No CrossRef data available.
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
Published online by Cambridge University Press: 21 December 2023
Intraindividual variability (IIV) is defined as fluctuations in an individual’s cognitive performance over time1. IIV has been identified as a marker of neurobiological disturbance making it a useful method for detecting changes in cognition among cognitively healthy individuals as well as those with prodromal syndromes2. IIV on laboratory-based computerized tasks has been linked with cognitive decline and conversion to mild cognitive impairment (MCI) and/or dementia (Haynes et al., 2017). Associations between IIV and AD risk factors including apolipoprotein (APOE) ε4 carrier status, neurodegeneration seen on brain imaging, and amyloid (Aß) Positron emission tomography (PET) scan status have also been observed1. Recent studies have demonstrated that evaluating IIV on smartphone-based digital cognitive assessments is feasible, has the capacity to differentiate between cognitively normal (CN) and MCI individuals, and may reduce barriers to cognitive assessment3. This study sought to evaluate whether such differences could be detected in CN participants with and without elevated AD risk.
Participants (n=57) were cognitively normal older adults who previously received an Aß PET scan through the Butler Hospital Memory and Aging Program. The sample consisted of primarily non-Hispanic (n=49, 86.0%), White (n=52, 91.2%), college-educated (M=16.65 years), females (n=39, 68.4%). The average age of the sample was 68 years old. Approximately 42% of the sample (n=24) received a positive PET scan result. Participants completed brief cognitive assessments (i.e., 3-4 minutes) three times per day for eight days (i.e., 24 sessions) using the Mobile Monitoring of Cognitive Change (M2C2) application, a mobile app-based cognitive testing platform developed as part of the National Institute of Aging’s Mobile Toolbox initiative (Sliwinski et al., 2018). Participants completed visual working memory, processing speed, and episodic memory tasks on the M2C2 platform. Intraindividual standard deviations (ISDs) across trials were computed for each person at each time point (Hultsch et al., 2000). Higher ISD values indicate more variability in performance. Linear mixed effects models were utilized to examine whether differences in IIV existed based on PET scan status while controlling for age, sex at birth, and years of education.
n interaction between PET status and time was observed on the processing speed task such that Aß- individuals were less variable over the eight assessment days compared to Aß + individuals (B= -5.79, SE=2.67, p=.04). No main or interaction effects were observed on the visual working memory task or episodic memory task.
Our finding that Aß- individuals demonstrate less variability over time on a measure of processing speed is consistent with prior work. No associations were found between IIV in other cognitive domains and PET status. As noted by Allaire and Marsiske (2005), IIV is not a consistent phenomenon across different cognitive domains. Therefore, identifying which tests are the most sensitive to early change is crucial. Additional studies in larger, more diverse samples are needed prior to widespread clinical use for early detection of AD.
You have
Access