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. Author manuscript; available in PMC: 2023 Oct 1.
Published in final edited form as: J Aging Health. 2022 May 5;34(6-8):1135–1143. doi: 10.1177/08982643221097681

Long-Term Effects of Cognitive Training on All-Cause Mortality in US Older Adults

George W Rebok 1,2,3, Alison Huang 1, Emily Smail 1, Rostislav Brichko 4, Jeanine M Parisi 1, Michael Marsiske 5, David L Roth 2, Roland J Thorpe Jr 3,6, Cynthia Felix 1, Richard N Jones 7, Sherry L Willis 8
PMCID: PMC10069226  NIHMSID: NIHMS1885547  PMID: 35510611

Abstract

Objectives:

Cognitive abilities have been implicated as predictors of mortality in older adults. This study examines the effects of cognitive training on mortality 20 years post-intervention.

Methods:

Data come from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) randomized control trial (N = 2802). Participants were cognitively and physically healthy, community-dwelling adults aged 65 and older. Cox proportional hazard models were used to investigate (1) the association between baseline cognition and mortality risk and (2) the effect of ACTIVE cognitive training (memory, reasoning, and speed of processing) on mortality risk 20 years post-intervention.

Results:

Higher baseline cognition predicted lower mortality risk 20 years post-intervention. No significant effects of ACTIVE cognitive training in memory, reasoning, or speed of processing on mortality risk were observed.

Discussion:

More work is needed to identify cognitive training interventions that may lead to lower mortality risks in later adulthood.

Keywords: cognitive training, mortality risk, cognitive decline, all-cause mortality

Introduction

Cognitive abilities and cognitive decline have been implicated as predictors of functional disability and mortality in older adults. Although there is evidence that initial levels of cognitive abilities, as well as cognitive declines, predict long-term mortality risk (Batterham et al., 2012; Bosworth et al., 1999; Van Gelder et al., 2007), to our knowledge, there have been no studies examining the impact of cognitive interventions on mortality outcomes in the older population. This study was designed to fill that gap using outcome data from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) trial, a large (N = 2802) randomized trial of the effects of three cognitive interventions (memory, reasoning, and speed of processing) on cognition and everyday function in a sample of cognitively normal, community-dwelling older adults.

Several studies have demonstrated positive health outcomes as a result of participation in cognitive training interventions, including enhanced cognitive performance and functional ability (Ball et al., 2002; Borella et al., 2010; Brehmer et al., 2012; Cantarella et al., 2017; Kelly et al., 2014; Rebok et al., 2014; Willis et al., 2006), lowered depressive symptoms (Motter et al., 2016; Wolinsky et al., 2009), improved sleep (Almondes et al., 2017; Diamond et al., 2015; Keramtinejad et al., 2019), and overall increases in general health and well-being (Wolinsky et al., 2006). Cognitive training may impact mortality risk through its beneficial effects on cognitive, functional, and physical and mental health outcomes. To our knowledge, there have been no studies examining the long-term effects of cognitive training on mortality risk.

Prior studies also suggest that the beneficial effects from participation in cognitive training differ based on certain participant characteristics. For example, in the ACTIVE study, participants with higher education showed greater improvement in memory performance after training (Rebok et al., 2013); lower education was associated with improvement in reasoning performance (Willis & Caskie, 2013). Similarly, beneficial effects of cognitive training among older adults have been found to differ by race; African Americans experienced less improvement in memory and reasoning ability compared to non-Hispanic whites after participation in cognitive training (Zahodne et al., 2015). Level of education and race, two participant characteristics that may moderate the effects of cognitive training, are also strongly associated with risk of mortality.

Many studies have documented associations between lower education and higher mortality risk (Hummer & Hernandez, 2013; Lleras-Muney, 2005; Sorlie et al., 1995). There are several, socially linked theories that have been proposed, for example, individuals with higher levels of education may be better able to understand and implement new health-related information (Phelan et al., 2004), have better access to health-related amenities (Everett et al., 2013), or learn to reduce or eliminate exposure to risk factors associated with preventable disease (e.g., smoking) (Masters et al., 2014).

Higher risk of mortality among African Americans (Howard et al., 2000; Hummer et al., 1999) compared to non-Hispanic whites can be attributed to historical and continued disparities in availability of socioeconomic resources (Hummer et al., 1999). Some literature also suggests a “crossover” effect in older adults where, relative to white older adults, African American older adults below age 75 have higher mortality rates but beginning around age 80, their mortality rate is lower (Roth et al., 2016). This finding holds even when adjusting for socioeconomic status, suggesting strong selective survival of older African American adults (Yao & Robert, 2011).

As education and race are both potential moderators of the effects of ACTIVE cognitive training on cognitive ability and strong predictors of mortality risk, the potential moderating role of these factors in the cognitive training—mortality relationship is important to consider. Sex, while not observed to moderate the effects of the ACTIVE cognitive training, has shown to moderate the effects of other cognitive training interventions (Rahe et al., 2015). Sex is also a strong predictor of mortality risk (Wu et al., 2021) and an important participant characteristic to consider in cognitive training—mortality relationships.

