Context Cognitive function in older adults is related to independent living
and need for care. However, few studies have addressed whether improving cognitive
functions might have short- or long-term effects on activities related to
Objective To evaluate whether 3 cognitive training interventions improve mental
abilities and daily functioning in older, independent-living adults.
Design Randomized, controlled, single-blind trial with recruitment conducted
from March 1998 to October 1999 and 2-year follow-up through December 2001.
Setting and Participants Volunteer sample of 2832 persons aged 65 to 94 years recruited from
senior housing, community centers, and hospital/clinics in 6 metropolitan
areas in the United States.
Interventions Participants were randomly assigned to 1 of 4 groups: 10-session group
training for memory (verbal episodic memory; n = 711), or reasoning (ability
to solve problems that follow a serial pattern; n = 705), or speed of processing
(visual search and identification; n = 712); or a no-contact control group
(n = 704). For the 3 treatment groups, 4-session booster training was offered
to a 60% random sample 11 months later.
Main Outcome Measures Cognitive function and cognitively demanding everyday functioning.
Results Thirty participants were incorrectly randomized and were excluded from
the analysis. Each intervention improved the targeted cognitive ability compared
with baseline, durable to 2 years (P<.001 for
all). Eighty-seven percent of speed-, 74% of reasoning-, and 26% of memory-trained
participants demonstrated reliable cognitive improvement immediately after
the intervention period. Booster training enhanced training gains in speed
(P<.001) and reasoning (P<.001)
interventions (speed booster, 92%; no booster, 68%; reasoning booster, 72%;
no booster, 49%), which were maintained at 2-year follow-up (P<.001 for both). No training effects on everyday functioning were
detected at 2 years.
Conclusions Results support the effectiveness and durability of the cognitive training
interventions in improving targeted cognitive abilities. Training effects
were of a magnitude equivalent to the amount of decline expected in elderly
persons without dementia over 7- to 14-year intervals. Because of minimal
functional decline across all groups, longer follow-up is likely required
to observe training effects on everyday function.
Nearly half of community-dwelling persons aged 60 years and older express
concern about declining mental abilities.1 Although
there is substantial evidence that many cognitive abilities and processes
are related to measures of functional status, need for care, and quality of
life, few studies have addressed whether improving cognitive functions might
have short- or long-term effects on activities related to living independently.
Interventions designed to delay or prevent the need for nursing homes, home
care, and hospital stays can save health care costs, while also ensuring the
independence and dignity of the aging population.
A growing body of research supports the protective effects of late-life
intellectual stimulation on incident dementia.2,3 Recent
research from both human and animal studies indicates that neural plasticity
endures across the lifespan, and that cognitive stimulation in the environment
is an important predictor of enhancement and maintenance of cognitive functioning,
even in old age. Moreover, sustained engagement in cognitively stimulating
activities has been found to impact neural structure in both older humans
and rodents.4-6 Conversely,
limited education has been found to be a risk factor for dementia.7 There is also a sizeable body of literature documenting
that different types of cognitive training programs have large and durable
effects on the cognitive functioning of older adults, even in advanced old
the same time, several important issues remain understudied. First, prior
cognitive training studies with older adults have often paid relatively little
attention to the use of appropriate control groups, the representativeness
or heterogeneity of participants, the generalizability of training findings
beyond particular laboratories, or adherence of participants to training protocols.
For example, it has not been uncommon for such studies to analyze only compliant
participants. Second, the broader implications of training on daily functioning
in older adults, for the most part, have not been studied.
The primary objective of the ACTIVE (Advanced Cognitive Training for
Independent and Vital Elderly) trial was to test the effectiveness and durability
of 3 distinct cognitive interventions in improving the performance of elderly
persons on basic measures of cognition and on measures of cognitively demanding
daily activities (eg, food preparation, driving, medication use, financial
management). These interventions previously had been found successful in improving
cognitive abilities under laboratory or small-scale field conditions.8-16 We
hypothesized that the effects of cognitive training on primary outcomes will
be largely mediated through the basic cognitive abilities being trained. The
detailed hypotheses may be summarized by 2 points: each training group will
perform better than the other training and control groups on their respective
primary and proximal outcomes, and those groups that received booster training
will perform better than those that did not receive booster training on their
primary and proximal outcomes.
