Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States.
J Med Internet Res. 2022 May 9;24(5):e34347. doi: 10.2196/34347.
Cognitive testing in large population surveys is frequently used to describe cognitive aging and determine the incidence rates, risk factors, and long-term trajectories of the development of cognitive impairment. As these surveys are increasingly administered on internet-based platforms, web-based and self-administered cognitive testing calls for close investigation.
Web-based, self-administered versions of 2 age-sensitive cognitive tests, the Stop and Go Switching Task for executive functioning and the Figure Identification test for perceptual speed, were developed and administered to adult participants in the Understanding America Study. We examined differences in cognitive test scores across internet device types and the extent to which the scores were associated with self-reported distractions in everyday environments in which the participants took the tests. In addition, national norms were provided for the US population.
Data were collected from a probability-based internet panel representative of the US adult population-the Understanding America Study. Participants with access to both a keyboard- and mouse-based device and a touch screen-based device were asked to complete the cognitive tests twice in a randomized order across device types, whereas participants with access to only 1 type of device were asked to complete the tests twice on the same device. At the end of each test, the participants answered questions about interruptions and potential distractions that occurred during the test.
Of the 7410 (Stop and Go) and 7216 (Figure Identification) participants who completed the device ownership survey, 6129 (82.71% for Stop and Go) and 6717 (93.08% for Figure Identification) participants completed the first session and correctly responded to at least 70% of the trials. On average, the standardized differences across device types were small, with the absolute value of Cohen d ranging from 0.05 (for the switch score in Stop and Go and the Figure Identification score) to 0.13 (for the nonswitch score in Stop and Go). Poorer cognitive performance was moderately associated with older age (the absolute value of r ranged from 0.32 to 0.61), and this relationship was comparable across device types (the absolute value of Cohen q ranged from 0.01 to 0.17). Approximately 12.72% (779/6123 for Stop and Go) and 12.32% (828/6721 for Figure Identification) of participants were interrupted during the test. Interruptions predicted poorer cognitive performance (P<.01 for all scores). Specific distractions (eg, watching television and listening to music) were inconsistently related to cognitive performance. National norms, calculated as weighted average scores using sampling weights, suggested poorer cognitive performance as age increased.
Cognitive scores assessed by self-administered web-based tests were sensitive to age differences in cognitive performance and were comparable across the keyboard- and touch screen-based internet devices. Distraction in everyday environments, especially when interrupted during the test, may result in a nontrivial bias in cognitive testing.
在大规模人群调查中,认知测试常用于描述认知衰老,并确定认知障碍的发病率、风险因素和长期发展轨迹。随着这些调查越来越多地在基于互联网的平台上进行,基于网络和自我管理的认知测试需要进行密切调查。
为年龄敏感的认知测试开发并提供了两种基于网络的自我管理版本,即用于执行功能的停止和转换任务切换测试和用于感知速度的图形识别测试,这些测试已在理解美国研究中的成年参与者中进行了测试。我们检查了不同互联网设备类型之间认知测试分数的差异,以及参与者在进行测试的日常环境中自我报告的干扰与测试分数之间的关联程度。此外,还为美国人口提供了全国性的标准。
数据来自一个基于概率的互联网面板,代表美国成年人口——理解美国研究。有机会使用键盘和鼠标设备以及触摸屏设备的参与者被要求在设备类型之间随机进行两次认知测试,而只能使用一种设备的参与者则被要求在同一设备上进行两次测试。在每次测试结束时,参与者回答了有关测试过程中发生的中断和潜在干扰的问题。
在完成设备所有权调查的 7410 名(停止和转换)和 7216 名(图形识别)参与者中,6129 名(停止和转换的 82.71%)和 6717 名(图形识别的 93.08%)参与者完成了第一个会话,并正确回答了至少 70%的测试题。平均而言,不同设备类型之间的标准化差异很小,Cohen d 的绝对值范围为 0.05(停止和转换的切换分数和图形识别分数)至 0.13(停止和转换的非切换分数)。较差的认知表现与年龄较大呈中度相关(绝对值 r 范围为 0.32 至 0.61),并且这种关系在不同设备类型之间是可比的(绝对值 Cohen q 范围为 0.01 至 0.17)。大约 12.72%(停止和转换的 779/6123)和 12.32%(图形识别的 828/6721)的参与者在测试过程中被打断。干扰预测认知表现较差(所有分数的 P<.01)。具体干扰(例如,看电视和听音乐)与认知表现的关联不一致。使用抽样权重计算的加权平均分数得出的全国性标准表明,随着年龄的增长,认知表现会变差。
自我管理的基于网络的测试评估的认知分数对认知表现的年龄差异敏感,并且在基于键盘和触摸屏的互联网设备之间具有可比性。日常环境中的干扰,尤其是在测试过程中被打断时,可能会导致认知测试出现相当大的偏差。