Practice and Drop-Out Effects During a 17-Year Longitudinal Study of Cognitive Aging

Abstract

Interpretations of longitudinal studies of cognitive aging are misleading unless effects of practice and selective drop-out are considered. A random effects model taking practice and drop-out into account analyzed data from four successive presentations of each of two intelligence tests, two vocabulary tests, and two verbal memory tests during a 17-year longitudinal study of 5,899 community residents whose ages ranged from 49 to 92 years. On intelligence tests, substantial practice effects counteracted true declines observed over 3 to 5 years of aging and remained significant even with intervals of 7 years between successive assessments. Adjustment for practice and drop-out revealed accelerating declines in fluid intelligence and cumulative learning, linear declines in verbal free recall, and no substantial change in vocabulary. Socioeconomic status and basal levels of general fluid ability did not affect rates of decline. After further adjustment for demographics, variability between individuals was seen to increase as the sample aged.

Influence of serotonin transporter gene polymorphisms on cognitive decline and cognitive abilities in a nondemented elderly population

“…44 In addition to linear trend terms the models also included dummy variables to account for practice effects, previously shown to be important in these data. 45 The statistical significance of terms in the regressions were obtained from adjusted Wald's tests. As vocabulary ability tends to remain relatively stable with age, longitudinal analysis was not performed on the MHA test.…”

Unhappiness, health and cognitive ability in old age

“…The model has two levels, one at individual level including random intercept and random age effect, and the other a residual error. The methodological issues and notation of this model are discussed in detail elsewhere (Rabbitt et al 2004). This model examines how population average scores for each cognitive test vary with the effects of age, sex, occupational category, city of residence, year of entry (recruitment cohort) and practice (i.e.…”

“…However, because more-able participants experience greater gains (Rabbitt et al 2001(Rabbitt et al , 2004, practice effects during longitudinal studies may selectively mask declines in individuals who are relatively more contented, and so more able, giving the misleading impression that less-able depressed individuals decline more rapidly.…”

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

“…Even on the meta-analytic level, evidence is mixed regarding the effectiveness of cognitive training in both younger and older adults (e.g., Au et al 2015;Dougherty et al 2016;Karbach and Verhaeghen 2014;Kelly et al 2014;Lampit et al 2014;Melby-Lervåg and Hulme 2013;Melby-Lervåg et al 2016;Schwaighofer et al 2015;Soveri et al 2017). Aside from design and methodological choices potentially explaining the diverging findings (e.g., Noack et al 2009;Shipstead et al 2012), many authors increasingly articulated the potentially important influence of individual differences on cognitive training trajectories and outcomes (e.g., Buitenweg et al 2012;Guye et al 2016;Könen and Karbach 2015;Shah et al 2012;von Bastian and Oberauer 2014 Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern 2017) accentuate with increasing age (e.g., Rabbitt et al 2004) and have been shown to be related to personality (e.g., Graham and Lachman 2012), cognition-related beliefs such as need for cognition (NFC; e.g., Fleischhauer et al 2010;Hill et al 2013), and everyday life activities (e.g., Jopp and Hertzog 2007). Investigating which of these individual differences potentially predict cognitive training outcomes may not only explain inconsistencies concerning the effectiveness of cognitive training, but also identify possible subgroups of individuals that are more or less responsive to cognitive training, thereby constituting the conceptual groundwork for developing individually tailored interventions to boost training effectiveness.…”

“…Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern 2017) accentuate with increasing age (e.g., Rabbitt et al 2004) and have been shown to be related to personality (e.g., Graham and Lachman 2012), cognition-related beliefs such as need for cognition (NFC; e.g., Fleischhauer et al 2010;Hill et al 2013), and everyday life activities (e.g., Jopp and Hertzog 2007). Investigating which of these individual differences potentially predict cognitive training outcomes may not only explain inconsistencies concerning the effectiveness of cognitive training, but also identify possible subgroups of individuals that are more or less responsive to cognitive training, thereby constituting the conceptual groundwork for developing individually tailored interventions to boost training effectiveness.…”