questions to answer

  

DescriptionPART 1
From the Early Enriching Activities Article — why may engagement in stimulating activities early
in life be linked to later life cognitive functioning? (hint: read only the highlighted parts of the
article)
Please see attachment below
PART 2
What are your reactions to the following two videos about Alzheimer’s disease? Do you know
anybody that has had/are having similar experiences? What would you do if your grandparents
or parents were going diagnosed with this disease?
“Life with Alzheimer’s” Presents… TRUTH – YouTube
Experience 12 Minutes In Alzheimer’s Dementia – YouTube
PART 3
Watch the following video on Maite’s experience caring for her mother with Alzheimer’s disease.
Picture yourself as Maite. Imagine the challenges both emotionally and logistically of being in her
position. What do you think would be the hardest aspect of this for you? How would you
prioritize the demands of daily life?
Day in the Life of an Alzheimer’s Caregiver: Heartbreaking – YouTube
Part 4
Watch the following video below about caregiving for someone with Alzheimer’s disease. Do you
think agitation and anxiety are reasons why so many caregivers experience burnout?
Caregiver Training: Agitation and Anxiety | UCLA Alzheimer’s and Dementia Care Program YouTube
PART 6
Based on what you learned from the lecture recording about cognitive reserve. What activities
would you recommend to your family members to build their cognitive reserves?

PART 7
Question:
If someone asked you about Alzheimer’s Disease and Related Dementias — and says they have
Alzheimer’s because they always lose their keys and forget a bill once in a while. Based on the
recorded lecture, what are follow-up questions you would ask them to test whether this is part
of normal aging vs ADRD?
INSANE IN THE MEMBRANE – AGING BODY & MIND – YouTube
PART 8
Question:
Why do you think we observe the paradox of aging?
INSANE IN THE MEMBRANE – AGING BODY & MIND – YouTube
PART 9
Question:
Your friends likely make the common mistake that Alzheimer’s Disease and Dementia as the
same thing. Describe the different to them.
Journals of Gerontology: Psychological sciences
cite as: J Gerontol B Psychol Sci Soc Sci, 2018, Vol. 00, No. 00, 1–11
doi:10.1093/geronb/gby056
Advance Access publication May 07, 2018
Variety of Enriching Early-Life Activities Linked to Late-Life
Cognitive Functioning in Urban Community-Dwelling
African Americans
Thomas Chan, PhD,1,2 Jeanine M. Parisi, PhD,1 Kyle D. Moored, BS,1,2 and
Michelle C. Carlson, PhD1,2
Department of Mental Health and 2Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health,
Baltimore, Maryland.
1
Address correspondence to: Thomas Chan, PhD, Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health, 2024
E. Monument Street, Suite 2-700, Baltimore, MD 21231. E-mail: thomas.chan@jhu.edu
Received: January 24, 2018; Editorial Decision Date: April 30, 2018
Decision Editor: Angela Gutchess, PhD
Abstract
Objectives: The early environment is thought to be a critical period in understanding the cognitive health disparities
African Americans face today. Much is known about the positive role enriching environments have in mid- and late-life
and the negative function adverse experiences have in childhood; however, little is known about the relationship between
enriching childhood experiences and late-life cognition. The current study examines the link between a variety of enriching
early-life activities and late-life cognitive functioning in a sample of sociodemographic at-risk older adults.
Method: This study used data from African Americans from the Brain and Health Substudy of the Baltimore Experience
Corps Trial (M = 67.2, SD = 5.9; N = 93). Participants completed a battery of neuropsychological assessments and a sevenitem retrospective inventory of enriching activities before age 13.
Results: Findings revealed that a greater enriching early-life activity score was linked to favorable outcomes in educational
attainment, processing speed, and executive functioning.
Discussion: Results provide promising evidence that enriching early environments are associated with late-life educational
and cognitive outcomes. Findings support the cognitive reserve and engagement frameworks, and have implications to
extend life-span prevention approaches when tackling age-related cognitive declines, diseases, and health disparities.
Keywords: Cognitive reserve, Developmental assets, Health disparities, Life course, Minority research
Participation in cognitively enriching activities earlier in
the life span is hypothesized to protect against age-related
cognitive declines and diseases (Carlson, 2011; Carlson,
Eldreth, Chuang, & Eaton, 2012; Chan & Carlson, 2016;
Gow, Pattie, & Deary, 2017; Stern, 2002; Wu, Rebok, &
Lin, 2016). Enriching social and affective activities such as
volunteering, multilingualism, and musical activities have
been associated with late-life cognitive functioning (Carlson
et al., 2012b; Craik, Bialystok, & Freedman, 2010; HannaPladdy & MacKay, 2011; Kensinger & Gutchess, 2016).