The present study aims to assess the impact of cognitive training on all-cause mortality over 20 years post-intervention. We assessed (1) the association between baseline cognitive ability and mortality risk and (2) the effect of participation in a cognitive training intervention (memory training, reasoning training, and speed of processing training) on mortality risk. We also assessed differences in the effect of cognitive training intervention on mortality by level of education, sex, and race. This study extends the existing literature on the long-term effects of cognitive training interventions.

Methods

Data for this research come from the ACTIVE study. ACTIVE is the largest randomized controlled trial to test whether cognitive training can improve basic cognitive abilities and help maintain functional independence in older adults. The ACTIVE intervention was conducted between March 1998 and October 1999 in six sites across the United States. ACTIVE participants were adults aged 65 years and older, community-dwelling, and cognitively and functionally healthy. Participants were free of severe sensory impairments and medical conditions likely to impact functioning or significantly increase mortality risk (Jobe et al., 2001).

ACTIVE randomized 2802 participants to one of three cognitive training intervention arms (memory, reasoning, or speed of processing) or to a no-contact control arm.

Each training included ten 60–75 minute small-group sessions across 5–6 weeks. Memory training targeted verbal episodic memory by teaching participants mnemonic strategies for learning and remembering different items (e.g., word lists and story details). Reasoning training targeted problem-solving abilities by helping participants recognize serial patterns in everyday activities. Speed of processing training targeted visual searching skills and ability to process complex information by administering increasingly complex speed tasks on a computer (Rebok et al., 2014). Follow-up assessments were conducted immediately post-intervention and at 1, 2, 3, 5, and 10 years after the intervention. More recently, as part of the ACTIVE 20-year follow-up study, ACTIVE data were linked to several administrative data sources. The current study uses the ACTIVE – National Death Index (NDI) linked data. For full details regarding the ACTIVE study design, see (Jobe et al., 2001) and (Ball et al., 2002).

Measures

Cognition.

Global cognition and three domain-specific measures of cognition were assessed. The Mini-Mental State Exam (MMSE) assesses language, memory, orientation, attention, and construction skills (Folstein et al., 1975) and has been widely used as a measure of general mental status to screen for dementia (Arevalo-Rodriguez et al., 2015). Scores range from 0 to 30, with lower scores reflecting poorer mental functioning. Cognitive tests used in ACTIVE assessed the targeted domains of the cognitive interventions (memory, reasoning, and speed of processing) (Jobe et al., 2001). The memory measures examined verbal episodic memory and included the Hopkins Verbal Learning Test (Brandt, 1991), the Rey Auditory-Verbal Learning Test (Schmidt, 1996), and the Rivermead Behavioral Paragraph Recall Test (Wilson et al., 1999). The reasoning measures examined identification of patterns and included the total correct number of items for Letter Sets (Thurstone & Thurstone, 1949), Letter Series (Ekstrom, 1976), and Word Series (Gonda, 1985). The speed of processing measures included three Useful Field of View (UFOV) conditions (Ball et al., 1988), which required identification and location of visual information under varying levels of cognitive demand. We used composite scores created by (Jobe et al., 2001) for each domain of cognitive function using the average of the standardized scores for each test in that composite measure.

Cognitive Training Intervention

Cognitive training intervention group was modeled as a 4-category variable (memory training, reasoning training, speed of processing training, and control [reference]).

Mortality Status

Mortality status was determined through linkage to the NDI. Participants were classified as deceased if they were successfully matched via personal identifiable information to an NDI record. Mortality status was also determined through surveillance of public record databases (Lexis Nexis, ancestry.com, and public obituaries). The analytic sample captures deaths up to 20 years after the ACTIVE intervention. Participants (n = 16) whose personal identifiable information was unavailable were excluded from the analyses.

Covariates

Sociodemographic factors included: age (in years); sex (female or male); race/ethnicity (white, African American, or other [includes Asian, Indian, biracial, and other]; and education (in years). Depressive symptoms were assessed using an abbreviated Center for Epidemiologic Studies-Depression (CES-D) scale (Radloff, 1977). The 12-item version of this measure asks participants to report frequency of depressive symptoms from the prior week on a four-level scale: rarely or none of the time, some of the time, much of the time, and most or all of the time. Total scores range from 0 (no depressive symptoms) to 36. Physical functioning was assessed using the Medical Outcomes Study 36-Item Short Form physical functioning scale (Ware & Sherbourne, 1992). Physical functioning (e.g., ability to lift and carry groceries) was assessed on a 3-level continuum. Scores for this domain range from 0 to 100, with higher scores reflecting better physical health. Finally, chronic comorbidities were included as a measure of physical health. The measure of chronic conditions reflects the total number of all self-reported chronic conditions (i.e., presence of hypertension, stroke, heart disease, congestive heart failure, high cholesterol, cancer, asthma, cataracts, diabetic retinopathy, glaucoma, macular degeneration, and osteoporosis). Intervention site (University of Alabama, Birmingham, Indiana University School of Medicine, Hebrew Rehabilitation Center for the Aged, Johns Hopkins University, Wayne State University, and Penn State University) and indicators for each wave of testing and training (replicate) were included as study design covariates.

