MIND Food and Speed of Processing Training in Older Adults With Low Education, The MINDSpeed Alzheimer's Disease Prevention Pilot Trial
Phase: NA
Conditions: Low Education, Cognition, Cognitive Training, Polyphenols
Sponsor: Indiana University

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Official Title: MIND Food and Speed of Processing Training in 
Older Adults with Low Education, The MINDSpeed Alzheimer’s 
Disease Prevention Pilot Trial 
 
NCT03419052 
 
Version date: 8/1/23 
 
 
 
 
 
 
 
 

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SPECIFIC AIMS 
Low education, i.e., fewer than 12 years,(1) is the top population-attributable Alzheimer’s disease (AD) risk 
factor worldwide.(2)  The National Academy of Medicine has recommended that education be routinely 
recorded in electronic health records (3) and the IOM 2015 report Cognitive Aging recommended evaluation of 
multi-component cognitive interventions for vulnerable populations.(4)  In the proposed study, we seek to test 
an intervention that could reach millions of older adults with low education.(5)  Our intervention involves two 
positive behaviors that may have additive effects on cognition—consumption of “MIND” foods and speed of 
processing training.  Consistent with the objectives of PA-16-365, we aim to establish feasibility, mechanisms 
of action, and initial efficacy of these non-pharmacological interventions in older adults with low education 
receiving care in federally qualified health centers (FQHC).  FQHCs provide care to over 40 million lower SES 
adults in the U.S.(6)     
 
Low cognitive reserve is a leading hypothesis for low education’s effects on early cognitive decline and AD 
onset.(7)  Adult hippocampal neurogenesis (AHN) is fundamental to cognition and learning(8) and diet is 
critical to maintaining an environment conducive to AHN.(9)  Specific dietary components (e.g., polyphenols 
and polyunsaturated fatty acids) have emerged as key interventions for cognitive health and function.(9, 10) 
Unfortunately, low education is associated with unhealthy dietary habits(11) that are very difficult to completely 
eliminate.(12)  Fortunately, supplementing unhealthy dietary habits with healthy foods has real potential to 
impact cognitive health and function.(12)  For example, moderate adherence to the “MIND” diet developed by 
the Rush Memory and Aging group cut AD risk by one-third over 4.5 years.(13)  The diet was built from 
epidemiological (14, 15) and randomized controlled trial evidence that consumption of certain foods can 
improve short-term cognition including speed of processing.(16-18)  
While dietary components support the creation of new neurons, cognitive training promotes integration of 
neurons into neural networks.(19-21)  Speed of processing training, in particular, results in very large 
performance gains(22-24) that are maintained for years.(25, 26) “Speed training” targets fluid mental 
processing speed and reduces the length of time needed to process more and more complex information.(27)  
In a secondary analysis of data from the largest cognitive training trial ever conducted, we showed speed 
training had a large effect among participants with fewer than 12 years of education; the effect size was 50% 
larger in those with fewer than 12 years of education compared to the subsample with 16 or more years.(28)  
The training advantage in the less educated carried out to 5 years. 
At this time, there are no published randomized trials of speed of processing training combined with MIND 
foods.  Nor are there published randomized trials of MIND foods or speed training properly designed and 
powered to assess effects in older adults with low education.  Using a 2x2 factorial design, we propose to test 
in a randomized controlled trial the effects of speed training and MIND foods in older adults with low education.  
The ultimate goal of this line of research is to prevent or delay AD in this vulnerable group.   
This study will enroll older adults receiving care in Eskenazi Health who have 12 years of education or less.  
Interested and eligible individuals who consent to the study will receive a study-provided tablet running our 
professionally designed MINDSpeed app.  The app will provide each participant with 12wks of access, support, 
and incentives for all 4 study arms: 1) MIND food + Speed training, 2) MIND food + Speed training control, 3) 
MIND food control + Speed training, or 4) MIND food control + Speed training control.   We refer to the 
combinatorial MIND food and Speed training intervention as MINDSpeed, while the combination of food control 
and speed training control we refer to as Double Control. 
The primary specific aim of this pilot clinical trial is to determine MINDSpeed effects on an executive cognitive 
composite (ECC) score relative to Double Control.  The primary hypotheses are that MINDSpeed is more 
effective than MIND food or Speed training alone and all are more effective than Double Control in improving 
the objective ECC measure at immediate post-training and 6-month follow-up.   
Exploratory aims are to determine: 1) feasibility (i.e., adherence, adverse events, costs), 2) treatment effect 
modifiers (e.g., education, age, baseline cognitive status, APOE ε4 carrier status), and 3) mechanisms 
including the roles of inflammation, oxidative stress, hippocampal volume, and functional connectivity.   