Much of this evidence relates to mid- to late-life, such that
adults who engaged in more cognitively enriching activities
show greater resiliency to both normal and pathological
forms of age-related cognitive decline (Carlson et al., 2009,
2012b; Ghisletta, Bickel, & Lövdén, 2006; Stine-Morrow,
Parisi, Morrow, & Park, 2008; Wilson et al., 2005). Very
little, however, is known about the link between cognitively enriching activities in childhood (before age 13) and
late-life cognitive functioning. The current study builds
upon what is known about mid- to late-life enrichment
Published by Oxford University Press on behalf of The Gerontological Society of America 2018. This work is written by (a) US
Government employee(s) and is in the public domain in the US.
1
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Research Article
2
Variety of Enriching Early-Life Activities
Builds Cognitive Reserve
Multiple works have suggested that engaging in a variety
of enriching activities supports positive cognitive functioning (Carlson et al., 2012b; Parisi et al., 2012). The link
between earlier life enrichment and late-life cognition stems
from Stern’s (2002) seminal concept of cognitive reserve.
The cognitive reserve hypothesis suggests that engaging
in enriching activities earlier in the life span builds neuroprotective assets (i.e., reserves) that help delay the onset
of clinical deficits (e.g., age-related cognitive declines,
Alzheimer’s disease) until this reserve is overwhelmed by
the expression of pathology. In support, Bradley et al.
(1989) found in three ethnic groups that the number of
play materials in the home, and the variety of experiences
to which children are exposed, were positively related to
their subsequent cognitive development. Additionally,
Wilson and colleagues (2005) found that the frequency of
early-life activities (retrospectively recalled from age 6 to
12), both inside and outside the home (e.g., reading newspapers, going to a museum), were associated with semantic
memory and perceptual speed in a sample of older adults
from 25 residential facilities. Despite few studies, almost
nothing is known about whether engaging in a variety of
enriching activities outside the home in childhood is related
to late-life cognition.
The empirical evidence that demonstrates that a variety of enriching activities outside the home in early life
are neuroprotective comes from studies that have consistently found links between educational attainment and
late-life cognition (e.g., Kemppainen et al., 2008; Richards
& Sacker, 2003; Stern, Albert, Tang, & Tsai, 1999). These
findings generally support what the cognitive reserve and
engagement hypotheses predict—educational opportunities
promote a venue to experience a greater variety of cognitively stimulating activities that help build cognitive reserve
(Stern, 2002).
Although evidence points to engagement in enriching
activities outside the home in early life to be beneficial to
late-life cognition, historically sociodemographic at-risk
populations are less likely to experience adequate enrichment; they may even experience deprivation, as contextual
opportunities to engage in cognitively stimulating activities may be resource restricted both inside and outside the
home. Specifically, African Americans who grow up in the
inner city—such as Baltimore—are at a higher risk for
developing cognitive impairments throughout the life span
because socioeconomic barriers may limit these cognitively
protective opportunities (the Baltimore Memory Study;
Schwartz et al., 2003).
Congruently, much work shows that impoverished
early-life conditions—inside and outside the home—correspond with poorer cognitive outcomes (Duncan, BrooksGunn, & Klebanov, 1994) and increased risk of developing
dementia or Alzheimer’s disease (Borenstein, Copenhaver,
& Mortimer, 2006; Melrose et al., 2014). Likewise, deprivations in early life may also lead to toxic stress responses,
which are detrimental to subsequent cognitive development
(C. A. McEwen & B. S. McEwen, 2017)—and a potential
biological source of health disparities experienced later in
life (Shonkoff, Boyce, & McEwen, 2009). For this at-risk
group, much has been highlighted with deprivation and
poorer later life cognition; however, much less is known
about early-life enrichment as building cognitive reserves
in childhood: an encouraging avenue to ward off potential downstream cognitive impairments and pathologies
(Richards & Sacker, 2003; Stern, 2002).
Taken together, urban-dwelling African Americans are
most at-risk of developing late-life cognitive impairments
and diseases (Mayeda et al., 2016; Tang et al., 2001). This
may be due, in part, to early-life factors, potentially during the formative childhood years when cognitive development sets the foundation for subsequent maturation.
The cognitive reserve and engagement hypothesis suggest
that enriching activities in early life are important to latelife cognition (Carlson, 2011; Carlson, Eldreth, Chuang,
& Eaton, 2012a; Schooler, Mulatu, & Oates, 1999; Stern,
2002; Stine-Morrow et al., 2008, 2014). Thus, a vital question to study is whether engagement in enriching activities
in early life is associated with late-life cognitive performance for those most at-risk to developing cognitive impairments and disorders (illustrated visually in Figure 1). This
study examines the link among early-life engagement in
enriching activities, educational attainment, and late-life
cognition in a sample of sociodemographically at-risk
urban community-dwelling African Americans.
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to examine the association between cognitively enriching
activities in childhood and late-life cognition.