Statistical Analyses

Four cox proportional hazards models were used to separately investigate the associations between baseline cognitive score (MMSE, memory score, reasoning score, and speed of processing score) and 20-year mortality risk. Time was modeled as time since the study’s baseline visit (study initiation). The proportional hazard assumption was checked by examining Kaplan–Meier curves and Schoenfeld residuals.

Cox proportional hazards models were also used to investigate the effect of cognitive training intervention group (memory, reasoning, speed of processing, and control [reference]) and 20-year mortality risk. To further assess the effect of cognitive training on mortality, the effect of booster training on mortality was assessed via Cox proportional hazards models. Booster training, a refresher session of the tasks and strategies taught during training, occurred 1 and 3 years after the intervention for a randomized subsample of participants. Participants who attended at least 80% of intervention training sessions were eligible for booster training. Booster training was modeled by a 7-level categorical variable (memory [no booster], memory [booster], reasoning [no booster], reasoning [booster], speed of processing [no booster], speed of processing [booster], and control [reference]). The effect of reliable improvement on training-specific cognitive tests at post-test on mortality was also assessed. Participants who, at post-test, performed at least 1 standard error of measurement (SEM) higher than their baseline performance on training-specific cognitive tests were classified as showing reliable improvement after training. One SEM was calculated following methods from Dudek (Ball et al., 2002; Dudek, 1979). Reliable improvement was modeled by a 7-level categorical variable (memory [no reliable improvement], memory [reliable improvement], reasoning [no reliable improvement], reasoning [reliable improvement], speed of processing [no reliable improvement], speed of processing [reliable improvement], and control [reference]).

Finally, differences in the effect of cognitive training intervention on mortality risk by education, sex, and race were independently assessed. An interaction term between intervention group and education, sex, and race, respectively, was added to models assessing the effect of cognitive training intervention on mortality risk.

All models were adjusted for sex, age, race, years of education, number of depressive symptoms, physical functioning, number of chronic conditions, intervention site, intervention group, and replicate. Analyses were conducted in Stata 15.

Results

Participant Characteristics

Participant demographics were highly similar across training groups. The mean age of participants at baseline was approximately 73 years old in all groups, and roughly 75% of the sample was female (range: 73.6–76.8% across intervention groups) and white (range: 70.8–74.0%). All groups showed similar education levels (mean near 13.5 years), and similar scores on cognitive measures (e.g., MMSE, domain scores) at baseline. A full description of sample characteristics is available in Table 1.

Table 1.

Demographic characteristics of 2802 ACTIVE participants at baseline, stratified by intervention group.

Memory (N = 703) Reasoning (N = 699) Speed of processing (N = 702) Control (N = 698)
Demographic characteristics
 Age at baseline (mean, SD)  73.22 (5.87)    73.26 (5.56)     73.08 (5.56)  73.79 (5.92)
 Sex (N, % female)     537 (76.4%)       537 (76.8%)        538 (76.6%)     514 (73.6%)
Race N (%)
 White     520 (74.0)       495 (70.8)        517 (73.6)     496 (71.1)
 Black     176 (25.0)       190 (27.2)        175 (24.9)     187 (26.8)
 Other      7 (1.0)      14 (2.0)       10 (1.5)    15 (2.1)
Education and cognitive scores (mean, SD)
 Years of education  13.67 (2.69)    13.55 (2.70)     13.69 (2.63)  13.50 (2.65)
 MMSE total  27.29 (2.05)    27.26 (2.01)     27.43 (1.97)  27.27 (2.00)
 Functional ability  −0.02 (2.03)      0.09 (2.09)     −0.06 (2.02)  −0.02 (2.00)
 Memory score    0.04 (2.41)    −0.00 (2.31)       0.02 (2.32)  −0.14 (2.37)
 Reasoning score    0.12 (2.69)    −0.02 (2.68)       0.07 (2.57)  −0.17 (2.71)
 Speed score  −0.10 (2.52)    −0.03 (2.45)       0.02 (2.40)    0.11 (2.46)
Mortality status—deceased N (%)     493 (70.1%)       516 (73.8%)        492 (70.0%)     508 (72.8%)
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Association between Cognition and 20-Year Mortality Risk

Results from four, independent Cox proportional hazards models evaluating the association between each cognitive score and 20-year mortality risk are shown in Table 2 (full results shown in Supplemental Tables A1–A4). After full adjustment for demographic, health, and study design characteritics, higher performance at baseline on the MMSE was associated with 3% lower hazard of mortality (HR: 0.97, 95% CI: 0.94–0.99). Higher baseline performance on tests of memory, reasoning, and speed of processing was each independently associated with 6% lower hazard of mortality over 20 years (memory: HR: 0.94; 95% CI: 0.92–0.96; reasoning; HR: 0.94; 95% CI: 0.92–0.97; speed of processing: HR: 0.94; 95% CI: 0.92–0.96).

