Encyclopedia of Gerontology and Population Aging

Living Edition
| Editors: Danan Gu, Matthew E. Dupre

Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE)

  • Kelsey R. ThomasEmail author
  • George W. Rebok
  • Sherry L. Willis
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-69892-2_1075-1

Project Goals/Missions/Objectives

The primary goal of the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) trial has been to determine whether cognitive training interventions in the domains of memory, reasoning, and visual speed of processing can improve cognitively demanding everyday functioning activities (Jobe et al. 2001) (See “Cognitive Training”). The secondary goal was to determine the process by which the interventions impacted daily functioning, including whether improvement on the proximal cognitive outcomes of memory, reasoning, or speed mediated this process (Jobe et al. 2001; Gross et al. 2018). To date, ACTIVE is the largest and longest-running cognitive training trial for cognitively normal older adults, funded by NIH, and continues to be an important study for understanding how factors such as health, mood, self-efficacy, and cognitive impairment may moderate intervention effects.

Study Design and Features


ACTIVE is a multisite, randomized, controlled, single-masked cognitive intervention trial in older adults that was co-sponsored by the National Institute on Aging and the National Institute of Nursing Research (Jobe et al. 2001). It used a three-group design, including three treatment conditions (memory, reasoning, or speed of processing) and a no-contact control group. The six field sites included: University of Alabama at Birmingham, Boston Hebrew Rehabilitation Center for Aged, Indiana University School of Medicine, Johns Hopkins University, Pennsylvania State University, and Wayne State University. Eligible community-dwelling adults aged 65 and older were recruited from community centers, senior housing, churches, hospitals and clinics, and various registries and rosters (e.g., state driver’s license and identification card registry, rosters of assistance/service programs for low-income older adults) between March 1998 and October 1999.


Exclusion criteria included (a) significant cognitive dysfunction, defined by a Mini-Mental State Examination (MMSE) score <24; (b) functional impairment, defined by dependency or regular assistance in activities of daily living (ADLs) on Minimum Dataset (MDS) Home Care; (c) self-reported diagnoses of Alzheimer’s disease, stroke within the last 12 months, or certain cancers; (d) current chemotherapy or radiation therapy; or (e) poor vision (self-reported or worse than 20/70 with correction), hearing, or communicative ability (interviewer-rated) that would have significantly interfered with the interventions or outcome assessments (Jobe et al. 2001; Ball et al. 2002; Willis et al. 2006; Rebok et al. 2014). Five thousand participants were contacted for the study, and 2832 were enrolled and randomized (905 ineligible, 1263 refused) (Ball et al. 2002; Willis et al. 2006); due to a randomization error of 30 participants, the analytic sample consists of 2802 participants. These 2802 participants had a baseline mean age of 73.6 years, mean education of 13 years, were 26% Black/African American, and were 76% female.


Participants were randomized into one of four groups, which include three treatment arms (memory, reasoning, or speed of processing) and a no-contact control group. The three training conditions that were selected for use in ACTIVE were chosen due to prior evidence that these interventions improve cognition as well as due to studies that show speeded visual abilities, memory, and reasoning are important for aspects of everyday functioning (e.g., Willis 1987; Ball et al. 1988; Rebok and Balcerak 1989). The interventions included ten 60–75-min sessions over the course of 5–6 weeks and were conducted in small groups. Across intervention arms, the first 5 sessions focused on strategy introduction and practice, while sessions 6–10 focused on additional practice but did not introduce new strategies.

The memory training taught mnemonic strategies (organization, visualization, association) for remembering verbal material (e.g., word lists, text). Reasoning training taught strategies for finding the pattern in a letter or word series (e.g., a c e g i …) and identifying the text item in the series. Speed of processing training involved visual search and divided attention (identifying an object on a computer screen at increasingly shorter exposure times, followed by dividing attention between two search tasks). Participants who completed the initial training (at least 8 of 10 sessions) were eligible for booster training, and a random 60% of the eligible sample were recruited to booster training. Booster training was conducted at 11- and 35-months post-training and involved four 75-minute sessions. The goal of the booster sessions was to maintain the improvement in cognitive ability; the content of these sessions was similar to the training sessions (Ball et al. 2002; Willis et al. 2006).