The recruitment goal for the ACTIVE trial was to enroll a diverse sample
of older adults who, at enrollment, were living independently in good functional
and cognitive status. Recruitment was conducted from March 1998 to October
1999; 2-year follow-up data were collected through December 2001. Details
of the recruitment procedures have been published elsewhere.17 Participants
aged 65 to 94 years were enrolled across 6 field sites using a variety of
sampling frames and recruitment strategies (state driver's license and identification
card registries, medical clinic rosters, senior center and community organization
rosters, senior housing sites, local churches, and rosters of assistance and
service programs for low-income elderly persons). Oral assent was obtained
for brief telephone screening, and written informed consent was obtained in
person from each potential participant prior to administration of in-person
Persons were excluded from participation if they were younger than 65
years at screening; if they had already experienced substantial cognitive
decline (score of ≤22 on the Mini-Mental State Examination [MMSE]18); had a self-reported diagnosis of Alzheimer disease;
had already experienced substantial functional decline (self-reported need
for weight-bearing support or full caregiver performance of dressing, personal
hygiene, or bathing 3 or more times in the previous 7 days); had medical conditions
that would predispose them to imminent functional decline or death (eg, stroke
within the past 12 months, certain cancers, or current chemotherapy or radiation
treatment for cancer); had recent cognitive training; were unavailable during
the testing and intervention phases of the study; or had severe losses in
vision (self-reported difficulty in reading newsprint, or measured vision
worse than 20/70 with best correction), hearing (interviewer-rated), or communicative
ability (interviewer-rated) that would sufficiently impair performance to
make participation impossible.
The study protocol was approved by the institutional review boards at
the University of Alabama at Birmingham; Wayne State University, Detroit,
Mich; the Hebrew Rehabilitation Center for the Aged, Roslindale, Mass; the
Johns Hopkins University School of Medicine, Baltimore, Md; Indiana University,
Bloomington; Purdue University, Indianapolis, Ind; Pennsylvania State University,
University Park; the University of Florida, Gainesville; and the New England
Research Institutes, Watertown, Mass.
The ACTIVE trial was sponsored by the National Institute on Aging and
the National Institute of Nursing Research, and was randomized, controlled,
and single-blind, using a 4-group design, including a no-contact control group
and 3 intervention groups (memory training, reasoning training, or speed-of-processing
training). These 3 interventions were selected because they showed the most
promise in smaller laboratory studies and had been related to instrumental
activities of daily living (IADL).8,19-26 Each
intervention group received a 10-session intervention, conducted by certified
trainers, for 1 of 3 cognitive abilities—memory, inductive reasoning,
or speed of processing. Assessors were blinded to participant intervention
assignment. Training exposure and social contact were standardized across
interventions so that each intervention served as a contact control for the
other 2 interventions. Booster training was provided to a random subsample
in each intervention group. Measurement points consisted of baseline tests,
an immediate posttest (following the intervention), and 1 and 2 annual posttests.
The interventions were conducted in small group settings in ten 60-
to 75-minute sessions over 5- to 6-week periods. These were behavioral interventions
with no pharmacological component. In all 3 conditions, sessions 1 through
5 focused on strategy instruction and individual and group exercises to practice
the strategy. Sessions 6 through 10 provided additional practice exercises
but introduced no new strategies.
Memory training12,27-29 focused
on verbal episodic memory. Participants were taught mnemonic strategies for
remembering word lists and sequences of items, text material, and main ideas
and details of stories. Participants received instruction in a strategy or
mnemonic rule, exercises, individual and group feedback on performance, and
a practice test. For example, participants were instructed how to organize
word lists into meaningful categories and to form visual images and mental
associations to recall words and texts. The exercises involved laboratory-like
memory tasks (eg, recalling a list of nouns, recalling a paragraph), as well
as memory tasks related to cognitive activities of everyday life (eg, recalling
a shopping list, recalling the details of a prescription label).
Reasoning training10,13 focused
on the ability to solve problems that follow a serial pattern. Such problems
involve identifying the pattern in a letter or number series or understanding
the pattern in an everyday activity such as prescription drug dosing or travel
schedules. Participants were taught strategies to identify a pattern and were
given an opportunity to practice the strategies in both individual and group
exercises. The exercises involved abstract reasoning tasks (eg, letter series)
as well as reasoning problems related to activities of daily living.
Speed-of-processing training8,30 focused
on visual search skills and the ability to identify and locate visual information
quickly in a divided-attention format. Participants practiced increasingly
complex speed tasks on a computer. Task difficulty was manipulated by decreasing
the duration of the stimuli, adding either visual or auditory distraction,
increasing the number of tasks to be performed concurrently, or presenting
targets over a wider spatial expanse. Difficulty was increased each time a
participant achieved criterion performance on a particular task.
Eleven months after the initial training was provided, booster training
was offered to a randomly selected 60% of initially trained subjects in each
of the 3 intervention groups. Booster training was delivered in four 75-minute
sessions over a 2- to 3-week period.
The ACTIVE trial had multiple outcomes, both proximal (cognitive abilities)
and primary (daily function) (Table 1).
Composites were created to represent each domain. Each composite was the average
of 2 or 3 test scores, equally weighted, and was designed as a measure of
ability rather than performance on a specific test.
Proximal outcomes permitted a test of the impact of the 3 interventions
on the appropriate cognitive abilities. Memory assessment
focused on episodic verbal memory tasks. Reasoning assessment
focused on tasks requiring identification of patterns in letter or word series
problems. Speed-of-processing assessment focused
on identifying the minimum stimulus duration at which participants could identify
and localize information, with 75% accuracy, under varying levels of cognitive
Primary outcomes were aspects of functional activities, both performance-based
and self-reported. Everyday problem solving represented
the ability to reason and correctly identify information in common everyday
stimuli (eg, medication labels, charts, forms). This was measured via paper-and-pencil
testing and behavioral simulations of everyday tasks. Everyday
speed emphasized the speed with which participants interacted with
real-world stimuli. Participants were asked to look up a specific telephone
number, find food items on a crowded shelf of groceries, find ingredients
on food labels, count out specified amounts of change, find specified information
on medicine bottles, and respond appropriately to different traffic signs. Activities of daily living (ADL) and instrumental activities of daily living included self-ratings drawn
from the Minimum Data Set—Home Care (MDS-HC).43Driving habits included self-ratings of driving difficulty
and avoidance of specific driving situations.
Tests were standardized by pooling scores at all time points and applying
a Blom transformation,44 producing more normally
distributed scores. Scores for tests at each time point were standardized
to the baseline mean and SD. If 1 or more tests of a composite were missing,
the composite score was calculated as the average of the nonmissing tests.
To evaluate the effects of ACTIVE training over 2 years, a repeated-measures,
mixed-effects model was used.45 The dependent
variables were the proximal and primary composites measured at 4 time points:
baseline, immediate posttest, first annual evaluation (A1), and second annual
evaluation (A2). At posttest only the cognitive variables, the Everyday Problems
Test, and the primary speed composite were measured. The independent variables
were restricted to the basic design features: fixed effects for training group
(memory, reasoning, speed, control); time (3 or 4 points); booster training;
field site; and replicate within site. Three interaction terms were chosen
for their importance and interpretability: time × training, representing
the net effect of the trial; time × booster, representing nonspecific
effects of the additional social contact of attending booster training, regardless
of content; and time × booster × training, representing the training-specific
effects of each booster intervention. For this analysis, the repeated-measures
model was fitted to the available data, ignoring missing data. Then, to determine
if selective attrition influenced the trial results, missing data were imputed
using multiple imputation procedures,46 and
the analysis was repeated.
Hypotheses were tested by comparing outcome composite scores at later
times (posttest, A1, and A2) to baseline scores and to control group scores,
yielding net differences. The net effect of training at any time was defined
as: (trained mean − control mean at later time) − (trained mean
− control mean at baseline). Similarly, the net effect of each booster
training was defined as: (booster mean − unboosted mean at later time)
− (booster mean − unboosted mean at baseline). Results are expressed
as effect sizes (ie, difference in means divided by intrasubject SD) to allow
direct comparison of different outcomes. In addition, covariate-adjusted training
effects were examined, with covariates of age, sex, cognitive status (MMSE
score), years of education, and visual acuity. Given the substantial variation
associated with field site and replicate, these 2 factors were also included
as covariates in all analyses.
Secondary analyses investigated the percentage of participants who showed
reliable improvement in each training group. A participant was classified
as having improved reliably on a particular measure if his or her performance
at a follow-up occasion exceeded baseline performance on that measure by 1
SEM.47 The formula for reliable change was
computed as outlined by Dudek,47 and analyses
were conducted using SAS v8.2 (SAS Institute Inc, Cary, NC). P<.05 was considered significant.
Five thousand individuals were contacted for participation (Figure 1). A total of 2832 persons were eligible,
905 (18.1%) were ineligible, and 1263 (25.3%) refused (either directly or
passively by not coming to any appointments) prior to randomization. Reasons
for ineligibility were: cognitive impairment on the MMSE (270 [29.8%]), vision
impairment (192 [21.2%]), unavailability due to schedule (202 [22.3%]), too
young (85 [9.4%]), medical conditions predisposing to imminent decline or
short life expectancy (79 [8.7%]), significant ADL disability (48 [5.3%]),
impaired communication (15 [1.7%]), diagnosed Alzheimer disease (7 [0.8%]),
and prior participation in cognitive training trials (7 [0.8%]). Enrollment
at the field sites ranged from 405 to 498 participants. Thirty eligible persons
were randomized inappropriately, thus violating protocol, and were excluded
from analyses. The analytic sample consists of 2802 participants randomized
by the New England Research Institutes with a concealed system. Intention-to-treat
analyses were used.
Ineligible participants were comparable with eligible participants in
age (mean, 77 years) and proportion of women (77%). Ineligible participants
tended to have a higher percentage of nonwhite persons (48%) and lower cognitive
function (mean MMSE score, 20.9). Potential participants who were eligible
and randomized (n = 2802) were comparable with the group that was eligible
and not randomized (n = 1263). Compared with the nonrandomized group, the
randomized group was slightly younger (mean, 74 vs 75 years), more educated
(13.5 vs 12.3 years), scored higher in cognitive function (MMSE score, 27.3
vs 26.8), and had fewer nonwhite participants (27% vs 40%). The baseline characteristics
of the ACTIVE cohort and its comparability with the general population are
provided in Table 2.
Results of the analyses are summarized in Table 3 and Table 4.
Eighty-nine percent of participants completed treatment (≥8 training sessions),
and 80% of the sample was retained at the 2-year follow-up, despite the advanced
age of the cohort. The net effect of ACTIVE training on the proximal (cognitive)
composites is displayed in the top portion of Table 3. Each training program produced an immediate effect on its
corresponding cognitive ability. It is important to note that while these
analyses were conducted on Blom-transformed variables, a near-identical pattern
of findings was obtained with the untransformed variables. Temporal trends
in the mean cognitive composite scores by intervention group are shown in Figure 2.
The net effect of ACTIVE training on functional outcome composites is
detailed in the lower section of Table 3. These effects were generally small on the effect-size scale—most
were below 0.10—and did not differ significantly from zero at A1 or
A2. It is important to note, however, that the vast majority of this sample
remained functionally independent over the course of the 24-month observation
period. For the crucial measures of ADL performance—measures that have
been shown to predict movement into home care and institutional programs—a
relatively low ADL decline rate (defined as ≥2 points on the summary measure)
of 6% was observed at 12 months, with a modest increase to 8% at 24 months.
The impact of booster training at A1 and A2 is detailed in Table 4. Again, the strongest effects were
seen in cognitive outcomes, where boosters for reasoning and speed training
administered shortly before A1 produced significantly better performance.
The impact of reasoning and speed booster training was greater at A1 than
at A2. No effect was detected for memory booster on the memory proximal composite.
Compared with those who did not receive booster training, participants randomized
to speed booster performed significantly better at A1 on the functioning and
everyday speed composites (P<.05), and marginally
better at A2 (P<.10). Similarly, compared with
those who did not receive booster training, participants randomized to reasoning
booster performed marginally better on the functioning composite at A1 (P<.10).
The results of covariate-adjusted analyses were generally similar. While
effect sizes were universally higher after adjusting for age, sex, education,
visual function, and mental status, the overall pattern of results was the
same and is not presented here. Similarly, analyses of imputed data sets did
not differ in outcomes, suggesting that the trial results were not influenced
by selective attrition.
Consistent with results of the primary analyses, secondary analyses
indicated large immediate intervention gains on the cognitive outcomes. Eighty-seven
percent of speed-trained, 74% of reasoning-trained, and 26% of memory-trained
participants demonstrated reliable improvement on the pertinent cognitive
composite immediately following intervention. While intervention participants
showed reliable posttest gains, a comparable proportion of control participants
also improved, and the proportion of control participants exhibiting reliable
retest gain remained fairly constant across study intervals.