Table 2.

Results from four proportional hazards models assessing association between baseline cognition (MMSE, memory, reasoning, and speed) and 20-year survival, ACTIVE randomized control trial (N = 2802).

Model 1
 Model 2
 Model 3
Hazard ratio 95% CI p-value Hazard ratio 95% CI p-value Hazard ratio 95% CI p-value
MMSE score 0.93 (0.92, 0.95) 0.00 0.97 (0.95, 0.99) 0.01 0.97 (0.94, 0.99) 0.01
Memory score 0.87 (0.86, 0.89) 0.00 0.94 (0.92, 0.95) 0.00 0.94 (0.92, 0.96) 0.00
Reasoning score 0.90 (0.89, 0.92) 0.00 0.94 (0.92, 0.95) 0.00 0.94 (0.92, 0.97) 0.00
Speed score 0.86 (0.85, 0.88) 0.00 0.93 (0.91, 0.95) 0.00 0.94 (0.92, 0.96) 0.00
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Model 1: Unadjusted.

Model 2: Adjusted for age, sex, levels of education, and race/ethnicity.

Model 3: Adjusted for age, sex, levels of education, and race/ethnicity, intervention site, replicate code, number of chronic conditions, CESD depression score, and SF-36 self-rated health.

Effect of Cognitive Training Intervention on 20-Year Mortality Risk

Results from Cox proportional hazards models evaluating the effect of cognitive training intervention group (memory, reasoning, speed of processing, and control [reference]) and 20-year mortality risk are shown in Table 3 [full results shown in Supplemental Table A5]. In the fully adjusted model, the memory, reasoning, and speed of processing intervention groups were not significantly associated with time to mortality compared to the control group. In secondary analyses, neither additional booster training nor reliable improvement on training-specific cognitive tests was significantly associated with mortality risk (results not shown).

Table 3.

Cox proportional hazard survival analyses modeling the effect of cognitive training intervention on mortality over 20 years, ACTIVE randomized control trial (N = 2802).

Model 1
 Model 2
 Model 3
Hazard ratio 95% CI p-value Hazard ratio 95% CI p - value Hazard ratio 95% CI p-value
Control 1 (Reference) – – 1 (Reference) – – 1 (Reference) – –
Memory intervention 0.94 (0.83, 1.06) 0.32 1.00 (0.88, 1.12) 0.94 0.97 (0.86, 1.11) 0.68
Reasoning intervention 1.01 (0.90, 1.14) 0.83 1.06 (0.94, 1.20) 0.32 0.99 (0.87, 1.12) 0.88
Speed intervention 0.96 (0.85, 1.08) 0.51 1.01 (0.90, 1.15) 0.81 1.00 (0.88, 1.13) 0.95
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Model 1: Unadjusted.

Model 2: Adjusted for age, sex, levels of education, and race/ethnicity.

Model 3: Adjusted for age, sex, levels of education, and race/ethnicity, intervention site, replicate code, number of chronic conditions, CESD depression score, and SF-36 self-rated health.

Differences in the Effect of Cognitive Training Intervention on 20-Year Mortality Risk by Education, Sex, and Race

Estimates of mortality risk associated with memory, reasoning, and speed of processing intervention groups (ref: control) were similar (p > .10) by sex and by race (Figure 1, Supplemental Tables A6–A8). However, an intervention by education interaction effect was observed for the memory (p-interaction = 0.00) and speed of processing (p-interaction = 0.01) intervention groups. Among participants with lower education (≤12 years), memory (HR: 1.64; 95% CI: 1.14, 2.36) and speed of processing (HR: 1.60; 95% CI: 1.09, 2.36) training were associated with a higher hazard of mortality compared to the control group (reference); however estimates may lack precision given the wide confidence intervals surrounding these estimates. No difference in mortality risk was observed between intervention and control (reference) groups among participants with higher levels of education (>12 years).

Figure 1.

Figure 1.

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Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of the relationship between cognitive training intervention (memory, reasoning, speed, and control) and 20-year mortality by years of education, sex, and race, ACTIVE randomized control trial (N = 2802).

Discussion

In agreement with previous research, our findings show that higher baseline levels of cognitive performance are related to lower mortality risk. However, improvement of cognitive abilities via cognitive training appears to be unrelated to subsequent mortality. No differences in the cognitive training—mortality relationship were observed by sex or race; however, the hazard of mortality associated with memory and speed of processing training (vs. control) was significantly higher among participants with ≤12 years of education, and a trend approaching significance for the same effect was found for reasoning training.

