课题基金 / 基金详情

Novel Approaches to Identifying and Engaging Disadvantaged Patients with Alzheimer’s Disease (AD) in Clinical Research

Novel Approaches to Identifying and Engaging Disadvantaged Patients with Alzheimer’s Disease (AD) in Clinical Research
识别弱势阿尔茨海默病 (AD) 患者并使之参与临床研究的新方法
批准号:
10448269
负责人:
Andrea L Gilmore-Bykovskyi
金额:
$21.71万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2023-05-31
关键词:
AddressAlzheimer disease detectionAlzheimer disease screeningAlzheimer&aposs DiseaseAlzheimer&aposs disease modelAlzheimer&aposs disease patientAlzheimer&aposs disease related dementiaAmbulatory CareAmbulatory Care FacilitiesAreaAttitudeAwardBiologicalCaregiversCaringChildClinicalClinical DataClinical ResearchClinical TrialsClinical Trials DesignCodeCognitive deficitsDataDevelopmentDevelopment PlansDiagnosisDiagnosticDisadvantagedDiseaseDisease ProgressionDoctor of PhilosophyElderlyElectronic Health RecordEnrollmentEnvironmentExclusionFacultyFocus GroupsGoalsGoldHealthHealth Services AccessibilityHealthcareImpaired cognitionImprove AccessIncidenceIndividualInfrastructureInstitutionInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)InterventionInterviewKnowledgeLeadLeadershipMeasuresMemoryMentorsMethodsModelingMoodsNatural Language ProcessingNursesOutpatientsPatientsPerformancePersonsPhenotypePrevalenceReadinessRegistriesResearchResearch ActivityResearch PersonnelRiskRisk FactorsScientistSensitivity and SpecificityServicesSymptomsTechniquesTerminologyTestingTextTimeTrainingValidationVulnerable PopulationsWorkacceptability and feasibilityacute carebasebehavior changecare episodecareercareer developmentcognitive testingcohortcommunity settingdesigndisadvantaged backgrounddisadvantaged populationdisease disparitydisease phenotypedisorder riskeffectiveness evaluationethnic minorityhealth disparityhelp-seeking behaviorhigh riskimprovedintervention mappingmachine learning methodmembermortalitynovelnovel strategiespilot testprogramsprotective factorsracial and ethnicracial minorityrecruitscreeningstatistical and machine learningstructured datasuccesssymptom managementunstructured datawillingness

