Shelia R Cotten

Background and Objectives The number of older adults diagnosed with dementia is expected to more than double by mid-century. Monitoring older adults’ cognitive functioning is essential for the early detection of dementia, which can increase treatment efficacy and slow the disease progression. Current approaches to detect preclinical dementia are expensive, invasive, and are not accessible to everyone. This study aimed to determine whether technology use patterns could be used as an indicator of cognitive change. Research Design and Methods Using six waves (2015 - 2020) from the National Health and Aging Trends Study (NHATS), we examined within-person and between-person effects of stopping four types of technology use on four cognitive outcomes. Results Our analyses showed that stopping the use of the Internet, computers, tablets, and sending texts and emails were related to cognitive decline. Discussion and Implications These results suggest that monitoring technology use patterns may be an innovative approach that is economical and efficient to identify individuals at risk of cognitive decline.

Background and Objectives Social media use (SMU) has increased over the past decade among older adults. Cross-sectional studies report SMU is related to negative mental health outcomes (i.e., depression). Given depression is the most common mental health issue for older adults and increases the risk of morbidity and mortality, determining longitudinally whether SMU is related to increased depression is critical. This study examined the longitudinal relationship between SMU and depression. Research Design and Methods Data from 6 waves (2015–20) of the National Health and Aging Trends Study (NHATS) were analyzed. Participants included a nationally representative sample of U.S. older adults, aged 65 years and older (N = 7,057). We used a Random Intercept Cross-Lagged Panel Modeling (RI-CLPM) framework to examine the relationship between primary outcomes of SMU and depression symptoms. Results No pattern was found of SMU predicting depression symptoms, or depression symptoms predicting SMU. The major driver of SMU in each wave was SMU in the previous wave. On average, our model accounted for 3.03% of the variance in SMU. The major predictor of depression in each wave was the pre-existing depression. Our model accounted for an average of 22.81% of the variance in depressive symptoms. Discussion and Implications The results suggest that SMU and depressive symptoms are driven by the previous patterns of SMU and depression, respectively. We did not find any patterns of SMU and depression influencing each other. NHATS measures SMU with a binary instrument. Future longitudinal research should employ measures that account for SMU duration, type, and purpose. These findings suggest that SMU may not be related to negative health outcomes, in this case, depression, for older adults.