Investigating the role of early-life enrichment on latelife health may be a key to better understanding cognitive
health disparities faced by sociodemographic at-risk groups.
There is little or weak evidence to support genotypic differences between races or ethnicities; however, African
Americans as a whole are still far more at-risk to suffer
from late-life cognitive impairments and diseases compared
with predominately Caucasian samples (Mayeda, Glymour,
Quesenberry, & Whitmer, 2016; Tang et al., 2001). Much
empirical evidence points to examining risk and protective
factors—especially early in life—to identify and address
reasons why some individuals are afflicted by cognitive
impairments and disorders while others, even from the
same upbringing, are resilient to developing them (Masten,
2001; Moceri et al., 2001; Sisco et al., 2015; Stern, 2002).
Together, for older African Americans, little is known
about whether cognitive enrichment in early life fosters
resiliency against age-related cognitive declines and impairments. This study investigates whether this connection
exists in a sociodemographically heterogeneous sample of
aging African Americans with the goal of building reserve
and resilience for those who are most at-risk for dementia.
Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00
Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00
3
Measures
Figure 1. The diagram illustrates the hypothesized connections between
cognitive enrichment in early life and the development of resiliency
against age-related cognitive declines and impairments. People who
engage in more cognitively enriching activities earlier in life build their
cognitive reserves (i.e., neuroprotective assets) exponentially against
age-related cognitive declines and impairments. Contrastly, people
deprived of enriching activities in early life experience an increased
risk of observing evidence of age-related cognitive declines and impairments. It should be noted that cognitive resiliency is plastic throughout the life span and contributions could always be made to cognitive
reserves; however, just like compound interest, the growth is not as
multiplying as if contributions were made earlier in life.
METHOD
Participants
The Baltimore Experience Corps Trial (BECT) was a randomized, controlled trial designed to examine the health benefits to older adults who participated in a community-based
intervention. Specifically, participants who were randomized
into the intervention condition were trained to assist and
mentor at-risk youth in Baltimore City elementary schools
and those in the control condition were offered to serve in a
low-activity volunteering program: see Carlson et al. (2015)
and Fried et al. (2013) for a comprehensive description of
the sample and procedures. Eligibilities include: (a) 60 or
older, (b) speak English, (c) clearance on criminal background check, (d) scored ≥24 on the Mini-Mental State
Examination (MMSE; Folstein, Folstein, & McHugh, 1975),
(e) achievement of sixth grade reading level on Wide Range
Achievement Test (WRAT; Wilkinson & Robertson, 2006),
and (f) agreeing to serve 15 or more hours per week for two
school years if randomized into the intervention condition.
From within the BECT sample, 123 of these people
were enrolled in the Brain and Health Substudy (BHS) to
Enriching early-life activities
EELAs assessed whether participants engaged in cognitively enriching activities in childhood. The inventory
asked participants to answer “yes” or “no” to whether they
engaged in each of the following seven activities before
age 13: (a) learning a foreign language, (b) volunteering
at church, (c) taking lessons (i.e., dance, choir), (d) playing
a musical instrument, (e) scouting, (f) playing team sports,
and (g) taking vacations. Endorsement of each activity was
tallied to derive a total score ranging from 0 to 7, with a
higher score indicating greater engagement in a variety of
cognitively enriching activities in early life (no = 0, yes = 1).
Educational attainment
Educational attainment was computed based on the number of total years of formal education completed. These
years were placed into categories of high school or less,
college, and post-college.
Cognitive assessment
The current study used a standardized battery of neuropsychological tests to evaluate the cognitive abilities of
older adults. Assessments were conducted by trained technicians who followed standardized protocols.
Processing speed
Pattern Comparison Task
Pattern Comparison Task (PCT) is a paper-and-pencil test
assessing visual processing speed (Salthouse & Babcock, 1991).
Participants were asked to determine whether two patterns
were either the same or different as quickly as possible. Scores
were the number of correct patterns distinguished in 30 s.
Trail Making Task – Part A
Trail Making Task – Part A (TMT-A) is a paper-andpencil test assessing psychomotor and visual search speed
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participate in more in-depth assessments (e.g., functional
magnetic resonance imaging [fMRIs], biomarkers, life history questionnaires). Importantly, BHS participants did not
differ from the larger BECT sample in age, gender, education, MMSE scores, or self-reported health (Carlson
et al., 2015). Prior to randomization to the intervention or
control condition, all participants underwent an extensive
battery of assessments to record their respective baseline
metrics. Baseline assessments included neuropsychological
and cognitive functioning, health history, physical ability,
and current and past living conditions and activities (for
more details, see Carlson et al., 2015).
The current study sample is restricted to African
American BHS participants (N = 93) who completed the
enriching early-life activities (EELAs) inventory. This study
was approved by the Johns Hopkins School of Medicine IRB
and each participant provided written, informed consent.