To our knowledge, this is the first study investigating the relationship between cognitive training and long-term effects on all-cause mortality. Several factors may explain the observed null effect of cognitive training intervention on mortality risk in ACTIVE. First, ACTIVE trained participants in a single cognitive domain (memory, reasoning, or speed of processing). This targeted training, which has beneficial effects for some health outcomes (Wolinsky et al., 2006, 2009, 2010) may not be adequate for impacting mortality risk. A more comprehensive intervention that includes cognitive training in multiple cognitive domains and/or cognitive training in combination with other lifestyle interventions (e.g., diet, physical activity, and blood pressure management) may have a stronger impact on mortality risk. Single-domain cognitive training and multi-domain training may differ in transfer effects, with the latter potentially contributing more to far transfer effects (generalization of training effects to tasks dissimilar from training tasks) compared to near transfer effects (improvement on tasks very similar to training tasks) more robustly (Barnett & Ceci, 2002; Chan et al., 2019; Wolinsky et al., 2016).

Additionally, ACTIVE was a 10-session intervention administered over 5–6 weeks. Although the effects of training showed some maintenance over 10 years (Rebok et al., 2014), the intervention dose and level of maintenance of training effects over time may not be sufficient for altering mortality risk and other far transfer outcomes over nearly two decades. Longer duration and/or increased dosage of cognitive training may potentially increase far transfer by inducing long-term changes in brain structure (Lövdén et al., 2010; Smith et al., 2009; Wolinsky et al., 2016). Furthermore, lack of consistent use of strategies introduced during cognitive training over post-intervention follow-up may partially explain observed null effects. Although this study cannot comment on consistency in participants’ use of strategies in everyday life post-intervention, no difference in mortality risk was observed among participants who received booster training 1 and 3 years post-intervention compared to those who did not, suggesting more robust efforts to promote use of trained strategies over time may be important for altering mortality risk. More research is needed in this area.

Our findings also suggest that among participants with lower levels of education, an increased risk of mortality among those who received cognitive training relative to participants in the control group. However, we did not find evidence that this increase in mortality is related to factors hypothesized to mediate the relationship between cognitive training and mortality, such as dose of training or training-related gains on the trained cognitive abilities. We also did not observe any differences in participant characteristics (e.g., age and number of chronic conditions) between intervention groups and control by level of education that could potentially explain these findings. Therefore, one interpretation is this is a chance finding. Nevertheless, as all trained groups suggest an apparent adverse effect of training on mortality, this is curious and remains unexplained. It is possible that low-educated individuals found the training more stress-inducing, leading to negative psychological (e.g., negative self-identity perceptions) and physiological (e.g., elevated cortisol levels) responses associated with increased mortality (e.g., [Wolinsky et al., 2020]). However, since we did not measure these variables, we can only speculate about the etiologic mechanisms underlying the education-training interactions. Future research should investigate potential differences in the cognitive training-mortality relationship by level of education.

Limitations and Strengths

It is important to note the limitations of the current study. First, this study focuses on the relationship between cognitive training and all-cause mortality. Studies that might assess the effect of training on cause-specific mortality would require much larger sample sizes. Additionally, mortality status may have been misclassified for participants with incomplete data for identifiers (e.g., social security number, date of birth, and sex) used for linkage of study data to NDI data. However, to minimize the possibility of misclassification, the study team conducted additional searches of public record obituaries and LexisNexis and Ancestry.com databases to confirm mortality status, and only a small percentage of participants were excluded due to their uncertain mortality status. Finally, participants were included in the ACTIVE study if they were, at baseline, living independently and cognitively and physically healthy; thus, generalizability of results to the broader population of older adults is limited. ACTIVE participants were also mostly white and African American; thus, generalizability of findings to other racial/ethnic groups is limited.

The strengths of this study include its large sample size and prospective longitudinal design spanning 20 years. The study also included three standardized cognitive interventions that were delivered with high fidelity by trained facilitators. All three interventions led to significant gains on the proximal cognitive ability targets, making it less likely that the lack of effects on the mortality outcome was due to lack of training effectiveness. Rather, the findings suggest the potential need to combine cognitive interventions with other lifestyle modifications (e.g., improved diet and exercise, better sleep, and reduced stress) to optimize their effectiveness on longevity.

