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.
Highlights
Alzheimer’s disease is the sixth leading cause of death in America, impeding health and well-being for an estimated 6.2 million Americans currently living with the dementia stages of the illness [1]
The present study extends this analysis of single assessments completed each day in a research office by evaluating cognitive status (0 = cognitively unimpaired (CU), 1 = mild cognitive impairment (MCI)) differences in the variability in mobile cognitive performance both within-day and from dayto-day in everyday life
For each mobile cognitive test, we evaluate cognitive status (0 = CU, 1 = MCI) differences in the variability in mobile cognitive performance across sessions within a day as well as across study days to examine if differences in variability emerge at the within-day level vs. the across-day-level timescales or both
Summary
Alzheimer’s disease is the sixth leading cause of death in America, impeding health and well-being for an estimated 6.2 million Americans currently living with the dementia stages of the illness [1]. Older adults with mild cognitive impairment (MCI), conceptualized as an intermediary stage between normative cognitive aging and dementia, have more memory and thinking problems than their age and education-level peers [2, 3]. We assess variability in test performance on mobile devices that may inform subtle differences in everyday cognitive performance between individuals with MCI and those who were cognitively unimpaired (CU). 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)
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