Proximal outcomes were those cognitive tests in the domains of the cognitive interventions (memory, reasoning, speed). The proximal memory measures examined verbal episodic learning/memory and included the Hopkins Verbal Learning Test total of three learning trials and recognition, the Rey Auditory Verbal Learning Test total of five learning trials and recognition, and the Rivermead Behavioural Paragraph Recall test immediate recall (See “Episodic Memory”). The proximal reasoning measures of Letter Sets, Letter Series, and Word Series examined identification of patterns and included the total correct number of items for each of the three measures. The proximal speed of processing measures included three Useful Field of View (UFOV) conditions, which required speeded identification and location of visual information under increasing levels of cognitive demand (Jobe et al. 2001; Ball et al. 2002) (See “Speed of Processing”).

Primary outcomes included measures of everyday functioning, both self-reported and performance-based. The self-reported measure of instrumental activities of daily living (IADL) was the IADL difficulty subscore from the MDS Home Care, which asked about 19 daily tasks (e.g., finances, meal preparation) and how difficult these tasks were for the participant over the past 7 days. There were two performance-based measures in the domain of everyday problem-solving: the Everyday Problems Test and Observed Tasks of Daily Living. There were also two performance-based measures in the domain of everyday speed: the Timed IADL and Complex Reaction Time (Jobe et al. 2001; Willis et al. 2006; Rebok et al. 2014).

The secondary outcomes included everyday mobility, health service utilization, and health-related quality of life. Additional measures of demographics, physical health (chronic disease, health behaviors, medications), depressive symptoms, psychosocial and self-efficacy factors, and cognitive screening were also collected and considered covariates. Measures were completed at the initial baseline occasion, immediately post-intervention (<10 days post-training), as well as at 1-, 2-, 3-, 5-, and 10-years post-training.

Major Findings

Intervention Outcomes

Findings from the ACTIVE study provide strong support for the efficacy of cognitive training. Each of the three interventions (memory, reasoning, speed) significantly improved the targeted proximal cognitive ability relative to the control group at immediate posttest and at 2-year and 5-year follow-up occasions (Ball et al. 2002; Willis et al. 2006). The reasoning and speed-trained groups also performed higher than controls at the 10-year follow-up occasion (Rebok et al. 2014). Related to the primary outcomes of everyday functioning, there were no treatment effects on everyday functioning at immediate posttest or at the 2-year follow-up; at the 5-year follow-up, the reasoning group reported significantly less difficulty in IADLs than the control group (Willis et al. 2006); at the 10-year follow-up, all treatment groups reported significantly less difficulty performing IADLs than the control group (Rebok et al. 2014). There were no significant intervention effects on performance-based simulated measures of everyday problem-solving or everyday speed; however, those in the speed and reasoning groups had fewer motor vehicle accidents (Ball et al. 2010). Taken together, these results suggest that cognitive training shows immediate benefit to the cognitive domain that was trained, but longer follow-up periods (i.e., 5–10 years) may be necessary before the training may be related to slower functional decline.

In investigations of who benefits from the cognitive interventions, memory training was associated with better memory performance over 5 years, but neither booster training nor adherence to the treatment significantly moderated this effect. Higher self-rated health status and higher education were associated with greater improvement in memory performance after training (Rebok et al. 2013). Related to the reasoning training, treatment adherence resulted in greater benefit from training. Having a lower level of education was associated with increased training effects for two of the reasoning measures, and participants with lower baseline MMSE scores had larger booster training effects (particularly the booster that occurred 3 years after training) on an overall reasoning composite score (Willis and Caskie 2013). Finally, for the speed training intervention, there were no differences in immediate training gain related to age, gender, education, MMSE, or self-rated health status; those who received booster training showed the most benefit (Ball et al. 2013).