This is the first study to investigate the long-term effects of cognitive training on subsequent mortality in a community-dwelling sample of healthy older adults. Future investigations should focus on testing the impact of other cognitive training interventions at higher doses, either alone or in combination, on mortality risk. These interventions could include different types of cognitive training embedded within lifestyle-focused weight, stress, physical activity, and sleep management interventions. Future research should also include analyses on outcomes that have been found to improve with cognitive training (e.g., depression, health-related quality of life, and sleep) that may mediate/moderate the training-mortality relationship. In studying this relationship, it will be important to examine not only all-cause mortality as an outcome but also cause-specific mortality. We plan to examine Medicare claims data to see if cognitive training is associated with decreased risk of Alzheimer’s dementia in the long term, and dementia-related mortality. Researchers should also consider studying cognitive training effects on terminal decline as a mortality-related correlate of aging changes (Gerstorf et al., 2011). Finally, the unexpected finding of differential impact of cognitive training by education status on mortality risk highlights the need for further research on both the quality and quantity of education as a potential moderator of training effects on subsequent mortality

Cognitive training does not appear to lower the risk of all-cause mortality among cognitively normal older adults. However, baseline cognitive performance levels do predict future mortality risk, independent of other risk factors, and may affect long-term survival. Future research is needed to further understand the effect of cognitive training on mortality and other distal outcomes in later life.

Supplementary Material

Supplemental

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The current study is supported by NIA R01 AG056486. The ACTIVE Cognitive Training Trial was supported by grants from the National Institutes of Health to six field sites and the coordinating center, including: Hebrew Senior-Life, Boston (NR04507), Indiana University School of Medicine (NR04508), Johns Hopkins University (AG014260), New England Research Institutes (AG014282), Pennsylvania State University (AG14263), University of Alabama at Birmingham (AG14289), and University of Florida (AG014276). The authors report no conflicts of interest. The opinions here are those of the authors and do not necessarily reflect those of the funding agencies, academic, research, governmental institutions, or corporations involved.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

