Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.