4
(Reitan, 1958). Participants were asked to sequentially connect numbers on a page as quickly as possible (maximum
240s). Scores were the time it took to complete the task.
The Rey Auditory Verbal Learning Test – Immediate Recall
and Rey Auditory Verbal Learning Test – Delayed Recall
Rey Auditory Verbal Learning Test – Immediate Recall
(RAVLT-IR) and RAVLT – Delayed Recall (RAVLT-DR)
are verbal tests assessing auditory verbal learning (Rey,
1964; Schmidt, 2004). Participants were asked to recall a
15-word list over five sequential learning trials. Scores on
the RAVLT-IR were the sum of correctly recalled words
over the five trials. Scores on the RAVLT-DR were the
number of correctly recalled from the list following a
20-min delay.
Executive functioning
Trail Making Task – Part B
TMT-B is a paper-and-pencil test administered after the
TMT-A assessing planning and attentional flexibility: hallmarks of executive set-shifting (TMT-B; Reitan, 1958).
Participants were asked to connect numbers and letters in
an ascending alpha-numeric sequence as quickly as possible
(maximum 420s). Scores were the time it took to complete the
task and TMT-A completion times were used as covariates in
models as baseline controls for individual psychomotor speed.
The Stroop Color-Words
Stroop Color-Words (Stroop C-W) is a computerized test
assessing inhibitory control (Trenerry, Crosson, DeBoe, &
Leber, 1989). Participants were presented with names of
colors (e.g., red, green, blue) and asked to identify the color
ink of these words. Congruent trials were those where the
color ink and the word matched, whereas incongruent trials were those where the color ink and word were different
(e.g., “green” written in blue ink). Inhibitory scores (i.e.,
the Stroop effect) were reaction times on correct trials. The
Stroop Color (Stroop-C) portion, with no interfering words
(e.g., “green” written in green ink), was used as a baseline
covariate to control for individual reaction times.
Covariates
Age, gender, mother’s socioeconomic status, and variety of
late-life activities on the Lifestyle Activities Questionnaire
(LAQ) were used as covariates in the main analyses. The
subjective socioeconomic status of participants’ mothers was incorporated into models to capture the relative
socioeconomic experiences in early life. This was measured
with a subjective social economic ladder ranging from 1 to
10 (worst off to best off), where participants were asked to
consider their mother’s socioeconomic standing (see Adler,
Epel, Castellazzo, & Ickovics, 2000).
The variety of LAQ was used to control for current
activity levels that have been found to be associated with
cognitive functioning (Carlson et al., 2012b) ranging from
doing volunteer work, gardening, and cooking. Binary
scores (0 = never or less than once a month, 1 = at least
once a month) were created and summed to yield a variety
of late-life activity scores (see Carlson et al., 2012b).
Analysis Plan
Multiple linear regression modeling was used to examine
the unique contribution of EELAs in predicting educational
attainment and late-life cognitive functioning on tasks of
processing speed, memory, and executive functioning, after
adjusting for covariates. After testing the hypothesized link
between EELAs and educational attainment, educational
attainment was incorporated in final models as covariates
to isolate the unique variance EELAs may explain. Prior to
conducting linear regression models, descriptive statistics and
plots, Shapiro–Wilk’s tests, and missing value analysis were
used to screen and examine characteristics of the sample.
Skewed variables such as Stroop, TMT-A, and TMT-B assessments were log-transformed to meet normality assumptions
for parametric analyses. Multiple imputation procedures were
used to handle missing data—a less biased method to handle
missing data compared with traditional listwise or pairwise
deletion techniques (Schafer & Olsen, 1998). All available
data (predictors, covariates, cognitive assessments) were used
in a fully conditional specification procedure (MCMC; 10
maximum iterations) to generate 10 imputed data sets.
Results
Characteristics of the Sample
Table 1 presents this study sample’s demographic characteristics, cognitive functioning, and degree of engagement in
early- and late-life activities. Participants (N = 93) were an
average age of 67.2 years (SD = 5.9), were majority females
(71%), and rated their mother’s socioeconomic status to be
relatively standard (M = 5.6, SD = 2.4, range 1–10). The
sample completed an average of 14.1 years of education
(SD = 2.6). Baseline global cognition was intact: MMSE
(M = 28.3, SD = 1.5). The sample had a minimal prevalence
of depressive symptoms: 1 out of 93 participants scored
>10 on the Geriatric Depression Scale (M = 1.3, SD = 1.9).
Results from Little’s Missing Completely at Random
(MCAR) test did not suggest systematic missingness in
the data χ2 (69, N = 93) = 69.11, p = .47. Results from
separate variance t-tests revealed that missing data on the
late-life activities covariate were were not completely missing at random as those with lower educational attainment
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Memory
The Wechsler Digit Span Backward
Digit Span Backward (DSB) task is a verbal test assessing auditory working memory. Participants were asked to
recall in backward sequential order a list of numbers that
were read aloud to them. Scores were the longest chain of
correctly recalled numbers.
Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00
Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00
Table 1. Demographic and Cognitive Characteristics of
Participants
M
SD
60.0–82.3
8.0–21.0
1.0–10.0
24.0–30.0
44.0–70.0
1.4–1.9
16.0–40.0
17.0–55.0
0–12.0
0–11.0
1.7–2.6
2.9–3.3
11.0–25.0
0–7.0
31.2%
37.6%
46.2%
51.6%
54.8%
68.8%
Note: RAVLT = Rey Auditory Verbal Learning Test; WRAT = Wide Range
Achievement Test. Log-transformed: Trial Making Task – Part A and Part B,
Stroop Color-Word. N = 93.
had higher levels of missingness values, t(14.7) = −3.40, p < .01. Otherwise, missing was relatively minimal on covariates and outcomes: mother’s socioeconomic status (6.45%) and variety of late-life activities (10.75%); cognitive assessments—Stroop-C (5.38%), Stroop C-W (6.45%), TMT-B (1.08%), and DSB (1.08%). Lastly, participants reported being involved in an average of 18.3 activities in later life (SD = 3.2). In terms of the current study’s main variable of interest, EELAs, participants reported engaging on average in three of seven activities in early life (childhood; SD = 1.7). EELAs and Educational Attainment Table 2 displays the results from linear regression modeling (final step). These results revealed that the degree of engagement in EELAs uniquely predicted educational attainment (β = .29, p < .05). EELAs and Processing Speed Table 3 displays results showing that the degree of engagement in EELAs uniquely predicted PCT (β = .38, p < .001) Table 2. Variety of EELAs Predicting Educational Attainment Educational attainment Final model B SE β (Constant) Age Gender Mother’s socioeconomic status Variety of EELAs 0.26 0.01 −0.22 −0.06 0.14 1.01 0.01 0.18 0.04 0.05 .10 −.12 −.16 .29* F R2 ΔR2 EELAs 2.79* .11 .07*** Note: EELAs = enriching early-life activities. N = 93. *p < .05. ***p < .001. and trended in significance in predicting TMT-A (β = −.22, p = .054). EELAs and Memory Table 4 displays the association between engagement in EELAs and memory performance. Results revealed a lack of association between EELAs and RAVLT-IR (β = .19, p = .12), RAVLT-DR either (β = −.06, p = .63), or DSB (β = .18, p = .14). EELAs and Executive Functioning Table 5 displays results that the degree of engagement in EELAs uniquely predicted the set-shifting component of executive function, measured by TMT-B (β = −.22, p < .05). In contrast, EELAs did not predict the inhibitory component of executive function, measured by Stroop C-W (β = −.05, p = .56). Sensitivity Analyses Sensitivity analyses were conducted to determine if there were specific drivers of the associations observed between EELAs and late-life cognition (presented in Supplementary Table 1, Models 1–4). First, we examined whether one of the seven EELA items was primarily responsible for the associations observed and found that no one activity was a stronger predictor than the summary measure (data not presented; ps > .10). Second, we accounted for WRAT
scores as a measure of educational quality in our models
and found that findings remained consistent with the exception that TMT-A was no longer a significant trend (Model
2, β = .18, p = .11). Third, we excluded specific covariates
that could serve as potential mediators of cognitive reserve,
including late-life LAQ (Model 3) and educational attainment (Model 4). Although findings were generally consistent, we found that the RAVLT-IR (Model 3, β = .21, p < .10; Model 4, β = .25, p < .05) and DSB (Model 3, β = .20, p < .10; Downloaded from https://academic.oup.com/psychsocgerontology/advance-article-abstract/doi/10.1093/geronb/gby056/4993339 by guest on 13 December 2018 Age (71% female, 29% male) 67.2 5.9 Years education completed 14.1 2.6 Mother’s socioeconomic status 5.6 2.4 Late-life cognition Mini-Mental State Examination 28.3 1.5 WRAT 58.6 6.6 Speed of processing Trial Making Task – Part A 1.6 0.1 Pattern Comparison Task 26.6 5.0 Memory functioning RAVLT – Immediate Recall 39.4 7.3 RAVLT – Delayed Recall 6.6 2.6 Digit Span Backwards 5.1 2.3 Executive functioning Trial Making Task – Part B 2.0 0.2 Stroop Color-Word 3.1 0.1 Variety of activities Late life (# endorsed of 29) 18.5 3.4 Early life (# endorsed of 7) 3.1 1.7    Learning a foreign language (% endorsed) 15.1%     Playing a musical instrument    Scouting    Taking lessons    Playing team sports     Volunteering at church    Taking vacations Range 5 Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00 6 Table 3. Variety of EELAs Predicting Processing Speed Trail Making Task – Part A Pattern Comparisons Task B SE β B SE β (Constant) Age Gender Mother’s socioeconomic status Educational attainment Variety of late life activities Variety of EELAs 1.21*** −0.01 −0.03 0.01 0.01 0.00 −0.02 0.18 0.00 0.03 0.01 0.02 0.00 0.01 .27** −.09 .16 .06 .04 −.22† 39.70*** −0.26 1.36 −0.31 −0.83 0.06 1.14 6.19 0.08 1.07 0.23 0.63 0.16 0.33 −.31*** .12 −.14 −.13 .04 .38*** 2.31* .14 .04† F R2 ΔR2 EELAs 4.85*** .25 .11*** Notes: EELAs = enriching early-life activities. Trail Making Task – Part A was log-transformed. N = 93. † p = .054. *p < .05. **p < .01. ***p < .001. Table 4. Variety of EELAs Predicting Memory Performance RAVLT – Immediate Recall RAVLT – Delayed Recall Digit Span Backward Final model B SE β B SE β B SE β (Constant) Age