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With no current treatments for AD, designing and testing interventions to prevent AD in vulnerable populations 
has become a top priority.(4)  The intervention we propose has potential to be highly effective in a vulnerable 
population.  Support for MINDSpeed feasibility and efficacy could lead to a follow-on AD prevention trial 
through existing and scalable programs and applications. 
 
[During the COVID-19 health crisis, biospecimen collection, imaging, and Viromeaging substudy 
enrollment will cease.] 
 
Viromeaging sub-study: [Enrollment closed 5/31/22 for this sub-study.] 
The purpose of this sub-study is to identify members of the aging population at increased risk for disease and 
frailty with the detection of biomarker panels applicable to inflammation in aging.  Frailty is a significant and 
independent predictor of poor clinical outcomes, including prolonged hospitalizations and death. It is now 
understood that aging is the single most important risk factor for chronic diseases and frailty.  
 
A sizable proportion of people over 65 years of age experiences more than one age-associated chronic 
disease (musculoskeletal, cardiovascular, neurodegenerative, metabolic, cancer) and will live with them for 
many years and decades, enjoying a reduced quality of life and costing themselves, and society, a 
disproportional amount of resources. One common thread frequently found in normal aging as well as in age-
associated chronic diseases is low-level inflammation, manifested by elevated levels of pro-inflammatory 
cytokines and acute phase reactants. This inflammation has been associated with development and/or 
accelerated progression of age-related chronic diseases. 
 
Aims of this sub-study are to 1) measure the blood virome in older adults, 2) assess markers of chronic 
inflammation in older adults and correlate these with evidence of immune aging and virus-specific immune 
responses, and 3) correlate the blood virome, evidence of systemic inflammation and the presence of chronic 
disease and frailty in test subjects and create a potential inflammatory and viral “biomarker” for the detection of 
the complications of aging. 
 
Investigators hypothesize that older adults will exhibit greater abundance of viruses, leading to increased 
immune stimulation and chronic inflammation, and this burden will be able to predict propensity for and timing 
of frailty and age-related diseases. The ultimate goal is to suggest potential interventions that might mitigate 
the development of chronic disease and frailty in the aging population. 
 
IMPACT 
This year’s dependent care costs of one quarter trillion dollars are projected to double to one half trillion by 
2030 as the population ages and the prevalence of Alzheimer’s Disease (AD) and other dementias grow.(29)  
Interventions that improve brain structure and function, and cognitive reserve of vulnerable older adults have 
the potential to forestall impairment in functional status(25, 30) and possibly delay time-to-onset of AD.(31)(32) 
A 2014 Lancet report estimated population-attributable AD risk factors from published meta-analyses and 
concluded that seven modifiable risk factors are responsible for almost one-third of all AD cases in the U.S.(2)  
Having completed no more than lower secondary education, equivalent to fewer than 12 years,(1) is one of 
these risk factors.  In addition to the potential direct effects of low early life education on cognition,(33) adults 
with low educational attainment are significantly more likely to have each of the other six modifiable AD risk 
factors including obesity, physical inactivity, hypertension, smoking, diabetes, and depression.(34, 35) Adults 
with low education are also more likely to have a low quality diet,(36, 37) which has emerged as a major AD 
risk factor as well.(9, 13)  According to the U.S. Census, seven million of today’s older adults did not complete 
upper level secondary education (i.e., no high school degree).  This represents almost one-fifth of the current 
older adult population.  Interventions are needed to improve cognitive reserve in this vulnerable population.   
Large randomized controlled trials have identified targeted cognitive training, particularly speed of processing 
training, as having large effect sizes for improved cognitive performance,(22, 24) including unsupervised, 
home-based training.(23) In fact, speed training has the largest effect size of any known cognitive intervention 
and this effect is lasting and has far transfer to other outcomes including depression.(38-41) Our analyses 
suggest that speed training had a dose-response association with 10yr dementia risk in a sample comprised 
mostly of well-educated seniors.(43)  In other secondary analyses, however, we showed that older adults with 

