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Latent Factor Structure of Outcome Measures Used in the HABIT® Mild Cognitive Impairment Intervention Programs
Journal of Alzheimer’s Disease ( IF 4 ) Pub Date : 2021-09-08 , DOI: 10.3233/jad-210582
Brittany DeFeis 1 , Gelan Ying 1 , Andrea M Kurasz 1 , Liselotte De Wit 1 , Priscilla Amofa 1 , Melanie Chandler 2 , Dona Locke 3 , Anne Shandera-Ochsner 4 , Vaishali Phatak 5 , Glenn Smith 1
Affiliation  

Background:In Alzheimer’s disease and related disorders (ADRD) research, common outcome measures include cognitive and functional impairment, as well as persons with mild cognitive impairment (pwMCI) and care partner self-reported mood and quality of life. Studies commonly analyze these measures separately, which potentially leads to issues of multiple comparisons and/or multicollinearity among measures while ignoring the latent constructs they may be measuring. Objective:This study sought to examine the latent factor structure of a battery of 12-13 measures of domains mentioned above, used in a multicomponent behavioral intervention (The HABIT® program) for pwMCI and their partners. Methods:Exploratory factor analysis (EFA) involved 214 pwMCI-partner pairs. Subsequent Confirmatory factor analyses (CFA) used 730 pairs in both pre- and post-intervention conditions. Results:EFA generated a three-factor model. Factors could be characterized as partner adjustment (29.9%), pwMCI adjustment (18.1%), and pwMCI impairment (12.8%). The subsequent CFA confirmed our findings, and the goodness-of-fit for this model was adequate in both the pre- (CFI = 0.937; RMSEA = 0.057, p = 0.089) and post-intervention (CFI = 0.942; RMSEA = 0.051, p = 0.430) groups. Conclusion:Results demonstrated a stable factor structure across cohorts and intervention conditions suggesting that three broad factors may provide a straightforward and meaningful model to assess intervention outcome, at least during the MCI phase of ADRD.

中文翻译:

HABIT®轻度认知障碍干预计划中使用的结果测量的潜在因素结构

背景:在阿尔茨海默病及相关疾病 (ADRD) 研究中,常见的结果测量包括认知和功能障碍,以及轻度认知障碍 (pwMCI) 和护理伙伴自我报告的情绪和生活质量。研究通常单独分析这些度量,这可能会导致度量之间存在多重比较和/或多重共线性的问题,同时忽略了它们可能正在度量的潜在结构。目标:本研究试图检查一组 12-13 个上述领域测量的潜在因素结构,用于 pwMCI 及其合作伙伴的多组分行为干预(HABIT® 计划)。方法:探索性因素分析 (EFA) 涉及 214 个 pwMCI-伙伴对。随后的验证性因素分析 (CFA) 在干预前和干预后条件下使用了 730 对。结果:EFA 生成了一个三因素模型。因素可以表征为合作伙伴调整 (29.9%)、pwMCI 调整 (18.1%) 和 pwMCI 减损 (12.8%)。随后的 CFA 证实了我们的发现,并且该模型的拟合优度在干预前(CFI = 0.937;RMSEA = 0.057,p = 0.089)和干预后(CFI = 0.942;RMSEA = 0.051, p = 0.430) 组。结论:结果表明,跨队列和干预条件的因素结构稳定,这表明三个广泛的因素可以提供一个直接且有意义的模型来评估干预结果,至少在 ADRD 的 MCI 阶段。因素可以表征为合作伙伴调整 (29.9%)、pwMCI 调整 (18.1%) 和 pwMCI 减损 (12.8%)。随后的 CFA 证实了我们的发现,并且该模型的拟合优度在干预前(CFI = 0.937;RMSEA = 0.057,p = 0.089)和干预后(CFI = 0.942;RMSEA = 0.051, p = 0.430) 组。结论:结果表明,跨队列和干预条件的因素结构稳定,这表明三个广泛的因素可以提供一个直接且有意义的模型来评估干预结果,至少在 ADRD 的 MCI 阶段。因素可以表征为合作伙伴调整 (29.9%)、pwMCI 调整 (18.1%) 和 pwMCI 减损 (12.8%)。随后的 CFA 证实了我们的发现,并且该模型的拟合优度在干预前(CFI = 0.937;RMSEA = 0.057,p = 0.089)和干预后(CFI = 0.942;RMSEA = 0.051, p = 0.430) 组。结论:结果表明,跨队列和干预条件的因素结构稳定,这表明三个广泛的因素可以提供一个直接且有意义的模型来评估干预结果,至少在 ADRD 的 MCI 阶段。RMSEA = 0.051, p = 0.430) 组。结论:结果表明,跨队列和干预条件的因素结构稳定,这表明三个广泛的因素可以提供一个直接且有意义的模型来评估干预结果,至少在 ADRD 的 MCI 阶段。RMSEA = 0.051, p = 0.430) 组。结论:结果表明,跨队列和干预条件的因素结构稳定,这表明三个广泛的因素可以提供一个直接且有意义的模型来评估干预结果,至少在 ADRD 的 MCI 阶段。
更新日期:2021-09-12
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