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Cerino ES, Katz MJ, Wang C, Qin J, Gao Q, Hyun J, Hakun JG, Roque NA, Derby CA, Lipton RB, Sliwinski MJ. Variability in Cognitive Performance on Mobile Devices Is Sensitive to Mild Cognitive Impairment: Results From the Einstein Aging Study. Front Digit Health 2021; 3:758031. [PMID: 34927132 PMCID: PMC8677835 DOI: 10.3389/fdgth.2021.758031] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/08/2021] [Indexed: 11/19/2022] Open
Abstract
Background and Objective: Within-person variability in cognitive performance has emerged as a promising indicator of cognitive health with potential to distinguish normative and pathological cognitive aging. We use a smartphone-based digital health approach with ecological momentary assessments (EMA) to examine differences in variability in performance among older adults with mild cognitive impairment (MCI) and those who were cognitively unimpaired (CU). Method: A sample of 311 systematically recruited, community-dwelling older adults from the Einstein Aging Study (Mean age = 77.46 years, SD = 4.86, Range = 70-90; 67% Female; 45% Non-Hispanic White, 40% Non-Hispanic Black) completed neuropsychological testing, neurological assessments, and self-reported questionnaires. One hundred individuals met Jak/Bondi criteria for MCI. All participants performed mobile cognitive tests of processing speed, visual short-term memory binding, and spatial working memory on a smartphone device up to six times daily for 16 days, yielding up to 96 assessments per person. We employed heterogeneous variance multilevel models using log-linear prediction of residual variance to simultaneously assess cognitive status differences in mean performance, within-day variability, and day-to-day variability. We further tested whether these differences were robust to the influence of environmental contexts under which assessments were performed. Results: Individuals with MCI exhibited greater within-day variability than those who were CU on ambulatory assessments that measure processing speed (p < 0.001) and visual short-term memory binding (p < 0.001) performance but not spatial working memory. Cognitive status differences in day-to-day variability were present only for the measure of processing speed. Associations between cognitive status and within-day variability in performance were robust to adjustment for sociodemographic and contextual variables. Conclusion: Our smartphone-based digital health approach facilitates the ambulatory assessment of cognitive performance in older adults and the capacity to differentiate individuals with MCI from those who were CU. Results suggest variability in mobile cognitive performance is sensitive to MCI and exhibits dissociative patterns by timescale and cognitive domain. Variability in processing speed and visual short-term memory binding performance may provide specific detection of MCI. The 16-day smartphone-based EMA measurement burst offers novel opportunity to leverage digital technology to measure performance variability across frequent assessments for studying cognitive health and identifying early clinical manifestations of cognitive impairment.
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Affiliation(s)
- Eric S. Cerino
- Department of Psychological Sciences, College of Social and Behavioral Sciences, Northern Arizona University, Flagstaff, AZ, United States
- Center for Healthy Aging, College of Health and Human Development, Pennsylvania State University, University Park, PA, United States
| | - Mindy J. Katz
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Cuiling Wang
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jiyue Qin
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Qi Gao
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jinshil Hyun
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jonathan G. Hakun
- Center for Healthy Aging, College of Health and Human Development, Pennsylvania State University, University Park, PA, United States
- Department of Neurology, College of Medicine, Pennsylvania State University, Hershey, PA, United States
| | - Nelson A. Roque
- Department of Psychology, University of Central Florida, Orlando, FL, United States
| | - Carol A. Derby
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Richard B. Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Martin J. Sliwinski
- Center for Healthy Aging, College of Health and Human Development, Pennsylvania State University, University Park, PA, United States
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