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low education have the largest performance responses to training.(42) Targeting those most responsive to 
training—e.g., seniors with less education—and optimizing that response could lower dementia risk. 
 
Importantly, dietary components have been shown to be critical in optimizing the brain’s ability to change in 
response to stimuli such as speed training.  In a comprehensive  review, the Institute of Psychiatry & 
Neuroscience, King’s College London, concluded that “animal and some human studies now demonstrate that 
diet-induced brain plasticity offers tremendous potential in promoting emotional and cognitive well-being” and 
recommended translational research with a focus on polyphenols to both reduce risk and increase reserves.(9)  
In a complex process, food-derived components serve as precursors of neurotransmitters and AHN, both keys 
to learning.  Thus, polyphenol-rich diet with speed training is a potentially high impact multi-component 
intervention for older adults with low education.   
 
Testing multi-component interventions in vulnerable populations is a research recommendation of the IOM 
April 2015 Cognitive Aging report(4) and a prevention recommendation of the May 2015 NIH AD summit.(44)  
Evaluation of treatment mechanisms was a further recommendation of the IOM report.  Successful outcomes 
in the proposed study would support a properly powered AD prevention trial, contribute to the National Plan to 
find effective therapies to prevent AD by 2025, and establish a multi-component prevention intervention that 
could reach millions of vulnerable adults.   
 
SIGNIFICANCE 
Low education is associated with a significantly higher risk of dementia.(2)  In analyses of six independent 
samples from four different countries, Piccinin et al showed education was associated with cognitive decline up 
to 11 years of education.(45)  Another recent study found a direct effect of years of education on cognitive 
decline up to 10 years of education.(46)  In these studies, years of education beyond 10 or 11 years were 
either indirectly associated with cognitive decline(46) or had very little association.(45)  The International 
Standard Classification of Education classifies 10 to 11 years of education as lower secondary.(1)  Recent 
estimates of population-attributable risks for AD identify low education as responsible for 19% of AD cases 
worldwide; the largest of seven modifiable risk factors.(2) Low education was found to be a top five modifiable 
risk factor in the United States(2) where lower secondary education has been compulsory since 1918.(47)  
The leading hypothesis for education’s effect on cognitive decline and AD is cognitive reserve,(48) which can 
be defined as the brain pathology level at which clinical deficits appear.(49)  A recent study with imaging 
showed that in the case of both amyloid and vascular effects on cognitive decline, more years of education and 
higher self-reported cognitive activity were associated with later onset of cognitive deficits but not rate of 
cognitive decline.  In fact, the data predicted that a participant with a high education/occupation score who had 
both amyloid and vascular pathology would have a 5-year advantage in cognitive function compared to a 
participant who had similar pathology but a low education/occupation score.(7)  Evidence that low educational 
attainment adversely affects cognitive reserve and dementia risk raises the question of how to protect the 
brains of millions of adults who did not complete upper level secondary education.   
Fortunately, promising interventions exist.  For example, the Advanced Cognitive Training for Independent and 
Vital Elderly (ACTIVE) randomized controlled trial,(25) and others,(23, 24) showed that short-term targeted 
cognitive training improves cognitive function in older adults.  All three major cognitive training trials to date 
have enrolled well-educated older adults with 80% to 95% having completed upper level secondary 
education(23-25) and five smaller trials have had samples with the same restricted range of education.(27)  
Across these trials, speed of processing training has had an effect size ranging from 0.61 to 1.67 on Useful 
Field of View (UFOV), a measure of visual attention and psychomotor integration.(27)  The ACTIVE study was 
a large multi-site randomized trial and had an effect size of 1.46 at immediate post-test and 1.21 at one year 
after training.  ACTIVE’s sample size was large enough to allow secondary analyses of training effects by 
educational attainment.  As we describe in the Preliminary Studies section, we found evidence that speed of 
processing training had an effect size in older adults with no upper secondary education around 2.0 on the 
UFOV measure at immediate post-test and at one year after training.(42)   
The UFOV gains in the ACTIVE trial have been shown to have meaningfully improved locus of control(50), 
self-rated health(38), depressive symptoms,(39) health-related quality of life,(40), and driving outcomes 
including longer time to driving cessation(51), and fewer at fault motor vehicle collisions.(41)  Four days per 