References

  1. Arevalo-Rodriguez I, Smailagic N, Roqué i Figuls MR, Ciapponi A, Sanchez-Perez E, Giannakou A, Pedraza OL, Cosp XB, & Cullum S (2015). Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database of Systematic Reviews, 3:CD010783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ball K, Berch DB, Helmers KF, Jobe JB, Leveck MD, Marsiske M, Morris JN, Rebok GW, Smith DM, Tennstedt SL, Unverzagt FW, & Willis SL (2002). Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA, 288(18), 2271–2281. 10.1001/jama.288.18.2271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ball KK, Beard BL, Roenker DL, Miller RL, & Griggs DS (1988). Age and visual search: Expanding the useful field of view. JOSA A, 5(12), 2210–2219. 10.1364/josaa.5.002210 [DOI] [PubMed] [Google Scholar]
  4. Barnett SM, & Ceci SJ (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. 10.1037/0033-2909.128.4.612 [DOI] [PubMed] [Google Scholar]
  5. Batterham PJ, Mackinnon AJ, & Christensen H (2012). The association between change in cognitive ability and cause-specific mortality in a community sample of older adults. Psychology and Aging, 27(1), 229–236. 10.1037/a0024517 [DOI] [PubMed] [Google Scholar]
  6. Borella E, Carretti B, Riboldi F, & De Beni R (2010). Working memory training in older adults: Evidence of transfer and maintenance effects. Psychology and Aging, 25(4), 767–778. 10.1037/a0020683. [DOI] [PubMed] [Google Scholar]
  7. Bosworth HB, Schaie KW, & Willis SL (1999). Cognitive and sociodemographic risk factors for mortality in the Seattle Longitudinal Study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 54(5), P273–P282. 10.1093/geronb/54b.5.p273 [DOI] [PubMed] [Google Scholar]
  8. Brandt J (1991). The Hopkins Verbal Learning Test: Development of a new memory test with six equivalent forms. The Clinical Neuropsychologist, 5(2), 125–142. 10.1080/13854049108403297 [DOI] [Google Scholar]
  9. Brehmer Y, Westerberg H, & Bäckman L (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6, 63. 10.3389/fnhum.2012.00063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cantarella A, Borella E, Carretti B, Kliegel M, & de Beni R (2017). Benefits in tasks related to everyday life competences after a working memory training in older adults. International Journal of Geriatric Psychiatry, 32(1), 86–93. 10.1002/gps.4448 [DOI] [PubMed] [Google Scholar]
  11. Chan S, Mueller U, & Masson MEJ (2019). Fartransfer effects of strategy-based working memory training. Frontiers inPsychology, 10, 1285. 10.3389/fpsyg.2019.01285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Almondes KM, Leonardo MEM, & Moreira AMS (2017). Effects of a cognitive training program and sleep hygiene for executive functions and sleep quality in healthy elderly. Dementia & Neuropsychologia, 11 (1), 69–78. 10.1590/1980-57642016dn11-010011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dudek FJ (1979). The continuing misinterpretation of the standard error of measurement. Psychological Bulletin, 86(1), 335–337. 10.1037/0033-2909.86.2.335 [DOI] [Google Scholar]
  14. Diamond K, Mowszowski L, Cockayne N, Norrie L, Paradise M, Hermens DF, Lewis SJ, Hickie IB, & Naismith SL (2015). Randomized controlled trial of a healthy brain ageing cognitive training program: Effects on memory, mood, and sleep. Journal of Alzheimer’s Disease, 44(4), 1181–1191. 10.3233/JAD-142061 [DOI] [PubMed] [Google Scholar]
  15. Ekstrom RB (1976). Kit of factor-referenced cognitive tests. Educational Testing Service. [Google Scholar]
  16. Everett BG, Rehkopf DH, & Rogers RG (2013). The nonlinear relationship between education and mortality: An examination of cohort, race/ethnic, and gender differences. Population Research and Policy Review, 32(6), 893–917. 10.1007/s11113-013-9299-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Folstein MF, Folstein SE, & McHugh PR (1975). Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
  18. Gerstorf D, Ram N, Hoppmann C, Willis SL, & Schaie KW (2011). Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study. Developmental Psychology, 47(4), 1026–1041. 10.1037/a0023426 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gonda J (1985). Schaie-thurstone mental abilities test: Word Series test. Consulting Psychologists Press. [Google Scholar]
  20. Howard G, Anderson RT, Russell G, Howard VJ, & Burke GL (2000). Race, socioeconomic status, and cause-specific mortality. Annals of Epidemiology, 10(4), 214–223. 10.1016/s1047-2797(00)00038-7 [DOI] [PubMed] [Google Scholar]
  21. Hummer RA, & Hernandez EM (2013). The effect of educational attainment on adult mortality in the United States. Population Bulletin, 68(1), 1–16. [PMC free article] [PubMed] [Google Scholar]
  22. Hummer RA, Rogers RG, Nam CB, & LeClere FB (1999). Race/ethnicity, nativity, and US adult mortality. Social Science Quarterly, 136–153. [Google Scholar]
  23. Jobe JB, Smith DM, Ball K, Tennstedt SL, Marsiske M, Willis SL, Rebok GW, Morris JN, Helmers KF, Leveck MD, & Kleinman K (2001). ACTIVE: A cognitive intervention trial to promote independence in older adults. Controlled Clinical Trials, 22(4), 453–479. 10.1016/s0197-2456(01)00139-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kelly ME, Loughrey D, Lawlor BA, Robertson IH, Walsh C, & Brennan S (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Ageing Research Reviews, 15, 28–43. 10.1016/j.arr.2014.02.004 [DOI] [PubMed] [Google Scholar]
  25. Keramtinejad M, Azadi A, Taghinejad H, & Khorshidi A (2019. Apr–Jun). The effectiveness of cognitive training on improving cognitive function and sleep quality in community-dwelling elderly in Iran. Sleep Science, 12(2), 88–93. 10.5935/1984-0063.20190065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lleras-Muney A (2005). The relationship between education and adult mortality in the United States. The Review of Economic Studies, 72(1), 189–221. 10.1111/0034-6527.00329 [DOI] [Google Scholar]
  27. Lövdén M, Bäckman L, Lindenberger U, Schaefer S, & Schmiedek F (2010). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659. [DOI] [PubMed] [Google Scholar]
  28. Masters RK, Hummer RA, Powers DA, Beck A, Lin SF, & Finch BK (2014). Long-term trends in adult mortality for US blacks and whites: An examination of period-and cohort-based changes. Demography, 51(6), 2047–2073. 10.1007/s13524-014-0343-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Motter JN, Pimontel MA, Rindskopf D, Devanand DP, Doraiswamy PM, & Sneed JR (2016). Computerized cognitive training and functional recovery in major depressive disorder: A meta-analysis. Journal of Affective Disorders, 189, 184–191. 10.1016/j.jad.2015.09.022 [DOI] [PubMed] [Google Scholar]