Gender Mother’s socioeconomic status Educational attainment Variety of late-life activities Variety of EELAs 33.45*** −0.08 3.71 −0.55 0.84 0.27 0.81 9.66 0.13 1.67 0.35 0.99 0.25 0.50 −.07 .23* −.17 .09 .12 .19 10.05** −0.06 0.73 −0.13 0.60 −0.03 −0.09 3.50 0.05 0.61 0.13 0.36 0.09 0.18 −.01 .13 −.11 .19 −.04 −.06 6.24* −0.03 0.31 −0.15 0.30 0.00 0.24 3.11 0.04 0.55 0.11 0.32 0.08 0.16 −.07 .06 −.16 .11 .00 .18 2.18 .13 .03 F R2 ΔR2 EELAs 1.11 .07 .00 0.98 .07 .02 Note: EELAs = enriching early-life activities; RAVLT = Rey Auditory Verbal Learning Test. N = 93. *p < .05. **p < .01. ***p < .001. Table 5. Variety of EELAs Predicting Executive Functioning Trail Making Task – Part Ba Stroop C-W Final model B SE β B SE β (Constant) Age Gender Mother’s socioeconomic status Educational attainment TMT-Aa or Stroop-Cb Variety of late-life activities Variety of EELAs 0.46 0.01 −0.07 0.03 −0.04 0.59 0.00 −0.03 0.29 0.00 0.04 0.01 0.02 0.14 0.01 0.01 .25** −.15 .26** −.15 .38*** .06 −.22* −0.11 0.00 0.03 0.00 0.01 1.01 0.00 0.00 0.30 0.00 0.01 0.00 0.01 0.09 0.01 0.00 .10 .16** −.04 .06 .80*** −.01 −.05 F R2 ΔR2 EELAs 9.02*** .43 .03* 28.62*** .70 .00 Note: EELAs = enriching early-life activities; Stroop-C = Stroop Color; Stroop C-W = Stroop Color-Word; TMT-A = Trail Making Task – Part A. Trail Making Task – Part B and Stroop C-W were log-transformed. N = 93. *p < .05. **p < .01. ***p < .001. Downloaded from https://academic.oup.com/psychsocgerontology/advance-article-abstract/doi/10.1093/geronb/gby056/4993339 by guest on 13 December 2018 Final model Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00 Model 4, β = .21, p < .10) now demonstrate a trend or become significant. African American older adults are disproportionately more likely to be afflicted by age-related cognitive declines and diseases (Mayeda et al., 2016; Tang et al., 2001). Much is known about the role adverse early-life experiences have on cognition for at-risk groups; however, we know little about the role of enrichment—a hypothesized link to neuroprotection against age-related declines and diseases (i.e., cognitive reserve; Stern, 2002). Subsequently, the current study analyzed a standardized battery of neuropsychological tests and a seven-item retrospective inventory of EELAs administered to a sample of community-dwelling African American older adults in Baltimore. Results provide evidence that a variety of EELAs were favorably linked to outcomes in later life educational attainment, speed of processing, and executive set-shifting. These findings extend upon previous work that found protective associations between late-life variety of lifestyle activities and cognition (Carlson et al., 2012b; Parisi et al., 2012) and contributes to the growing body of work on factors of cognitive resilience and reserve throughout the life span (e.g., Richards & Sacker, 2003; Stern, 2002). Additionally, this study used a simple measure, designed to be robust to recall bias, to connect early-life activities to late-life cognition. Growing evidence suggests that using a life span approach is vital to understanding how earlier exposures may be protective to downstream cognitive functioning (e.g., Gow et al., 2017; Wu et al., 2016)—especially important for groups who are at higher risks of developing age-related cognitive impairments and diseases. Findings also yield support for the benefits of early engagement in a variety of activities to late-life cognition; specifically reinforcing past empirical and theoretical links between engagement and cognitive processing speed (Schooler et al., 1999; Stine-Morrow et al., 2008, 2014) and executive functioning (Carlson et al., 2012a). Speed of processing and executive set-shifting are two cognitive functions that distinctively decline with age—synonymous with being mentally sharp. Early exposure to a variety of enriching activities may prime the cognitive and motivational systems (e.g., need for cognition) of a person to seek novelty and complexity throughout the life course: as almost everything in childhood is novel and requires consistently honing the abilities to differentiate, process, and integrate (Wu et al., 2016). These early abilities related to efficient integration and differentiation are necessary for the “preservation of an alert and vital mind”—one optimal developmental outcome of a person in later life (Csikszentmihalyi & Rathunde, 1998). In parallel to cognitive reserve, we propose that a variety of enriching experiences facilitates positive emotional development (e.g., cognitive-emotional complexity: Barrett, 2009; subjective well-being: Sheldon, Boehm, & Lyubomirsky, 2012). As suggested by this research, although not exclusively related to enriching activities, we postulate that a variety of experiences may build emotional complexity and ego-resilience—psychological reserves to adapt to later adverse life circumstances (Csikszentmihalyi & Rathunde, 1998). Building these psychological reserves earlier in the life course may represent a core component to developing and strengthening well-being (Masten, Cutuli, Herbers, & Reed, 2009). For instance, a variety of early enriching life experiences may strengthen protective psychological processes linked to defenses against age-related