In terms of the proportion of the intervention group showing reliable
gain in the trained domain, booster effects occurred for the speed conditions
(boost, 92%; no boost, 68%; control, 32%) and the reasoning conditions (boost,
72%; no boost, 49%; control, 31%). While some dissipation of intervention
effects occurred across time, cognitive effects were maintained from baseline
to A2, particularly for boosted participants (79% [speed boost] vs 37% [controls];
57% [reasoning boost] vs 35% [controls]).
To date, ACTIVE is the largest trial (N = 2802) of cognitive interventions
for the improvement of older adults' performance on specific cognitive and
perceptual abilities. Although studies have successfully used laboratory-based
interventions to improve cognitive performance in older adults,8,10,12,14,27,29,52 the
ACTIVE trial improved on previous studies in that it used a multisite, randomized,
controlled design; included a large, diverse sample; used common multisite
intervention protocols; and examined primary outcomes as well as long-term
transfer effects to everyday activities.
Overall, this large-scale study demonstrated that cognitive interventions
helped normal elderly individuals to perform better on multiple measures of
the specific cognitive ability for which they were trained. It did not, however,
demonstrate the generalization of such interventions to everyday performance,
at least in the initial 2 years. The effect sizes for the cognitive abilities
at immediate posttest are for the most part consistent with previous research.
Moreover, these effect sizes are comparable with or greater than the amount
of longitudinal decline that has been reported in previous studies (Table 5), suggesting that these interventions
have the potential to reverse age-related decline. Specifically, age-related
decline for reasoning ability in samples of elderly persons without dementia
has been found to be on the order of 0.22 SD over a 7-year interval (ages
67-74 years) and to increase to 0.42 SD over a 14-year interval (ages 67-81
years).53 Thus, immediate reasoning training
effects (0.48 SD) were comparable with the amount of decline reported to occur
in elderly persons without dementia over a 14-year interval. Likewise, decline
in memory ability has been reported to be approximately 0.25 SD over a 6-
or 7-year interval. Thus, memory training effects (0.25 SD) were comparable
with the expected decline over a 7-year interval in elderly persons without
decline for speed has been reported to be approximately 0.16 SD over 2 years.8 Immediate speed training effects (1.46 SD) were therefore
9 times greater than the expected decline over a 2-year interval in elderly
persons without dementia.
Although training impact on the proximal composites decreased over time,
it remained statistically significant, attesting to the durability of the
intervention training effects. This is an important finding, since prior interventions
(especially memory) have not shown 2-year durability. Furthermore, a very
high percentage of trained participants achieved reliable improvement on the
cognitive abilities, and ceiling effects at baseline on the cognitive measures
explain lack of reliable improvement for most others. Of further note, the
tests of training effects were conservative compared with those used in prior
cognitive aging research. That is, prior cognitive training research has not
used intention-to-treat analyses, instead excluding participants who dropped
out or were noncompliant. In addition, prior research has not used diverse
samples in terms of education and ethnicity. Thus, relative to prior work,
training effects on cognitive abilities in this study are strong.
Insufficient sample size was ruled out as an explanation for the small
effect sizes to date on the functional outcomes. The study was sufficiently
powered to detect an effect size of 0.20 at 95% power with a sample of 2832.56 Power calculations were based on 6 Bonferroni-corrected,
2-sided comparisons with an overall α level of .05, a correlation of
0.7 between baseline and follow-up (based on pilot data), and an 80% completion
rate.17 Based on these same assumptions, there
was 90% power to detect booster training effects. Given that we retained more
than 80% of the initial sample over the 2-year follow-up period and found
no differential loss across treatment and control conditions, there should
have been sufficient power to detect a significant effect of the cognitive
training on the functional outcomes.
The absence of transfer to real-world outcomes is not particularly surprising.
In addition to several decades of cognitive science research demonstrating
the difficulty of obtaining such transfer, most of our subjects were not yet
impaired in the domains of training. Indeed, there are several other potential
explanations for the observed lack of transfer to daily function: the proportion
of participants functioning at ceiling levels (ie, 43% had no room for improvement,
as indicated by baseline performance within 1 SEM of the "best" value) on
the daily functional composite, the evidence of strong practice or retest
effects in the control group, and the control group's lack of functional decline
over the 2-year follow-up period.