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中文摘要
翻译
项目概要/摘要 尽管阿尔茨海默病(AD)的患病率、发病率、诊断、治疗方面存在差异, 和死亡率方面,来自弱势背景的个人(例如少数种族/族裔)所占比例不成比例 在 AD 临床研究中代表性不足。当前 AD 研究的招募方法主要是确定 来自门诊诊所和社区环境的患者,或已有诊断的患者。依赖这些 招聘方法可能会对弱势群体的参与造成障碍,因为他们更 可能缺乏有关 AD 服务的信息、未被诊断并且获得门诊护理的机会有限。然而, 为了实现国家目标,迫切需要让弱势群体更多地参与 AD 研究。 广告研究。在急性护理环境中进行有针对性的 AD 筛查和量身定制的招募具有巨大的潜力 解决这些差距,因为弱势群体往往依靠这些环境来满足其健康需求。这个 K76 提案旨在为 Gilmore-Bykovskyi 博士提供培训,他是一位接受过老年科培训的护士和 AD 专家 症状管理以及作为独立临床医生科学家成功所需的培训,重点关注 改善 AD 识别,促进更多人参与研究并获得有效护理和 疗法,特别针对高危弱势群体。该组织的总体目标是 拟议的研究是设计筛选和招聘方法,以识别和吸引 处于不利地位的 AD 患者/护理人员及其亲生子女在急性护理机构的研究中。的 提案包括使用 EHR 验证 AD 的电子健康记录 (EHR) 表型模型 初步研究(目标 1)中确定的临床数据以及该模型的性能规范 弱势群体(目标 1a)。为了解决急性护理环境中的招聘问题,混合 方法策略将为适合急症护理的定制招募方法的设计提供信息 (目标 2)将在 30 名 AD 患者/护理人员中进行试点,以确定其可行性和可接受性 对注册试验注册意愿的初步影响(目标 2a)。作为一名初级教员 为早期研究人员提供广泛支持并在 AD 差异方面提供重要基础设施的机构 和 EHR 表型分析,Gilmore-Bykovskyi 博士处于完成拟议研究的理想环境中 并接受与她的职业目标相关的高级培训。 Gilmore-Bykovskyi博士的职业发展规划 整合了以下领域的教学和实践培训、个人指导和指导研究活动:1) 临床试验设计,2) 先进的统计和机器学习技术,3) 急症护理研究,4) AD 健康 差异,5) 招募和保留弱势群体,以及 6) 领导力。本次拟定奖项 解决基本差距和障碍,以提高弱势群体参与AD研究的程度 同时提供对 Gilmore-Bykovskyi 博士领导独立研究至关重要的培训和指导研究 临床 AD 研究的研究计划。
英文摘要
PROJECT SUMMARY/ABSTRACT Despite well-documented disparities in Alzheimer’s disease (AD) prevalence, incidence, diagnosis, treatment, and mortality, individuals from disadvantaged backgrounds (e.g. racial/ethnic minorities) are disproportionately under-represented in clinical AD research. Current recruitment methods for AD research predominantly identify patients from outpatient clinics and community settings, or with pre-existing diagnoses. Reliance on these recruitment approaches may create barriers to participation for disadvantaged individuals as they are more likely to lack information about AD services, be undiagnosed and have limited access to outpatient care. Yet, greater enrollment of disadvantaged individuals into AD studies is critically needed to achieve national goals for AD research. Targeted AD screening and tailored recruitment within acute care settings has strong potential to address these gaps, as disadvantaged individuals often rely on these settings to meet their health needs. This K76 proposal is designed to provide Dr. Gilmore-Bykovskyi, PhD, a geriatric trained nurse and expert in AD symptom management with the training required for success as an independent clinician-scientist focused on improving AD identification to promote greater participation in research and access to effective care and therapies, specifically targeting high-risk disadvantaged populations. The overarching objective of the proposed research is to design screening and recruitment approaches for identifying and engaging disadvantaged AD patients/caregivers and their biological children in research from acute care settings. The proposal consists of validation of an electronic health record (EHR) Phenotype Model for AD using EHR clinical data identified in preliminary studies (Aim 1), and specification of this Model for performance among disadvantaged individuals (Aim 1a). To address recruitment from acute care environments, mixed methods strategies will inform the design of tailored recruitment approaches appropriate to acute care (Aim 2) which will be piloted with 30 AD patients/caregivers to determine their feasibility, acceptability and preliminary impact on willingness to enroll in a Trial Registry (Aim 2a). As a junior faculty member at an institution with extensive support for early stage investigators and significant infrastructure in AD disparities and EHR Phenotyping, Dr. Gilmore-Bykovskyi is in an ideal environment to complete the proposed research and pursue advanced training relevant to her career goals. Dr. Gilmore-Bykovskyi’s career development plan integrates didactic and practical training, individual mentoring and mentored research activities in the areas of 1) clinical trial design, 2) advanced statistical and machine learning techniques, 3) acute care research, 4) AD health disparities, 5) recruitment and retention of vulnerable populations and 6) leadership. This proposed award addresses fundamental gaps and barriers to improve inclusion of disadvantaged individuals in AD research while affording training and mentored research critical for Dr. Gilmore-Bykovskyi to lead an independent research program in clinical AD research.
期刊论文(8)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1177/1556264620974898
发表时间: 2021-03
期刊: Journal of empirical research on human research ethics : JERHRE
影响因子: --
作者: [Benson C, Friz A, Mullen S, Block L, Gilmore-Bykovskyi A]
通讯作者: Gilmore-Bykovskyi A
DOI: 10.1111/jgs.17539
发表时间: 2022-03
期刊: Journal of the American Geriatrics Society
影响因子: 6.3
作者: [McCreedy E, Gilmore-Bykovskyi A, Dorr DA, Lima J, McCarthy EP, Meyers DJ, Platt R, Vydiswaran VGV, Bynum JPW]
通讯作者: Bynum JPW
Efficacy of Mealtime Interventions for Malnutrition and Oral Intake in Persons With Dementia: A Systematic Review.
进餐时间干预的营养不良和痴呆患者口服摄入的功效:系统评价。
DOI: 10.1097/wad.0000000000000387
发表时间: 2020-10
期刊: Alzheimer disease and associated disorders
影响因子: 2.1
作者: [Borders JC, Blanke S, Johnson S, Gilmore-Bykovskyi A, Rogus-Pulia N]
通讯作者: Rogus-Pulia N
DOI: 10.1001/jamanetworkopen.2020.35040
发表时间: 2021-01-04
期刊: JAMA network open
影响因子: 13.8
作者: [Gilmore-Bykovskyi A, Block L, Kind AJH]
通讯作者: Kind AJH
7
    Characterizing Episodes of Lucidity in Dementia Using Observational and Applied Computational Linguistics Approaches
    • 批准号:
      10266124
    • 项目类别:
    • 资助金额:
      $23.05万
    • 财政年份:
      2020
    • 负责人:
      Andrea L Gilmore-Bykovskyi
    • 依托单位:
    Characterizing Episodes of Lucidity in Dementia Using Observational and Applied Computational Linguistics Approaches
    • 批准号:
      10677987
    • 项目类别:
    • 资助金额:
      $72.75万
    • 财政年份:
      2020
    • 负责人:
      Andrea L Gilmore-Bykovskyi
    • 依托单位:
    Characterizing Episodes of Lucidity in Dementia Using Observational and Applied Computational Linguistics Approaches
    • 批准号:
      10704748
    • 项目类别:
    • 资助金额:
      $70.56万
    • 财政年份:
      2020
    • 负责人:
      Andrea L Gilmore-Bykovskyi
    • 依托单位:
    Characterizing Episodes of Lucidity in Dementia Using Observational and Applied Computational Linguistics Approaches
    • 批准号:
      10094836
    • 项目类别:
    • 资助金额:
      $19.42万
    • 财政年份:
      2020
    • 负责人:
      Andrea L Gilmore-Bykovskyi
    • 依托单位: