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首页> 外文期刊>Assessment >Assessing Fatigue in Late-Midlife: Increased Scrutiny of the Multiple Fatigue Inventory-20 for Community-Dwelling Subjects
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Assessing Fatigue in Late-Midlife: Increased Scrutiny of the Multiple Fatigue Inventory-20 for Community-Dwelling Subjects

机译:评估中年后期的疲劳:针对社区居民受试者的多重疲劳清单20的详细审查

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摘要

Previous methods examining the Multiple Fatigue Inventory-20 (MFI-20) fatigue questionnaire have been limited to classical test theory, for example, factor analytic approaches. We employed modern test theory to further strengthen the construct validity of the MFI-20 fatigue in a sample of healthy late-midlife subjects. Five subdimensions of perceived fatigue were examined in n = 7,233 subjects: general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue. Fatigue burden was compared across age groups (aged 48-52 vs. 57-63) and gender. Mokken item response theory was used to investigate dimensionality, monotonicity, and invariant item ordering (IIO). In both age groups, as well as by gender, the Motivation domain presented with weak scalability, suggesting that caution be exercised when interpreting sum scores. For all groupings, the strongest scaling properties were observed in the General Fatigue domain. However, the General Fatigue domain did not meet the property of IIO. Two domains (for all groupings) did meet the minimum criteria for the property of IIO: Physical Fatigue and Activity. Introducing model parameters for items served to enhance the interpretive power of the MFI-20, allowing for the identification of the most optimal scales. Poorly performing items were more easily identified, and person ability was assessed more accurately.
机译:以前检查多重疲劳清单20(MFI-20)疲劳调查表的方法仅限于经典测试理论,例如因子分析方法。我们采用了现代测试理论,以进一步增强健康中年中年受试者样本中MFI-20疲劳的构造效度。在n = 7,233名受试者中,检查了感知到的疲劳的五个子维度:一般疲劳,身体疲劳,活动减少,动机减少和精神疲劳。比较了不同年龄段(48-52岁对57-63岁)和性别的疲劳负担。 Mokken项目响应理论用于研究维数,单调性和不变项目排序(IIO)。在两个年龄段以及按性别划分的动机领域中,可扩展性均较弱,这表明在解释总和分数时应谨慎行事。对于所有组,在“一般疲劳”域中观察到最强的缩放属性。但是,“一般疲劳”域不符合IIO的属性。对于所有分组,两个域确实满足了IIO属性的最低标准:身体疲劳和活动。为项目引入模型参数有助于增强MFI-20的解释能力,从而确定最佳秤。绩效较差的项目更容易被识别,人员能力也得到更准确的评估。

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