With respect to ceiling effects in everyday functional abilities, this
finding does not reflect poor measurement choices; rather, one would expect
that most participants would show high levels of competence on these self-
and household-care tasks if they continue to reside independently in the community,
as was true at enrollment. Thus, improved cognitive function could not be
expected to improve intact everyday abilities over a 2-year period.
Consistent with prior cognitive intervention research showing large
retest or practice effects,57 the approximately
5 hours of practice on cognitive tests at each assessment occasion resulted
in retest effects for the control group. Approximately 25% of control participants
showed reliable gain on cognitive and functional composites as a result of
practice effects, and these retest effects were evident across study intervals.
Particularly notable were practice effects on the daily function composites.
These large retest effects contributed to ceiling-level performance across
groups that precluded demonstration of additional gain as a result of training.
In terms of the observed lack of functional decline in the control group,
it is important to note that individuals with extant functional or cognitive
decline were carefully screened out, and the study focused instead on intact
individuals whose future decline rates were likely to mimic or be less than
rates for the general elderly population. It was therefore unclear whether
participants would show evidence of decline similar to established population
parameters over the 12- and 24-month observation periods, or whether individuals
in the sample would be more resilient and less subject to decline over such
a short period of time. In specifying expected effect sizes for the functional
outcome measures, the former position was adopted (ie, decline rates would
follow established patterns). However, for the crucial everyday measures of
IADL and ADL performance, the observed decline rates were significantly below
established population norms. At 12 months, only 25% of participants experienced
a 2-point or greater drop in the 36-point IADL scale, while by 24 months 28%
had experienced this small increase in dependency. For the 30-point ADL performance
scale, 6% were more dependent at 12 months and 8% at 24 months. Prior longitudinal
research on cognitively demanding measures of everyday functioning indicates
that age-related decline occurs later for these tasks than for the more basic
abilities that were the focus of training. Reliable age-related decline on
everyday problem-solving tasks has been shown not to occur until individuals
are in their mid seventies, whereas declines on basic abilities such as reasoning
and memory typically occur in their mid sixties.58
In summary, it is clear that proximal training effects occurred, that
they continued (albeit at lower levels) through 24 months, and that a significant
segment of trained individuals went forward through 2 years of life with better
cognitive skills than did the controls. Due to lack of functional declines
thus far, it is not yet clear whether differential functional decline across
treatment groups will be observed in the future as this select cohort enters
more fully into an age of functional loss.
Lowenthal MF, Berkman PC, Buehler JA, Pierce RC, Robinson BC, Trier ML. Aging and Mental Disorder in San Francisco. San Francisco, Calif: Jossey-Bass; 1967.
Wilson RS, Mendes de Leon CF, Barnes LL.
et al. Participation in cognitively stimulating activities and risk of incident
Alzheimer disease. JAMA.2002;287:742-748.Google Scholar
Scarmeas N, Levy G, Tang MX, Manly J, Stern Y. Influence of leisure activity on the incidence of Alzheimer's disease. Neurology.2001;57:2236-2242.Google Scholar
Churchill JD, Galvez R, Colcombe S, Swain RA, Greenough WT. Exercise, experience, and the aging brain. Neurobiol Aging.2002;23:941.Google Scholar
Krampe RT, Ericcson KA. Maintaining excellence: deliberate practice and elite performance in
young and older pianists. J Exp Psychol Gen.1996;125:331-359.Google Scholar
Recanzone GH. Cerebral cortical plasticity: perception and skill acquisition. In: Gazzaniga S, ed. The New Cognitive Neurosciences. Cambridge, Mass: The MIT Press; 2000:237-247.
Callahan CM, Hall KS, Hui SL, Musick BS, Unversagt FW, Hendrie HC. Relationship of age, education, and occupation with dementia among
a community-based sample of African Americans. Arch Neurol.1996;53:134-140.Google Scholar
Ball K, Owsley C. Increasing mobility and reducing accidents of older drivers. In: Schaie KW, Pietrucha M, eds. Mobility and Transportation
in the Elderly. New York, NY: Springer Publishing Co Inc; 2000:213-251.
Ball KK, Beard BL, Roenker DL, Miller RL, Griggs DS. Age and visual search: expanding the useful field of view. J Opt Soc Am A.1988;5:2210-2219.Google Scholar
Baltes PB, Willis SL. Plasticity and enhancement of intellectual functioning in old age:
Penn State's Adult Development and Enrichment Project (ADEPT). In: Craik FIM, Trehub S, eds. Aging and Cognitive
Processes. New York, NY: Plenum Press; 1982:353-390.
