Nikki H 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
{"title":"梅奥规范研究:基于回归的斯特里克学习广度、符号测试和梅奥测试驱动筛选电池在轻度认知障碍和痴呆患者中的远程自我管理规范数据。","authors":"Nikki H 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","doi":"10.1080/13854046.2025.2469340","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objective:</b> Few normative data for unsupervised, remotely-administered computerized cognitive measures are available. We examined variables to include in normative models for Mayo Test Drive (MTD, a multi-device remote cognitive assessment platform) measures, developed normative data, and validated the norms. <b>Method:</b> 1240 Cognitively Unimpaired (CU) adults ages 32-100 years (96% White) from the Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center with Clinical Dementia Rating<sup>®</sup> of 0 were included. We converted raw scores to normalized scaled scores and derived regression-based normative data adjusting for age, age<sup>2</sup>, sex, and education (base model); alternative norms are also provided (age + age<sup>2</sup> + sex; age + age<sup>2</sup>). We assessed additional terms using an <i>a priori</i> cut-off of 1% variance improvement above the base model. We examined low test performance rates (< -1 <i>SD</i>) in independent validation samples (<i>n</i> = 167 CU, <i>n</i> = 64 mild cognitive impairment (MCI), <i>n</i> = 14 dementia). Rates were significantly different when 95% confidence intervals (CI) did not include the expected 14.7% base rate. <b>Results:</b> No model terms met the <i>a priori</i> 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. <b>Conclusion:</b> 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. 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引用次数: 0
摘要
目的:关于无监督、远程管理的计算机认知测量的规范性数据很少。我们检查了Mayo Test Drive (MTD,一种多设备远程认知评估平台)测量的规范模型中的变量,开发了规范数据,并验证了规范。方法:纳入来自梅奥临床衰老研究中心和梅奥阿尔茨海默病研究中心的1240名认知功能未受损(CU)成年人,年龄32-100岁(96%为白人),临床痴呆评分®为0。我们将原始分数转换为标准化比例分数,并推导出基于回归的规范数据,调整了年龄、年龄、性别和教育程度(基础模型);还提供了其他规范(年龄+年龄2 +性别;age + age2)。我们使用比基本模型高出1%的方差改进的先验截止值来评估附加项。我们检查了独立验证样本(n = 167 CU, n = 64轻度认知障碍(MCI), n = 14痴呆)的低测试表现率(SD)。当95%置信区间(CI)不包括预期的14.7%基本率时,发生率显著不同。结果:除基本模型外,没有模型项满足先验截止条件,包括设备类型、响应输入源(如鼠标等)或会话干扰。规范显示,在独立验证样本中,CU的预期低绩效率和MCI和痴呆的更高低绩效率。结论:典型的规范模型适合于远程自我给药MTD测量,并且对认知障碍敏感。设备类型和响应输入源不能解释足够的方差以纳入规范模型,但对于个人层面的解释很重要。未来的工作将增加来自代表性不足群体的个人的包容性。
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 (MTD, 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's 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 SD) 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 the inclusion of individuals from under-represented groups.
期刊介绍:
The Clinical Neuropsychologist (TCN) serves as the premier forum for (1) state-of-the-art clinically-relevant scientific research, (2) in-depth professional discussions of matters germane to evidence-based practice, and (3) clinical case studies in neuropsychology. Of particular interest are papers that can make definitive statements about a given topic (thereby having implications for the standards of clinical practice) and those with the potential to expand today’s clinical frontiers. Research on all age groups, and on both clinical and normal populations, is considered.