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week for six weeks of computerized speed of processing training was recently shown, relative to leisure mental 
activities, to maintain two networks susceptible to normal and abnormal aging effects including the default 
mode network (DMN) and central executive network (CEN).(52) Thus, we include speed of processing training 
in our intervention for its broad effects and potential to enhance cognitive reserve.(39)   
 
The second component of our multi-component intervention is focused on establishing an environment 
conducive to training response and learning.  Long-term potentiation underlies learning and refers to activity-
dependent synaptic changes in response to frequent stimuli.(53)  This process depends on a micro-
environment that supports the presence of neural stem cells in the dentate gyrus subregion of the 
hippocampus and their renewal and differentiation into mature neurons.(9)  Blood vessels deliver biochemical 
factors that improve (or worsen) this micro-environment, which is one of the ways in which diet may affect 
cognition.(9, 10)  Recent animal studies have shown multiple subclasses of flavonoids, a class of polyphenols, 
remain stable after digestion and may cross the blood-brain barrier to reach the hippocampus.(54)  
Polyphenols play a central role in managing inflammation, oxidative stress, and metabolic function.(54, 55) As 
reviewed in detail by our consultant Dr. Tangney, plant-based foods like berries, nuts, cocoa, and green leafy 
vegetables contain a range of polyphenols and improve metabolic function,(13, 54) combat oxidative stress, 
and are cardio and neuroprotective.(10)  Flavonoids in particular have been shown in human trials of 12 weeks 
duration to improve metabolic function, and to reduce oxidative stress and inflammation.(10, 54)  One trial in 
adults aged 50-69 years showed that participants randomized to high vs low flavonols via cocoa for 3 months 
had higher dentate gyrus function as evidenced by functional MRI.(56) Importantly, consumption of these foods 
two or more times per week for 12 weeks has been 
shown to improve performance on cognitive tests, 
including speed of processing.(18),(14),(57),(16)    
Diets high in these foods are increasingly studied 
for their potential to delay AD.  Secondary 
analyses of large randomized trials have shown 
that the Mediterranean diet improves Mini-Mental 
State Examination (MMSE) scores relative to low 
fat diet control(16) and that the DASH diet relative 
to usual diet improves speed of processing.(17)  
More recently the MIND diet, which de-emphasizes 
dairy, potato, and general fruit consumption and 
specifically emphasizes berries, nuts, olive oil, and 
green leafy vegetables, has been shown to be 
associated with lower AD risk in a non-randomized 
prospective study.(13)  Even modest MIND diet 
adherence was associated with reduced AD risk.  As shown in our broad conceptual model (Figure 1), we 
include MIND foods in our intervention for their potential effects on cognitive reserve. 
 