Greenberg C, Powers SM. Memory improvement among adult learners. Educ Gerontol.1987;12:385-394.Google Scholar
Rebok G, Balcerak LJ. Memory self-efficacy and performance differences in young and old adults:
effects of mnemonic training. Dev Psychol.1989;25:714-721.Google Scholar
Willis SL. Cognitive training and everyday competence. In: Schaie KW, ed. Annual Review of Gerontology
and Geriatrics. Vol 7. New York, NY: Springer; 1987.
Willis SL, Nesselroade CS. Long-term effects of fluid ability training in old-old age. Dev Psychol.1990;26:905-910.Google Scholar
Yesavage JA. Nonpharmacologic treatments for memory losses with normal aging. Am J Psychiatry.1985;142:600-605.Google Scholar
Willis SL, Schaie KW. Training the elderly on the ability factors of spatial orientation
and inductive reasoning. Psychol Aging.1986;1:239-247.Google Scholar
Jobe JB, Smith DM, Ball K.
et al. ACTIVE: a cognitive intervention trial to promote independence in older adults. Control Clin Trials.2001;22:453-479.Google Scholar
Folstein MF, Flolstein SE, McHugh PR. "Mini-mental state": a practical method for grading the cognitive state
of patients for the clinician. J Psychiatr Res.1975;12:189-198.Google Scholar
Ball K, Owsley C, Stalvey B, Roenker D, Sloane M, Graves M. Driving avoidance and functional impairment in older drivers. Accid Anal Prev.1998;30:313-322.Google Scholar
Ball K, Owsley C, Sloane M, Roenker D, Bruni J. Visual attention problems as a predictor of vehicle crashes in older
drivers. Invest Ophthalmol Vis Sci.1993;34:3110-3123.Google Scholar
Diehl M, Willis SL, Schaie KW. Everyday problem solving in older adults: observational assessment
and cognitive correlates. Psychol Aging.1995;10:478-490.Google Scholar
Leirer VO, Morrow DG, Pariante GM, Sheikh JI. Elders' nonadherence, its assessment, and computer assisted instruction
for medication recall training. J Am Geriatr Soc.1988;36:877-884.Google Scholar
Owsley C, Ball K, Sloane M, Roenker D, Bruni J. Visual/cognitive correlates of vehicle accidents in older drivers. Psychol Aging.1991;6:403-415.Google Scholar
Owsley C, McGwin G, Ball K. Vision impairment, eye disease, and injurious motor vehicle crashes
in the elderly. Ophthalmic Epidemiol.1998;5:101-113.Google Scholar
Owsley C, Ball K, McGwin G.
et al. Visual processing impairment and risk of motor vehicle crash among
older adults. JAMA.1998;279:1083-1088.Google Scholar
Willis SL, Jay GM, Diehl M, Marsiske M. Longitudinal change and prediction of everyday task competence in the
elderly. Res Aging.1992;14:68-91.Google Scholar
Rasmusson DX, Rebok GW, Bylsma FW, Brandt J. Effects of three types of memory training in normal elderly. Aging Neuropsychol Cogn.1999;6:56-66.Google Scholar
Lachman ME, Weaver SL, Bandura M, Elliot E, Lewkowicz C. Improving memory and control beliefs through cognitive restructuring
and self generated strategies. J Gerontol.1992;47:293-299.Google Scholar
Kliegl R, Smith J, Baltes PB. On the locus and process of magnification of age differences during
mnemonic training. Dev Psychol.1990;26:894-904.Google Scholar
Ball K, Beard B, Roenker D, Miller R, Ball D. Visual search: age and practice. Invest Ophthalmol Vis Sci.1988;29:448.Google Scholar
Brandt J. The Hopkins Verbal Learning Test: development of a new memory test
with six equivalent forms. Clin Neuropsychol.1991;5:125-142.Google Scholar
Rey A. L'examen psychologique dans les cas d'encephalopathie tramatique. Archives de Psychologie.1941;28:21.Google Scholar
Wilson B, Cockburn J, Baddeley A. The Rivermead Behavioral Memory Test. Reading, England: Thames Valley Test Co; 1985.
Gonda J, Schaie K. Schaie-Thurstone Mental Abilities Test: Word Series
Test. Palo Alto, Calif: Consulting Psychologists Press; 1985.
