverse side effects.
Serious games have received great interests among researchers and clinicians as a promising, low-cost, non-
pharmacological intervention tool that can help assess and train patients’ cognitive function in a variety of
environments (e.g., in/outside the clinic). Serious games in healthcare represent digital applications focusing on
educating, informing, and enhancing patients’ health by leveraging the entertainment components of video
games. However, currently available solutions require substantial involvement of trained caregivers and
clinicians to motivate patients to adhere to the intervention protocol, which acts as the key barrier to the
widespread implementations of serious game-based interventions.
To bridge these gaps, this project aims to develop a mobile-health (mHealth) technology designed to encourage
the use of, and thereby maximizing the compliance to, serious game-based training in individuals with MCI
outside the clinic. We plan to employ a tablet-based application – namely Neuro-World – composed of six serious
games designed to stimulate working memory and selective attention, which is developed by our research team’s
industrial partner. Fully leveraging the system’s ability to provide clinically meaningful insights related to patients’
cognitive performance from their gameplays, their progress over time, and their compliance behaviors (e.g., use
frequency and duration), it is our ultimate research goal to develop an optimal, human-centered serious game
platform that can motivate patients’ to better adhere to the technological regimen.
To accomplish this research goal, Aim 1 will focus on evaluating the preliminary efficacy of our serious games
as a training tool to improve cognitive function. We will perform a small single-blind randomized controlled trial
in 50 individuals with MCI who will be randomized either to the intervention group or the control group. In Aim 2,
we will develop machine learning-based algorithms to derive clinically meaningful information regarding cognitive
function from the game performance. Aim 3 will finally investigate the optimal design of the mHealth system to
maximize patients’ compliance with serious game-based training via human-centered design approaches.
We believe that outcomes of this project will open a new door leading to previously unexplored datasets and
understanding of patient-technology interactions to promote positive behavior changes to enable self-
administered, serious game-based cognitive training, which can form the basis of a wide range of future
investigations of hemiparesis rehabilitation and personalized disease management.
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