梅奥常模研究:基于回归的常模数据,用于远程自我管理史翠克学习跨度、符号测验和梅奥驾驶测验筛选电池的综合测试,并在轻度认知障碍和痴呆症患者中进行验证

Nikki Horne Stricker, Ryan D Frank, Elizabeth A Boots, Winnie Z Fan, Teresa J Christianson, Walter K Kremers, John L Stricker, Mary M Machulda, Julie A Fields, John A Lucas, Jason Hassenstab, Paula A Aduen, Gregory S Day, Neill R Graff-Radford, Clifford R Jack, Jonathan Graff-Radford, Ronald C Petersen
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摘要

目的:目前几乎没有无监督远程计算机认知测量的标准数据。我们研究了梅奥 Test Drive(一种多设备远程认知评估平台)测量标准模型中应包含的变量,开发了标准数据,并对标准进行了验证。方法:研究对象包括梅奥诊所老龄化研究和梅奥阿尔茨海默病研究中心的 1240 名认知功能未受损(CU)的成年人,年龄在 32-100 岁之间(96% 为白人),临床痴呆评级为 0。我们将原始分数转换为标准化的标度分数,并得出基于回归的常模数据,调整年龄、年龄2、性别和教育程度(基础模型);还提供了其他常模(年龄+年龄2+性别;年龄+年龄2)。我们采用比基础模型高 1%方差的先验临界值来评估附加项。我们检查了独立验证样本(167 个 CU、64 个轻度认知障碍 (MCI)、14 个痴呆)中的低测试表现率(-1 标准差)。当 95% 置信区间 (CI) 不包括预期的 14.7% 基准比率时,比率会有明显差异。结果:在基础模型之外,没有任何模型项达到先验截止值,包括设备类型、响应输入源(如鼠标等)或会话干扰。在独立的验证样本中,常模显示出预期的 CU 低表现率,而 MCI 和痴呆症的低表现率更高:典型的常模似乎适用于远程自控 MTD 测量,并且对认知障碍很敏感。设备类型和响应输入源不足以解释纳入常模的变异,但对个体层面的解释非常重要。未来的工作将更多地纳入代表性不足群体的个体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mayo Normative Studies: regression-based normative data for remote self-administration of the Stricker Learning Span, Symbols Test and Mayo Test Drive Screening Battery Composite and validation in individuals with Mild Cognitive Impairment and dementia
Objective: Few normative data for unsupervised, remotely-administered computerized cognitive measures are available. We examined variables to include in normative models for Mayo Test Drive (a multi-device remote cognitive assessment platform) measures, developed normative data, and validated the norms. Method: 1240 Cognitively Unimpaired (CU) adults ages 32-100-years (96% white) from the Mayo Clinic Study of Aging and Mayo Alzheimer Disease Research Center with Clinical Dementia Rating of 0 were included. We converted raw scores to normalized scaled scores and derived regression-based normative data adjusting for age, age2, sex and education (base model); alternative norms are also provided (age+age2+sex; age+age2). We assessed additional terms using an a priori cut-off of 1% variance improvement above the base model. We examined low test performance rates (<-1 standard deviation) in independent validation samples (n=167 CU, n=64 mild cognitive impairment (MCI), n=14 dementia). Rates were significantly different when 95% confidence intervals (CI) did not include the expected 14.7% base rate. Results: No model terms met the a priori cut-off beyond the base model, including device type, response input source (e.g., mouse, etc.) or session interference. Norms showed expected low performance rates in CU and greater rates of low performance in MCI and dementia in independent validation samples. Conclusion: Typical normative models appear appropriate for remote self-administered MTD measures and are sensitive to cognitive impairment. Device type and response input source did not explain enough variance for inclusion in normative models but are important for individual-level interpretation. Future work will increase inclusion of individuals from under-represented groups.
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