cognitive declines (growth mindsets and open-minded input-driven learning; Chan & Carlson, 2016; Wu et al., 2016) and risk of dementia (purpose in life; Sutin, Stephan, & Terraccino, 2018). Likewise, strengthening psychological reserves earlier in the life course may promote brain reserves in areas most vulnerable to age-related impairments such as the hippocampus and amygdala (Davidson & McEwen, 2012). Overall, further examination of the role that a variety of experiences may have in building reserves is a promising step toward understanding the early-life activities associated with the development of cumulative protection against age-related cognitive declines, diseases, and psychopathologies. In terms of approach, universally supported theoretical and empirical frameworks—cognitive reserve hypothesis (Stern, 2002) and engagement hypothesis (Carlson, 2011; Carlson et al., 2012b; Schooler et al., 1999; Stine-Morrow et al., 2008, 2014)—warrant the greater need to investigate the link between early-life enrichment and later life cognitive functioning. Although the study of adverse childhood environmental exposures has dominated the literature and has been extremely important to identifying at-risk populations, understanding how enrichment can potentially be protective for cognitive and brain health is correspondingly valuable. The goal of providing early-life enrichment should not only be to guard people against age-related pathology, but also to promote growth to facilitate their long-term potential. Likewise, the absence of the negative (e.g., pathology) does not mean someone is flourishing (Seligman & Csikszentmihalyi, 2000). Investigating the enriching factors of early life are as equally, if not more, important to understand why some people make it and others do not despite coming from similar sociodemographic risk environments. As a focus on the potential benefits of early-life enrichment, the current study summed the number of enriching childhood experiences via the variety of EELA inventory. This approach parallels and complements the standard used to assess the relationships between the number of adverse childhood experiences—better known as ACEs—and negative outcomes (e.g., Anda et al., 2006). The EELA inventory offers a significant methodological contribution to connect early-life exposure with late-life cognitive plasticity, and researchers investigating cognitive reserve, environmental Downloaded from https://academic.oup.com/psychsocgerontology/advance-article-abstract/doi/10.1093/geronb/gby056/4993339 by guest on 13 December 2018 Discussion 7 8 Prevention Intervention and Policy Implications The current study’s findings have intervention and policy implications from a life course perspective to assist in tackling the larger issue of health disparities. First, our findings provide further evidence suggesting that building cognitive reserve earlier in the life span may strengthen late-life cognitive functioning. Since African Americans are at greater risk of developing cognitive impairments in later life, understanding how to best intervene in early life is a key to taking a public health prevention approach to benefit future generations. In support, interventions that have provided (predominately) African American children with enriching educational experiences, such as the Abecedarian Project and High/Scope Perry Preschool Program, have yielded favorable outcomes in educational attainment (Belfield, Nores, Barnett, & Schweinhart, 2006; Campbell et al., 2012). These exemplary interventions, however, concluded in early childhood (~age 5), are resource intensive, and focused exclusively on enrichment in the classroom setting. The current study’s findings suggest that another promising avenue to promote cognitive reserve is through providing opportunities to engage in enriching activities outside the classroom; this suggests increasing investments toward positive youth development programs that provide a variety of enriching experiences (e.g., volunteering, team sports) put at-risk youth on greater growth, reserve, and resilience trajectories (see Catalano, Berglund, Ryan, Lonczak, & Hawkins, 2004). Historically, however, local and national housing (e.g., Federal Housing Administration racial covenants) and educational policies (e.g., Plessy vs. Ferguson) have led to social disadvantages throughout the life course—that intervening with early-life enrichment does not address. Institutionalized-level policies experienced earlier in the life span, such as Brown vs. Board of Education (e.g., desegregated vs. segregated schools; Whitfield & Wiggins, 2003), have been linked to cognitive performance among older African Americans. Moreover, historical discriminatory policies have limited educational quality and early enrichment opportunities and have increased the likelihood of experiencing adverse childhood experiences, toxic stress responses due to exposures to accumulated stressors, poorer self-regulatory capacities (e.g., coping strategies), and stunted cognitive development (C. A. McEwen & B. S. McEwen, 2017) factors in route to downstream health disparities (Shonkoff, Boyce, & McEwen, 2009). Accordingly, investments in large-scale projects earlier in the life span, such