User centered design and behavioral economic principles guided the proposed interventions to maximize 
uptake, adherence, and scalability.  As we describe under Interventions, our professional design team worked 
with older less educated adults and consultants to create the proposed intervention.  User design preferences 
were paired with behavioral  principles—three in particular: 1) “easy” substantially increases uptake,(58) 2) 
small and frequent incentives significantly improve adherence,(59, 60) (61) and 3) intrinsic motivation is key to 
maintaining behavior.  Regarding easy, we limit cognitive training to speed of processing to maximize effect 
per minute of training.  Relatively speaking, the MIND diet is easy but we make it really easy; tablet based food 
selection with home delivery.  Intrinsic motivation is designed into speed of processing trainings by 
continuously adjusting to an individual’s competency, while foods are self-selected.(62, 63) 
 
INNOVATION 
As far as we are aware, this will be the first study of: 
1) cognitive training or MIND food interventions targeting less educated older adults,  
2) MIND and speed training as a multi-component intervention in any population, 
3) mechanisms of action, including fluid and imaging biomarkers, from this multi-component intervention, 

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4) MIND food consistent with user preferences and existing nationwide distribution systems, and 
5) Speed of processing and MIND foods access, support, and incentives via a single, integrated 
application. 
 
APPROACH 
Figure 2 shows our study design.  This is a 4-arm 
randomized controlled trial.  Those eligible and 
willing will be scheduled for a visit to complete 
informed consent and baseline assessments.  
Following completion of baseline assessments, 
patients will be randomized to two of the four 
treatment conditions.  Tablet devices and the 
study application will give access, incentives, and 
support for all interventions including control 
conditions (see Intervention section).  We assess 
all outcomes and mechanisms immediately post-
training (3-months) and at 6 month follow-up.  The 
primary outcome is an executive cognitive 
composite score.   
Preliminary Studies and Data.  Our team is 
multidisciplinary with expertise relevant to the 
proposed study.  We have two decades of experience enrolling and retaining vulnerable older adults, 
particularly lower SES adults from FQHCs, in epidemiological studies and single and multi-site randomized 
trials(64-68)(22, 25, 69, 70)(71)(72)(73)(74, 75) and in the Indiana Alzheimer Disease Center.  Saykin is PI of 
the Genetics Core of AD neuroimaging initiative (ADNI) and has published studies integrating ADNI’s 
longitudinal cognitive, neuroimaging, biomarker and genetics data,(76-80) reviews of conceptual and 
methodological issues in MRI neuroimaging and fluid biomarkers,(81) and of differential neuroimaging(82) and 
neuropsychological assessment.(83)  Drs. Unverzagt is expert in trials of cognitive training, Dr. Clark has 
experience operating large nutrition and health programs in FQHCs, and Dr. Tangney is a leader in clinical 
nutrition and cognitive health, including the MIND diet.(10, 13) The team is supported by infrastructures of the 
IU Neuroscience Center, the IU Center for Aging Research, and the Eskenazi Office of Applied Research. 
Below we present preliminary data specific to this proposed project.  Included here are our efforts at: 1) 
investigating effects of speed training, 2) nutrition and health promotion in FQHCs, and 4) completing blood 
draws, and structural and functional brain imaging in older adults.   
Speed of processing training and 10yr incident dementia (Edwards, Xu, Clark, Unverzagt, under review). Using 
data from the Advanced Cognitive Training in Vital Elderly (ACTIVE; NCT00298558) study we examined the 
effects of cognitive training versus a control condition on 10yr incident dementia.  ACTIVE was a multi-site, 
randomized, controlled clinical trial.(22, 84) Community-dwelling adults aged 65 years and older were eligible.  
Persons were excluded if they had significant cognitive dysfunction (score < 23 on the MMSE;(85) functional 
impairment; self-reported diagnoses of Alzheimer disease, stroke within the last 12 months, or certain cancers; 
current chemotherapy or radiation therapy; or poor vision, hearing, or communicative ability).  Enrollment 
resulted in a sample of 2,802 individuals (average age 74 years, average education 13 years, 74% white and 
26% African American, and 76% women).  Eligible participants were randomly assigned to one of three 
treatment arms (Memory, Reasoning, or computer-based Speed training) or a no-contact control group.  
Interventions were conducted in small groups in ten, 60-75 minute sessions over 5 to 6 weeks.  Screening and 
baseline assessment took place before randomization.  Outcome assessments were conducted immediately 
following and 1, 2, 3, 5, and 10 years post-training.  For our analyses, incident dementia was defined using a 
combination of interview- and performance-based methods. Weibull regression examined intent-to-treat and 
dose models to predict subsequent dementia incidence, adjusting for risk factors.  Intent-to-treat models 
indicated that participants randomized to speed of processing training were significantly less likely than 
controls to develop dementia over 10 years (HR=0.67, 95%CI 0.49-0.91, p=.012), but this association became 
nonsignificant after adjustment for sample characteristics significantly associated with dementia, including age, 
sex, race, mental status, physical function, depressive symptoms and diabetes (HR=0.73, 95%CI 0.54-1.00, 