Additional outcomes from the ACTIVE study have demonstrated associations between cognitive training and secondary outcomes. Speed of processing training, in particular, has been associated with reduced declines in health-related quality of life (Wolinsky et al. 2006), likelihood of developing depression (Wolinsky et al. 2009b), and predicted medical expenditures (Wolinsky et al. 2009a). Both reasoning and speed training enhanced internal locus of control beliefs, which is the cognitive sense of personal control over one’s life (Wolinsky et al. 2010). In addition to the training outcomes, examination of how the strategies taught during memory training were implemented after training has demonstrated that implementation of these strategies (e.g., method of loci, clustering) is related to improved memory and everyday functioning (Gross and Rebok 2011; Gross et al. 2014).

Examination of Race in ACTIVE

Given that 26% of the participants in ACTIVE are Black/African American, ACTIVE has provided an opportunity to investigate whether the interventions and cognitive measures performed similarly in Black/African American and non-Hispanic White participants. Zahodne et al. (2015) found that Black/African American participants reported greater external locus of control (i.e., perception of environmental constraints), and this was associated with smaller training gains. When only the no-contact control group was examined, after adjustment for age, education, health, and sex, there were essentially no significant differences in the rate of cognitive change by race (Marsiske et al. 2013).

Previous research has demonstrated cognitive test biases in Black/African Americans, so Multiple Indicators Multiple Causes (MIMIC) models were used to investigate evidence for race-related bias in the cognitive tests used in ACTIVE (Aiken Morgan et al. 2010). Results showed that after accounting for important factors such as age, sex, education, and physical function, there was little evidence that specific measures put either Black/African American or White participants at particular advantage and little evidence of cognitive test bias within ACTIVE.

Examination of Cognitive Impairment in ACTIVE

The exclusion criteria for ACTIVE were developed so that those with likely dementia would not be included at the baseline visit. However, participants with mild cognitive decline (MCI), thought to be the prodromal phase of dementia, were not excluded in ACTIVE. Additionally, over the course of the study, participants with incident dementia were not excluded. While clinical evaluations were not completed as part of the ACTIVE visits, the large amount of information gathered from the objective cognitive measures as well as ADL/IADL measures allow for algorithmic criteria to be used to determine MCI and dementia status in ACTIVE. Regarding incident dementia, there was no evidence that training reduced the risk for dementia over 5 years (Unverzagt et al. 2012); however, more recent work has shown that speed training reduced the risk for dementia in the sample remaining at 10 years follow-up (Edwards et al. 2017). These speed training effects showed a dose-dependent relationship such that each additional training session completed was associated with a 10% reduced risk of progression to dementia.

At baseline, participants were not excluded if they had only mild cognitive impairments and had intact ADL function. Approximately 30% of the baseline sample was classified as MCI based on one algorithmic approach that used the available cognitive tests in ACTIVE (Cook et al. 2013). MCI participants had faster self-reported functional decline than participants without MCI (Wadley et al. 2007) and had lower performance on a performance-based measure of everyday functioning. However, participants with MCI also demonstrated notable benefits from very simple verbal prompts that helped to generally maintain their functional performance over 10 years (Thomas and Marsiske 2014). When focusing on those participants with memory impairment at baseline, they significantly benefitted from the reasoning and speed training, but not from memory training (Unverzagt et al. 2007).

