Longitudinal Association Between Depressive Symptoms and Cognitive Function Among Older Adults: A Latent Growth Curve Modeling Approach

Abstract

Objectives: Although the evidence from numerous longitudinal studies has indicated a remarkable change in cognitive function (CF) and depressive symptoms (DS) over time, the parallel latent growth curve model (LGCM) has seldom been used to simultaneously investigate the relationship between their change trajectories. This study aimed to examine whether a change in DS was associated with CF over time using an LGCM. Methods: Data were collected from the Chinese Longitudinal Healthy Longevity Survey's 2011, 2014, and 2018 waves. A parallel LGCM examined the association between CF and DS. Results: The multivariate conditioned model's goodness of fit supported the validity of the longitudinal model (Tucker-Lewis index [TLI] = 0.90, comparative fit index [CFI] = 0.96, root mean square error of approximation [RMSEA] = 0.04). The results showed that the CF intercept was positively to the DS slope (β = 0.42, p = 0.004). The CF and DS slopes were significantly linked (β = -0.65, p = 0.002). Conclusion: The findings expand the knowledge about CF's effect on DS in older adults.

Keywords: cognitive function; depressive symptoms; latent growth curve model; longitudinal study; old adults.