  30. Phelan JC, Link BG, Diez-Roux A, Kawachi I, & Levin B (2004). Fundamental causes” of social inequalities in mortality: A test of the theory. Journal of Health and Social Behavior, 45(3), 265–285. 10.1177/002214650404500303 [DOI] [PubMed] [Google Scholar]
  31. Radloff LS (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. 10.1177/014662167700100306 [DOI] [Google Scholar]
  32. Rahe J, Liesk J, Rosen JB, Petrelli A, Kaesberg S, Onur OA, Kessler J, Fink GR, & Kalbe E (2015). Sex differences in cognitive training effects of patients with amnestic mild cognitive impairment. Aging, Neuropsychology, and Cognition, 22(5), 620–638. 10.1080/13825585.2015.1028883 [DOI] [PubMed] [Google Scholar]
  33. Rebok GW, Ball K, Guey LT, Jones RN, Kim HY, King JW, Marsiske M, Morris JN, Tennstedt SL, Unverzagt FW, & Willis SL (2014). Ten-year effects of the ACTIVE cognitive training trial on cognition and everyday functioning in older adults. Journal of the American Geriatrics Society, 62(1), 16–24. 10.1111/jgs.12607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rebok GW, Langbaum JB, Jones RN, Gross AL, Parisi JM, Spira AP, Kueider AM, Petras H, & Brandt J (2013). Memory training in the ACTIVE study: How much is needed and who benefits? Journal of Aging and Health, 25(8 Suppl), 21S–42S. 10.1177/0898264312461937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Roth DL, Skarupski KA, Crews DC, Howard VJ, & Locher JL (2016). Distinct age and self-rated health crossover mortality effects for African Americans: Evidence from a national cohort study. Social Science & Medicine, 156, 12–20. 10.1016/j.socscimed.2016.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schmidt M (1996). Rey auditory verbal learning test: A handbook. Western Psychological Services. [Google Scholar]
  37. Smith GE, Housen P, Yaffe K, Ruff R, Kennison RF, Mahncke HW, & Zelinski EM (2009). A cognitive training program based on principles of brain plasticity: Results from the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) Study. Journal of the American Geriatrics Society, 57(4), 594–603. 10.1111/j.1532-5415.2008.02167.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sorlie PD, Backlund E, & Keller JB (1995). US mortality by economic, demographic, and social characteristics: The National Longitudinal Mortality Study. American Journal of Public Health, 85(7), 949–956. 10.2105/ajph.85.7.949 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Thurstone LL, & Thurstone TG (1949). Examiner Manual for the SRA primary mental abilities test (Form 10-14). Science Research Associates. [Google Scholar]
  40. Van Gelder BM, Tijhuis MA, Kalmijn S, Giampaoli S, & Kromhout D (2007). Decline in cognitive functioning is associated with a higher mortality risk. Neuroepidemiology, 28(2), 93–100. 10.1159/000098552 [DOI] [PubMed] [Google Scholar]
  41. Ware JE Jr., & Sherbourne CD (1992). The MOS 36-item shortform health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. 10.1097/00005650-199206000-00002 [DOI] [PubMed] [Google Scholar]
  42. Willis SL, & Caskie GI (2013). Reasoning training in the ACTIVE study: How much is needed and who benefits? Journal of Aging and Health, 25(8 Suppl), 43S–64S. 10.1177/0898264313503987 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Willis SL, Tennstedt SL, Marsiske M, Ball K, Elias J, Koepke KM, Morris JN, Rebok GW, Unverzagt FW, Stoddard AM, & Wright E (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA, 296(23), 2805–2814. 10.1001/jama.296.23.2805 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wilson BA, Clare L, Cockburn J, Baddeley AD, Tate R, & Watson P (1999). The Rivermead behavioural memory test-extended version. [DOI] [PubMed] [Google Scholar]
  45. Wolinsky FD, Jones MP, & Dotson MM (2020). Does visual speed of processing training improve health-related quality of life in assisted and independent living communities? A randomized controlled trial. Innovation in Aging, 4(4), igaa029. 10.1093/geroni/igaa029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wolinsky FD, Mahncke HW, Weg MW, Martin R, Unverzagt FW, Ball KK, Jones RN, & Tennstedt SL (2009). The ACTIVE cognitive training interventions and the onset of and recovery from suspected clinical depression. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 64(5), 577–585. 10.1093/geronb/gbp061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wolinsky FD, Unverzagt FW, Smith DM, Jones R, Wright E, & Tennstedt SL (2006). The effects of the ACTIVE cognitive training trial on clinically relevant declines in health-related quality of life. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 61(5), S281–S287. https://doi.org/0.1093/geronb/61.5.s281 [DOI] [PubMed] [Google Scholar]
  48. Wolinsky FD, Vander Weg MW, Howren MB, Jones MP, & Dotson MM (2016). Effects of cognitive speed of processing training on a composite neuropsychological outcome: Results at one-year from the IHAMS randomized controlled trial. International Psychogeriatrics, 28(2), 317–330. 10.1017/S1041610215001428 [DOI] [PubMed] [Google Scholar]
  49. Wolinsky FD, Vander Weg MW, Martin R, Unverzagt FW, Willis SL, Marsiske M, Rebok GW, Morris JN, Ball KK, & Tennstedt SL (2010). Does cognitive training improve internal locus of control among older adults? Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 65(5), 591–598. 10.1093/geronb/gbp117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Wu YT, Niubo AS, Daskalopoulou C, Moreno-Agostino D, Stefler D, Bobak M, Oram S, Prince M, & Prina M (2021). Sex differences in mortality: Results from a population-based study of 12 longitudinal cohorts. CMAJ, 193(11), E361–E370. 10.1503/cmaj.200484 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Yao L, & Robert SA (2011). Examining the racial crossover in mortality between African American and white older adults: A multilevel survival analysis of race, individual socioeconomic status, and neighborhood socioeconomic context. Journal of Aging Research, 2011, 132073. 10.4061/2011/132073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Zahodne LB, Meyer OL, Choi E, Thomas ML, Willis SL, Marsiske M, Gross AL, Rebok GW, & Parisi JM (2015). External locus of control contributes to racial disparities in memory and reasoning training gains in ACTIVE. Psychology and Aging, 30(3), 561–572. 10.1037/pag0000042 [DOI] [PMC free article] [PubMed] [Google Scholar]

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