Age-varying association between depression and cognitive function among a national sample of older U.S. immigrant adults: the potential moderating role of physical activity

Cognitive Performance Deficits Are Associated with Clinically Significant Depression Symptoms in Older US Adults

“…The connection between cognitive function and depression likely also represents the drastic changes in lifestyles and circumstances that these critical age groups experience, as individuals reach retirement age [ 54 ]. Previous research has provided evidence that retirement is associated with a higher risk of depression and a modest but negative effect on cognition [ 55 , 56 , 57 ].…”

Modelling Age-Varying Associations among Group Memberships, Neighborhood Connectedness, and Well-Being

“…Their article also conducted novel cross-sectional analyses that documented variability of anxiety and depression symptoms with age and across different populations. Indeed, gerontological researchers are increasingly using TVEM to examine ages at which an association between two variables is comparably stronger or weaker relative to other time points (Cai et al, 2023;Chamberlain, Sprague, & Ross, 2022;Freed, Sprague, & Ross, 2023;Jung, Kim, Loprinzi, Ryu, & Kang, 2023;Sprague, Phillips, & Ross, 2019). Most of these investigations examined links between cognitive function and other relevant variables, but the study by Cai et al (2023) is an exemplar study examining variability in the association between one's social relationships (i.e., familial support) and depressive symptoms, using a large archival data set with participants in China.…”

“…The term "time invariant" means that the model was constructed such that the associations between each covariate and dependent variable were constant across age. Recent applications of TVEM in gerontological research have also specified models in which the primary predictor is time varying, whereas additional covariates are time invariant when there is limited explicit theory leading researchers to presume that associations with control variables would vary with time (e.g., Freed et al, 2023;Jung et al, 2023). Example SAS code is available in online Supplemental Materials.…”

“…Present analyses included a P-spline approach in which the researcher sets the maximum number of knots (i.e., 10; Li et al, 2017) and subsequent analyses fit a curve to the data and utilize smoothing to select the most parsimonious model. P-spline approaches are widely adopted in recent literature (e.g., Cai et al, 2023;Jung et al, 2023) and are useful in exploratory analyses (e.g., Lanza & Linden-Carmichael, 2021). Whereas alternative spline approaches such as the B-spline approach permit increased flexibility and reduce the risk that a model will be overly smoothed (i.e., too few inflection points), P-spline approaches were selected to align with existing literature, and because of the exploratory nature of the present analyses.…”

“…Future researchers should leverage larger and nationally representative data sets to further probe these associations. Such large-scale data would improve precision and would allow use of tools such as weighted TVEM, which can weight each age-specific sample to account for unequal probabilities of selection in large data sets while also accounting for clustering in schools or communities (see Jung et al, 2023).…”