Neighborhood Predictors of Cognitive Training Outcomes and Trajectories in ACTIVE

Abstract

We examined the influence of neighborhood socioeconomic position (SEP), racial/ethnic composition, and living in a major city on cognitive trajectories and intervention outcomes. Data came from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study (N = 2,438). Mixed effects analyses examined associations between neighborhood variables and memory, reasoning, speed of processing, and everyday cognition, estimating differences in initial gains (potentially related to practice) and long-term rate of change over 10 years. The effect of reasoning training on initial gain was weaker for individuals in a major city. For everyday cognition, there was a stronger initial gain for memory-trained and control participants in areas with more racial/ethnic minorities, and for speed-trained and control individuals in higher SEP areas. The racial/ethnic minority effect was no longer significant after adjustment for multiple comparisons. Neighborhood factors may be more important in practice-related improvement than in long-term change.

Neighborhood Environment and Cognition in Older Adults: A Systematic Review

“…Seventy-six percent of samples were population-based or randomly sampled. Eleven 32,37,38,45,47,49–54 studies used the MMSE, five studies 9,17,35,40,43 used the Telephone Interview for Cognitive Status, five used domain-specific cognitive measures, 39,42,44,48,51 and four used composite cognitive measures. 33,34,36,41 Eighty-four percent of studies used continuous measures of cognition instead of categorical/dichotomous measures, and ten studies used longitudinal cognitive measures.…”

Prognostic Models for Changes in Memory Performance After Memory Training in Healthy Older Adults: a Systematic Review

“…Table 1 gives an overview of the main characteristics of the included studies. Notably, n = 7 of the included studies investigated the same population (Gross et al, 2013;Gross & Rebok, 2011;Jones et al, 2013;Langbaum et al, 2009;Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015), namely the cognitive training trial ACTIVE.…”

“…Prognostic Models of Changes After Memory Training Table 2 summarizes the analysis of methods and results of the included studies. Concerning statistical methods which are used in the included studies, six studies used a latent growth curve model to calculate their prognostic models (Gross et al, 2013;Gross & Rebok, 2011;Jones et al, 2013;Lövdén et al, 2012;Rebok et al, 2013;Zahodne et al, 2015), four studies used a regression approach (Beck et al, 2013;Langbaum et al, 2009;McKitrick et al, 1999;Meyer et al, 2017), one study used a multilevel modeling approach (Macdonald et al, 2006), and one study used structural equation modeling (Zelinski et al, 2014). Over all models, the following predictors were investigated: age (integrated in n = 11 prognostic models), sex (n = 8), education (n = 7), ethnicity (n = 6), neuropsychological baseline values at the beginning of the training (n = 6), self-rated health status (n = 4), depressive status (n = 1), socioeconomic variables (i.e., living in major cities, neighborhood variables, employment status (n = 2)), and training-related variables (length of training, type of pre-training (n = 1)).…”

“…The numbers of predictors integrated in the prognostic models ranged from n = 1 (Jones et al, 2013, one predictor at several timepoints) to n = 15 (McKitrick et al, 1999). The predictors integrated in the model were highly heterogeneous: eight of twelve studies, however, integrated the sociodemographic predictors age, sex, and education in their models (with sometimes further additional predictors) (Beck et al, 2013;Gross et al, 2013;Gross & Rebok, 2011;Langbaum et al, 2009;Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015;Zelinski et al, 2014). In four of these studies (Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015;Zelinski et al, 2014), lower age and higher education predicted improvements in the memory outcomes (verbal short-and long-term memory) after training.…”

“…The predictors integrated in the model were highly heterogeneous: eight of twelve studies, however, integrated the sociodemographic predictors age, sex, and education in their models (with sometimes further additional predictors) (Beck et al, 2013;Gross et al, 2013;Gross & Rebok, 2011;Langbaum et al, 2009;Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015;Zelinski et al, 2014). In four of these studies (Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015;Zelinski et al, 2014), lower age and higher education predicted improvements in the memory outcomes (verbal short-and long-term memory) after training. However, it should be noted that three of these four studies are subsamples of the same study population of the ACTIVE trial (Meyer et al, 2017;Rebok et al, 2013;Zahodne et al, 2015).…”