Future Plans

In 2017, the next phase of ACTIVE was funded to conduct a 20-year follow-up and determine whether observed improved cognition and daily function during the 10-year study resulted in long-term reduction in dementia incidence and duration, years of disability, healthcare utilization and costs, and increased active years of life in advanced old age. This is particularly critical, as ACTIVE will be one of the first intervention studies to follow participants into advanced old age, which is the fastest growing age group in the population. For this next phase of ACTIVE, participants will not be recontacted, as the majority of the participants are expected to be deceased. Instead, data will primarily be obtained from Medicare/Medicaid records (e.g., ICD codes for dementia and depression, utilization of skilled nursing facilities), driving records, financial credit reports, and cause of death records from the National Death Index.

A unique addition to this next phase of ACTIVE is the opportunity not only to examine if the interventions were equally effective in Black/African American and non-Hispanic White participants but also to examine social forces that may be related to race disparities in intervention effects as well as prevalence of cognitive impairment. Specifically, social determinants of health in the domains of economic stability (i.e., occupational prestige, credit score ratings), education (i.e., reported education and cognitive stimulation, education quality), social and community context (i.e., marital status, number and relation of co-residents, mobility, census tract measures, population density), health and healthcare (i.e., number of healthcare providers in zip code, number of healthcare visits, health insurance), and neighborhood and environment (i.e., neighborhood socioeconomic position) will be examined as factors that may account for any race disparities that are observed.


The ACTIVE study is the largest and longest-running cognitive intervention study to date involving cognitively unimpaired older adults at baseline, and it continues to provide seminal information on cognitive training interventions in older adults without dementia. This is the first large-scale randomized controlled trial to show that cognitive training interventions can improve the domain of cognition that was trained and that these benefits can last up to 10 years after training. In general, the cognitive interventions provided an initial cognitive boost for individuals in that they had a higher “new baseline” level after training, though did not generally modify the trajectory of cognitive decline. There was little evidence of transfer of the training effects to everyday functioning initially; however, there is evidence that training may have demonstrated some transfer to better self-reported ADL/IADL functioning (for reasoning and speed groups) and lower incident dementia (for the speed group) by the 5- and 10-year follow-up visits, respectively.

In addition to primary training effects, the ACTIVE study’s community-based recruitment efforts resulted in inclusion of a diverse sample, which has allowed the ACTIVE study to address questions related to sociodemographic group and race differences in cognitive performance. Notably, within the ACTIVE sample, findings to date indicate race alone has not been found to be particularly predictive of rates of longitudinal cognitive change or individual differences in training benefit; social determinants of health associated with race must be considered. Taken together, the ACTIVE study has already significantly improved the field’s knowledge and understanding of the effects of cognitive training for older adults. ACTIVE has always included innovative methods for measuring daily functioning (e.g., performance-based measures, driving records) and will continue to expand the scope of the real-world outcomes during the next phase of ACTIVE with the implementation of Medicare/Medicaid, credit report, and death record information.