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p=.052). Analyses indicated greater doses of speed of processing (HR=0.91, 95%CI 0.86-0.97, p=.002) were 
associated with reduced rates of incident dementia relative to controls, and effects remained significant after 
adjustments for sample characteristics (HR=0.92, 95%CI=0.87-0.98, p=.013).   
 
Older Adults with Low Education Experience Large Effect of Speed of Processing Training (Clark, Xu, 
Unverzagt, Hendrie, 2016). In other analyses of the ACTIVE 
trial data, we investigated response to training in participants 
with less than 12 years of education (i.e., no upper level 
secondary education).(1) We assessed training effect sizes, 
defined as the net training effect (change from baseline for 
intervention – change from baseline for control) divided by the 
intra-subject standard deviation.  Baseline covariates included 
in the models to obtain adjusted training effects were age, 
female sex, minority race, married, body-mass index, current 
smoker, alcohol use, Short-Form 36 physical functioning, 
CES-D score, hypertension, type 2 diabetes, stroke, 
congestive heart failure, ischemic heart disease, high 
cholesterol, myocardial infarction, baseline MMSE score, 
visual acuity, and field site.  Sensitivity analyses were 
conducted to determine the longevity of differential training 
gains (2, 3, 5, and 10 years post-training).  We found similar 
response to memory and reasoning training for those with low 
vs high education but response to speed of processing training 
was significantly greater in the participants with low education.  
As shown in the figure, the effect size on the Useful Field of View speed of processing outcome was near 2.0 
in those with low education.  The training advantage for the less educated persisted to 3 years. 
 
Nutrition Education and Counseling for Obese FQHC Patients (Healthy Me); Fairbanks Foundation, Clark - PI.  
Healthy Me was a foundation-supported quality improvement program that is now integrated and supported by 
Eskenazi Health.  The Healthy Me program is structured around the 5A’s of behavior change model.(86)  
Enrollment in Healthy Me starts with an FQHC visit.  Five Coaches certified in behavior change counseling and 
fitness instruction are present two or more days per week in each FQHC.  Coaches meet in clinic with referred 
patients to assess their current weight-related behavior, listen to the patient’s concerns and goals, and 
collaboratively set an action plan that may include portion control, food preparation, exercise, or nutrition 
education classes held in the FQHC meeting rooms.  Each patient’s action plan is entered and tracked in the 
Healthy Me portion of Eskenazi’s EMR.  Dietary and physical activity self-monitoring instruction and logs are 
provided and a “passport to wellness” incentive program gives participants points for participation that earn 
them incentives (e.g., t-shirt, produce coupons, gym trial).  Healthy Me does not endorse a particular diet 
strategy or weight loss goal.  To date, Healthy Me has had 145,000 visits by over 25,000 adults.  Healthy Me 
recently won the Hulman Health Achievement Award from the Indiana Public Health Association.   
 
