Systematic identification and quantification of factors and their interactions with age, sex, and panel wave influencing cognitive function in Korean older adults - Eunmi Kim; Eunmi Kim; Jinkyung Oh; Jinkyung Oh; Jungsoo Gim; Jungsoo Gim; Jungsoo Gim; Iksoo Huh; Iksoo Huh (Frontiers in Public Health)
BackgroundCognitive decline in older adults is influenced by diverse factors, and degrees of influence of these factors may vary depending on sex, age cohorts, and passage of time. Moreover, these factors differ in their responsiveness to general interventions. Thus, identifying these factors including their interactions with age, sex, and panel wave and conducting a systematic quantification of their influences on cognitive function are both necessary for developing efficient intervention strategies.MethodsTo identify the influencing factors and their interactions, we applied a systematic stepwise variable selection using 2,535 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging from Wave 5 (2014) to Wave 8 (2020). These factors were subsequently grouped based on their modifiability to investigate group-wise influences on cognitive function. For handling the longitudinal data, a generalized least squares method was used, and the degrees of influence of these factors were measured using the delta R2.ResultsTwelve variables had significant main effects on cognitive function in older adults. Among these variables, age interacted with sex, regular exercise, and marital status. Sex interacted with regular exercise, education level, and depressive symptoms. Wave number interacted with depressive symptoms and social activity. In addition, the group-wise delta R2 values were found to be 10.9, 6.3, and 5.9% in the difficult-to-modify, modifiable, and non-modifiable factor groups, respectively. Afterwards, we provided the delta R2 for each sub-population divided by the levels of age, sex, and wave number to examine how these factors changed the influences.ConclusionBased on the interaction and quantification results, we elucidated the characteristics of the influencing factors and their degrees of influence, and we suggest grouping factors based on their modifiability to systematically prevent cognitive decline in older adults.