  1. Aiken Morgan AT, Marsiske M, Dzierzewski JM, Jones RN, Whitfield KE, Johnson KE, Cresci MK (2010) Race-related cognitive test bias in the active study: a mimic model approach. Exp Aging Res 36:426–452.  https://doi.org/10.1080/0361073X.2010.507427CrossRefGoogle Scholar
  2. Ball KK, Beard BL, Roenker DL, Miller RL, Griggs DS (1988) Age and visual search: expanding the useful field of view. J Opt Soc Am A 5:2210.  https://doi.org/10.1364/JOSAA.5.002210CrossRefGoogle Scholar
  3. Ball K, Berch DB, Helmers KF, Jobe JB, Leveck MD, Marsiske M, Morris JN, Rebok GW, Smith DM, Tennstedt SL, Unverzagt FW, Willis SL, for the ACTIVE Study Group for the AS (2002) Effects of cognitive training interventions with older adults. JAMA 288:2271.  https://doi.org/10.1001/jama.288.18.2271CrossRefGoogle Scholar
  4. Ball K, Edwards JD, Ross LA, McGwin G Jr (2010) Cognitive training decreases motor vehicle collision involvement of older drivers. J Am Geriatr Soc 58:2107–2113.  https://doi.org/10.1111/j.1532-5415.2010.03138.xCrossRefGoogle Scholar
  5. Ball KK, Ross LA, Roth DL, Edwards JD (2013) Speed of processing training in the ACTIVE study: how much is needed and who benefits? J Aging Health 25:65S–84S.  https://doi.org/10.1177/0898264312470167CrossRefGoogle Scholar
  6. Cook SE, Marsiske M, Thomas KR, Unverzagt FW, Wadley VG, Langbaum JBS, Crowe M (2013) Identification of mild cognitive impairment in ACTIVE: algorithmic classification and stability. J Int Neuropsychol Soc 19:73–87.  https://doi.org/10.1017/S1355617712000938CrossRefGoogle Scholar
  7. Edwards JD, Xu H, Clark DO, Guey LT, Ross LA, Unverzagt FW (2017) Speed of processing training results in lower risk of dementia. Alzheimer’s Dement 3:603–611.  https://doi.org/10.1016/j.trci.2017.09.002CrossRefGoogle Scholar
  8. Gross AL, Rebok GW (2011) Memory training and strategy use in older adults: results from the ACTIVE study. Psychol Aging 26:503–317.  https://doi.org/10.1037/a0022687CrossRefGoogle Scholar
  9. Gross AL, Brandt J, Bandeen-Roche K, Carlson MC, Stuart EA, Marsiske M, Rebok GW (2014) Do older adults use the method of loci? Results from the ACTIVE study. Exp Aging Res 40:140–163.  https://doi.org/10.1080/0361073X.2014.882204CrossRefGoogle Scholar
  10. Gross AL, Payne BR, Casanova R, Davoudzadeh P, Dzierzewski JM, Farias S, Giovannetti T, Ip EH, Marsiske M, Rebok GW, Schaie KW, Thomas K, Willis S, Jones RN (2018) The ACTIVE conceptual framework as a structural equation model. Exp Aging Res 44:1–17.  https://doi.org/10.1080/0361073X.2017.1398802CrossRefGoogle Scholar
  11. 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. Control Clin Trials 22:453–479.  https://doi.org/10.1016/S0197-2456(01)00139-8CrossRefGoogle Scholar
  12. Marsiske M, Dzierzewski JM, Thomas KR, Kasten L, Jones RN, Johnson KE, Willis SL, Whitfield KE, Ball KK, Rebok GW (2013) Race-related disparities in 5-year cognitive level and change in untrained active participants. J Aging Health 25:103S–127S.  https://doi.org/10.1177/0898264313497794CrossRefGoogle Scholar
  13. Rebok GW, Balcerak LJ (1989) Memory self-efficacy and performance differences in young and old adults: the effect of mnemonic training. Dev Psychol 25:714–721.  https://doi.org/10.1037/0012-1649.25.5.714CrossRefGoogle Scholar
  14. Rebok GW, Langbaum JBS, Jones RN, Gross AL, Parisi JM, Spira AP, Kueider AM, Petras H, Brandt J (2013) Memory training in the ACTIVE study. J Aging Health 25:21S–42S.  https://doi.org/10.1177/0898264312461937CrossRefGoogle Scholar
  15. Rebok GW, Ball K, Guey LT, Jones RN, Kim H-Y, King JW, Marsiske M, Morris JN, Tennstedt SL, Unverzagt FW, Willis SL (2014) Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc 62:16–24.  https://doi.org/10.1111/jgs.12607CrossRefGoogle Scholar