Nutrition and Exercise RCT; NIDDK R01 DK092377, Clark - PI.  Using an image-based nutrition education 
program that we developed for lower literacy participants, we are testing video-conference delivery of nutrition 
education and supervised exercise for FQHC patients in a randomized trial funded by NIDDK.  FQHC adults 
are recruited and randomized to usual care (Healthy Me), in person nutrition and exercise classes, or Internet-
delivered nutrition and exercise classes.  The purpose of the trial is to test whether eliminating travel and 
location barriers for urban poor adults can improve weight management outcomes.  This is an ongoing trial 
with no outcomes yet to report.  Participants for this trial are recruited from FQHC lists of obese adults via 
telephone.  The total sample size is to be 150.  Eight-three percent have completed the 6-month follow-up (3 
withdrew from the study and 7 could not be located).  Importantly, this trial includes comprehensive dietary 
assessments using the NCI 24hr dietary assessment tool.(87)   
  
Blood biomarkers (Exercise in Depression pilot). In a recent pilot, Dr. Clark obtained baseline and 3-month 
blood samples from six older adults with major depression disorder and transported and analyzed the blood.  
Blood draws were completed in the morning following a night of fasting.  Dr. Considine, who has decades of 

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experience storing and analyzing blood samples, computed C-reactive protein, interleukin-6, interleukin-1ra, 
tumor necrosis factor, vascular endothelial growth factor (VEGF), and brain-derived neurotrophic factor 
(BDNF).  In separate work, Drs. Saykin, Risacher, and colleagues analyzed the influence of genetic variation 
on plasma protein levels in older adults(78) and of APOE on Alzheimer’s biomarkers.(77)  
Indiana Memory and Aging Study (IMAS, R01 AG19771 - 
Saykin, PI).  Structural magnetic resonance imaging (MRI) 
measures of brain atrophy in IMAS(88) and ADNI(89, 90) have 
shown significant hippocampal and medial temporal lobe (MTL) 
atrophy in patients with MCI and particularly among those that 
convert to AD.(90)  Therefore, hippocampal atrophy is a key 
variable to examine in interventional studies.  In preliminary 
analyses we examined the impact of physical activity 
engagement in our IMAS cohort.  We determined that amnestic 
MCI (aMCI) participants who self-reported regular aerobic 
training had less hippocampal atrophy (Figure 4A; p = 0.021) 
and a trend for less frontal lobe atrophy (Figure 4B; p = 0.051) 
than MCI participants who did not report regular aerobic 
training.  Voxel-wise analysis supported the widespread nature 
of these differences (Figure 4C; voxel-wise threshold p<0.01 
(uncorrected for multiple comparisons) and minimum cluster 
size (k) = 50 voxels). In addition, older adults without MCI who 
engaged in regular cognitive activity engagement (daily reading 
and/or puzzles) showed significantly slower annual rate of hippocampal atrophy than those who did not (data 
not shown; p=0.009).  These results are consistent with hippocampal and frontal morphology being sensitive 
biomarkers for training-induced neuroplasticity.  Functional MRI (fMRI) of brain activation is also a sensitive 
biomarker for cognitive decline (see Risacher & Saykin, 2013(81) for review) and to intervention-induced 
changes.  We have reported improved brain activation in aMCI patients after treatment with donepezil.(81, 91)  
Preliminary examination in IMAS participants with aMCI indicated an impact of self-reported training on brain 
activation during a working memory task (Visual-Verbal 2-Back task [see MRI protocols below]).  Greater 
activation was detected in task-specific regions in aMCI patients who self-reported regular training relative to 
those who did not (Figure 4D; voxel-wise threshold p<0.01 and k = 50 voxels).   
PROPOSED METHODS:  
Participants.  The target population is adults aged 60 years or over born in the United States who self-report  
12 or fewer years of lifetime formal educational attainment.   
Inclusion criteria:  
• 
age 60 years or older,  
• 
≤ 12 years of education,  
• 
English speaking,  
• 
Marion County (or immediately surrounding county) resident with steady/fixed residence to receive food 
deliveries,  
• 
natural-born US citizen.   
• 
experience with smart phone and/or tablet [Applicable during COVID-19 restrictions only] 
 