as the Harlem Children Zone, aim to address some of these disadvantages by offering early-life support services concurrently with providing a variety of enrichment opportunities. Compared with the historical disadvantages experienced, these large-scale projects are postulated to be associated with favorable downstream educational achievement and subsequent cognitive functioning. Limitations and Future Directions Although this study’s findings elucidated connections between early-life enrichment and late-life educational attainment and cognition, there are limitations ripe for future research to address. First, the measure of variety of early-life activities is retrospective, which is susceptible to recall bias. Additionally, the novelty of the EELA means that there is no measure of validity or reliability. Also, this study inventoried only seven items that were Downloaded from https://academic.oup.com/psychsocgerontology/advance-article-abstract/doi/10.1093/geronb/gby056/4993339 by guest on 13 December 2018 enrichment, or protective factors can easily incorporate the EELA questions into prospective studies. The bias of retrospective recall for this measure was designed to be low by asking whether individuals engaged in these activities or not. Past research supports this approach as retrospective recall can be very accurate even after 50+ years (Berney & Blane, 1997) when the content is simple and concrete (i.e., devoid of emotion, retrospective impact bias; Wilson, Meyers, & Gilbert, 2003). In developmental research, cohort studies are still considered a gold standard, although they are susceptible to cohort effects. Science relies on replication as a vital process to corroborate toward understanding the “truth,” thus both cohort studies and retrospective recall measures should be methodologies used to vet the connections between early-life activities and late-life outcomes. Simple and practical instruments to implement into research studies, such as the EELAs, help provide a proxy life-span approach to connecting early and late life (Berney & Blane, 1997). Moreover, implementing the EELA questionnaire in other samples may help determine whether a variety of activities in early life is predictive and protective in normative samples: does this relationship between early enrichment and later life cognition generalize to other samples which may not be at risk? Ubiquitously, education has been an important indicator of cognitive reserve and functioning and subsequent resilience to cognitive declines and impairments late in life (Stern, 2002). Correspondingly, the current study’s findings suggest engagement in more variety of activities in early life corresponds with greater educational attainment by late life. The current study’s results expand on previous works in the larger BECT sample (Parisi et al., 2012) and offers evidence to support life-long plasticity and resilience of enriching exposures in youth both in-thehome (e.g., Bradley et al., 1989; Wilson et al., 2005) and outside-the-home (Gow et al., 2017). Our findings suggest that exposure to enriching environments early in life may help perpetuate the likelihood of ongoing opportunities for sociodemographically at-risk groups to build their developmental potential. Thus, the focus on understanding both adverse and enriched childhood experiences is suggested to be necessary to tackling and narrowing health disparities related to late-life cognitive resilience. Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00 Journals of Gerontology: PSYCHOLOGICAL SCIENCES, 2018, Vol. 00, No. 00 Conclusions The approach and findings of the current study provide promising evidence that engagement in a variety of enriching childhood activities links with cognitive health and resilience. Realistically, however, merely providing at-risk groups more enriching environments are not going to be the panacea to quelling cognitive health disparities: as there are many real historical, societal, economic, and familial factors. Nevertheless, investigating both the adverse and enriching conditions of early-life exposures is necessary to improve the science and eventual interventions that support cognitive health throughout the life span for people who are most at risk—that is ACEs and EELAs. The current study’s findings suggest that engaging in a variety of positive exposures in early life may keep the mind sharper later. Lastly, the cognitive reserve could be thought of like as an emergency savings account, it lays dormant, until something goes awry; those with more savings are able to stave off crisis (i.e., cognitive declines or diseases), while those with little reserve go into crisis quicker. For at-risk groups, the current study’s findings suggest that investing in enrichment earlier in life are important to help build this emergency defense. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online. Funding This work was supported by the National Institute on Aging (P01 AG027735 to M. C. Carlson); Johns Hopkins Epidemiology, Biostatistics, and Mental Health of Aging Research Fellowship (NIA-T32AG000247 to T. Chan and K. D. Moored); and a grant by the Alzheimer’s Drug Discovery Foundation. 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