  16. Thomas KR, Marsiske M (2014) Verbal prompting to improve everyday cognition in MCI and unimpaired older adults. Neuropsychology 28:123–134.  https://doi.org/10.1037/neu0000039CrossRefGoogle Scholar
  17. Unverzagt FW, Kasten L, Johnson KE, Rebok GW, Marsiske M, Koepke KM, Elias JW, Morris JN, Willis SL, Ball K, Rexroth DF, Smith DM, Wolinsky FD, Tennstedt SL (2007) Effect of memory impairment on training outcomes in ACTIVE. J Int Neuropsychol Soc 13:953–960.  https://doi.org/10.1017/S1355617707071512CrossRefGoogle Scholar
  18. Unverzagt FW, Guey LT, Jones RN, Marsiske M, King JW, Wadley VG, Crowe M, Rebok GW, Tennstedt SL (2012) ACTIVE cognitive training and rates of incident dementia. J Int Neuropsychol Soc 18:669–677.  https://doi.org/10.1017/S1355617711001470CrossRefGoogle Scholar
  19. Wadley VG, Crowe M, Marsiske M, Cook SE, Unverzagt FW, Rosenberg AL, Rexroth D (2007) Changes in everyday function in individuals with psychometrically defined mild cognitive impairment in the advanced cognitive training for independent and vital elderly study. J Am Geriatr Soc 55:1192–1198.  https://doi.org/10.1111/j.1532-5415.2007.01245.xCrossRefGoogle Scholar
  20. Willis SL (1987) Cognitive training and everyday competence. Annu Rev Gerontol Geriatr 7:159–188.  https://doi.org/10.1891/0198-8794.7.1.159CrossRefGoogle Scholar
  21. Willis SL, Caskie GIL (2013) Reasoning training in the ACTIVE study. J Aging Health 25:43S–64S.  https://doi.org/10.1177/0898264313503987CrossRefGoogle Scholar
  22. Willis SL, Tennstedt SL, Marsiske M, Ball K, Elias J, Koepke KM, Morris JN, Rebok GW, Unverzagt FW, Stoddard AM, Wright E, ACTIVE Study Group for the AS (2006) Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA 296:2805.  https://doi.org/10.1001/jama.296.23.2805CrossRefGoogle Scholar
  23. 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. J Gerontol Ser B Psychol Sci Soc Sci 61:S281–S287.  https://doi.org/10.1093/geronb/61.5.S281CrossRefGoogle Scholar
  24. Wolinsky FD, Mahncke HW, Kosinski M, Unverzagt FW, Smith DM, Jones RN, Stoddard A, Tennstedt SL (2009a) The ACTIVE cognitive training trial and predicted medical expenditures. BMC Health Serv Res 9:109.  https://doi.org/10.1186/1472-6963-9-109CrossRefGoogle Scholar
  25. Wolinsky FD, Mahncke HW, Weg MWV, Martin R, Unverzagt FW, Ball KK, Jones RN, Tennstedt SL (2009b) The ACTIVE cognitive training interventions and the onset of and recovery from suspected clinical depression. J Gerontol Ser B Psychol Sci Soc Sci 64B:577–585.  https://doi.org/10.1093/geronb/gbp061CrossRefGoogle Scholar
  26. 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? J Gerontol Ser B Psychol Sci Soc Sci 65B:591–598.  https://doi.org/10.1093/geronb/gbp117CrossRefGoogle Scholar
  27. 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. Psychol Aging 30:561–572.  https://doi.org/10.1037/pag0000042CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kelsey R. Thomas
    • 1
    • 2
    Email author
  • George W. Rebok
    • 3
  • Sherry L. Willis
    • 4
  1. 1.Department of PsychiatryUniversity of California, San Diego School of Medicine La JollaUSA
  2. 2.VA San Diego Healthcare System San DiegoUSA
  3. 3.Department of Mental HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  4. 4.Department of Psychiatry and Behavioral Sciences; Department of PsychologyUniversity of WashingtonSeattleUSA

Section editors and affiliations

  • Matthew E. Dupre
    • 1
    • 2
  1. 1.Department of Population Health Sciences, School of MedicineDuke UniversityDurhamUSA
  2. 2.Duke Clinical Research Institute, School of MedicineDuke UniversityDurhamUSA