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In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants.

Self-reported cognitive difficulties are common in older adults and may be an early indicator of future cognitive decline or dementia. In past retrospective reports, cognitive difficulties have been linked with differences in social engagement or social relationships among older adults. However, little is known about how self-reported cognitive difficulties in daily life, such as memory lapses, relate to older adults’ daily social experiences. This study examined how self-reported cognitive difficulties were related to older adults’ daily social interactions and loneliness. Data were drawn from 312 community-dwelling older adults (aged 70 to 90 years) who reported their social interactions and loneliness throughout the day (five times) as well as cognitive difficulties (e.g., memory lapses, problems with attention) at the end of each day for 14 days. Multilevel models revealed that participants reported fewer memory lapses on days when they reported more frequent interactions with family members (p=.041). Higher levels of disruptions to daily activities caused by cognitive difficulties, in turn, predicted higher levels of loneliness the next day (p=.006), but not changes in social interactions the next day. At the between-person level, more memory lapses in daily life were associated with less frequent social interactions with friends, but more frequent unpleasant social interactions and higher levels of loneliness on average. These results suggest that older adults’ self-reported cognitive difficulties were dynamically associated with their social interactions and loneliness at the daily level and played an important role in older adults’ social life and well-being.

Understanding age-related change in cognition and identification of pathological changes requires sensitive and valid measurement of cognitive performance across time. Technological advances, such as ambulatory assessment of cognition using smartphones, have enabled intensive longitudinal methods where data is collected with many measurements over time. Our research group has developed novel ambulatory assessments that provide reliable, sensitive, and ecologically valid measurement of cognition across multiple timescales; from momentary changes to change across years. This symposium will present a spectrum of approaches to analysis of intensive longitudinal data that can inform models of cognitive aging. All three presentations will draw on data from measurement burst studies that apply our ambulatory cognitive assessment methods in community-based samples (i.e., systematically recruited in the Bronx, New York). For each measurement burst, participants undergo assessment consisting of brief surveys and cognitive tests via smartphone, up to 7 times per day across 14 days. Oravecz et al. will discuss the application of a Bayesian multilevel implementation of the double exponential model to account for retest effects while quantifying change in peak cognitive performance across time. Kang et al., will demonstrate a growth curve modeling approach for assessing the effects of between-person variables (i.e., loneliness) on change in cognition across measurement bursts. Harrington et al., will demonstrate a model-based cluster analysis approach, leveraging ambulatory assessments of subjective and objective cognitive function to unpack latent groups as a function of age and loneliness. Measurement, Statistics, and Research Design Interest Group Sponsored Symposium.

Loneliness is a risk factor for dementia, however it’s relationship with cognitive health during midlife is unclear. We evaluated whether loneliness was associated with profiles of objective and subjective memory in younger and middle-aged adults. Participants (aged 25 to 64 years) underwent an initial loneliness assessment, followed by 14-days of momentary (5 per day) cognitive assessments (objective memory) and daily ratings of memory (subjective memory). Cluster analysis was conducted using person-level means of objective and subjective memory. Three clusters were identified: (1) highest objective and subjective memory (9%); (2) lowest subjective but not objective memory (84%); (3) lowest objective but not subjective memory (7%).There was a trend for higher levels of loneliness in Cluster 2 relative to Clusters 1 and 3. Results suggest that loneliness is more closely related with subjective than objective memory during midlife and are informative for development of interventions targeting cognitive health. Part of a symposium sponsored by the Measurement, Statistics, and Research Design Interest Group.