Thurstone L, Thurstone T. Examiner Manual for the SRA Primary Mental Abilities
Test (Form 10-14). Chicago, Ill: Science Research Associates; 1949.
Ekstrom RB, French JW, Harman H, Derman D. Kit of Factor Referenced Cognitive Tests. Revised ed. Princeton, NJ: Educational Testing Service; 1976.
Ball K, Owsley C. The useful field of view test: a new technique for evaluating age-related
declines in visual function. J Am Optom Assoc.1993;64:71-79.Google Scholar
Willis S, Marsiske M. Manual for the Everyday Problems Test. University Park: Pennsylvania State University; 1993.
Diehl M, Marsiske M, Horgas A, Saczynski J. Psychometric properties of the Revised Observed Tasks of Daily Living
(OTDL-R). Paper presented at: Annual Meeting of the Gerontological Society
of America; November 20-23, 1998; Philadelphia, Pa.
Owsley C, Sloane M, McGwin G, Ball K. Timed Instrumental Activites of Daily Living (TIADL) tasks: relationship
to cognitive function and everyday performance assessments in older adults. Gerontology.2002;48:254-265.Google Scholar
Morris J, Morris S. ADL assessment measures for use with frail elders. In: Teresi J, Lawton M, Holmes D, Ory M, eds. Measurement in Elderly Chronic Care Populations. New York, NY: Springer;
Owsley C, Stalvey B, Wells J, Sloane M. Older drivers and cataract: driving habits and crash risk. J Gerontol A Biol Sci Med Sci.1999;54:M203-M211.Google Scholar
Morris JN, Fries BE, Steel K.
et al. Comprehensive clinical assessment in community setting: applicability
of the MDS-HC. J Am Geriatr Soc.1997;45:1017-1024.Google Scholar
Blom G. Statistical Estimates and Transformed Beta Variables. New York, NY: John Wiley & Sons; 1958.
Cnaan A, Laird NM, Slasor S. Using the general linear mixed model to analyse unbalanced repeated
measures and longitudinal data. Stat Med.1997;16:2349-2380.Google Scholar
Schafer JL. Analysis of Incomplete Multivariate Data. London: Chapman & Hall; 1997.
Dudek FJ. The continuing misinterpretation of the standard error of measurement. Psychol Bull.1979;86:335-337.Google Scholar
Cohen RA, Van Nostrand JF. Trends in the health of older Americans: United States 1994. Vital Health Stat.1995;3:1-328.Google Scholar
Federal Interagency Forum on Aging-Related Statistics. Older Americans 2000: Key Indicators of Well Being. Washington, DC: US Government Printing Office; 2000.
Crum RM, Anthony JC, Basset SS, Folstein MF. Population-based norms for the Mini-Mental State Examination by age
and educational level. JAMA.1993;269:2386-2391.Google Scholar
McHorney CA, Kosinski M, Ware JE. Comparisons of the costs and quality of norms for the SF-36 health
survey collected by mail versus telephone interview: results from a national
survey. Med Care.1994;32:551-567.Google Scholar
Edwards JD, Wadley VG, Myers RS, Roenker DL, Cissell GM, Ball KK. The transfer of a speed of processing intervention to near and far
cognitive functions. Gerontology.2002;48:329-340.Google Scholar
Schaie KW. Intellectual Development in Adulthood: The Seattle
Longitudinal Study. New York, NY: Cambridge University Press; 1996.
Small BJ, Dixon RA, Hultsch DF, Hertzog C. Longitudinal changes in quantitative and qualitative indicators of
word and story recall in young-old and old-old adults. J Gerontol B Psychol Sci Soc Sci.1999;54:P107-P115.Google Scholar
Zelinski E, Burnright KP. Sixteen-year longitudinal and time lag changes in memory and cognition
in older adults. Psychol Aging.1997;12:503-513.Google Scholar
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Mahwah, NJ: Lawrence Erlbaum; 1988.
Hofland BF, Willis SL, Baltes PB. Fluid intelligence performance in the elderly: intraindividual variability
and conditions of assessment. J Educ Psychol.1981;73:573-586.Google Scholar
Willis S, Marsiske M. Life-span perspective on practical intelligence. In: Tupper D, Cicerone K, eds. The Neuropsychology
of Everyday Life. Boston, Mass: Kluwer Academic Publishers; 1990:183-198.