Exclusion criteria:  
• 
living in nursing home 
• 
self-reported diagnosis of dementia, Alzheimer’s disease (AD), cancer with short life expectancy, 
multiple sclerosis, epilepsy, schizophrenia, bipolar disorder, Parkinson disease; current chemotherapy 
or radiation therapy; history of brain tumor, brain surgery, brain infection; stroke or myocardial infarction 
within the past 12 months;  
• 
current alcohol consumption ≥8 drinks per week for women or ≥15 drinks per week for men;  
• 
poor vision (self-reported difficulty reading a newspaper) or color blind;  

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• 
low communicative ability, functional status, or other disorders (examiner rated) that would interfere 
with interventions and assessments;  
• 
prior involvement in similar cognitive training studies;  
• 
unable or unwilling to provide blood sample at Baseline* [Applicable during normal study operations 
(non-COVID-19 restrictions) only] 
• 
unable or unwilling to provide urine sample at Baseline* [Applicable during normal study operations 
(non-COVID-19 restrictions) only] 
• 
tumor, hemorrhage, aneurysm, hydrocephalus, or other significant clinical finding from Baseline brain 
MRI [Applicable during normal study operations (non-COVID-19 restrictions) only] 
 
*Additional opportunities will be given to collect blood and urine if the first attempt is unsuccessful. When 
necessary, a urine collection cup will be left with the subject to collect a sample when they are ready to 
void. If Baseline blood or urine cannot be obtained prior to randomization, subject will be deactivated from 
the study. 
 
Participants meeting MINDSpeed eligibility criteria are also eligible for Viromeaging sub-study. [Enrollment 
closed 5/31/22 for this sub-study.] 
 
Recruitment and Randomization.  Recruitment will be primarily through Eskenazi Health.  Eskenazi is one of 
the five largest safety-net health systems in the United States and actively provides care to approximately 
20,000 adults age 60 and over.  Health system data managers or staff with access to EMR may identify 
patients who meet study demographic and clinical criteria.  Providers will give eligibility review and permission 
to contact potential participants.  Potentially eligible participants will be approached at an Eskenazi visit or 
called by telephone by a research assistant.  [During the COVID-19 health crisis, all recruitment will take place 
via telephone.] 
 
Recruitment through self-referral will also be accepted. Any potentially eligible participant who indicates 
interest will complete a brief screener including low education eligibility followed by information about the study 
interventions and assessments, including explanations of blood draw and imaging procedures. [During the 
COVID-19 health crisis, biospecimen collection and imaging will cease.] 
 
Those who remain interested and eligible will schedule an appointment to 
complete informed consent and baseline assessment including fluid biomarker 
samples and optional imaging; consenting participants will be presented with the 
option to participate in the Viromeaging sub-study at baseline assessment as 
well [Enrollment closed 5/31/22 for this sub-study.]. Study information sheets 
and/or appointment letters will be mailed to participants as requested prior to the 
appointment. [During the COVID-19 health crisis, verbal consent will be 
obtained by phone, and Viromeaging substudy enrollment will cease.]  
Assessments will be completed primarily at the IU Health Neuroscience Center by trained assessors, but can 
be broken down into multiple shorter visits or completed in part by phone, if desired.  [During the COVID-19 
health crisis, all study visits will be completed by phone or Zoom.] Participants completing all baseline 
assessments will be randomized.  Randomization will be stratified by age (60-69; 70 and over) and MRI 
completion.  To address frequent moves and service changes in the population, we will obtain contact 
information for 2 relatives/friends at enrollment.   
 
[After the IU Research Restart beginning June 1 2020, study visits will be completed by a combination 
of in-person and telephone measures until full in-person measures are allowed to resume. 
Biospecimen collection, imaging, and Viromeaging substudy enrollment will be included; written 
consent will be required.] 
Interventions.  As noted, a professional UX design team led a user-centered design research effort to 
integrate the speed of processing and MIND food interventions.  First, five users evaluated application screens 
and functions rendered on Sketch (https://www.sketchapp.com/).  Second, these same users experienced 

Page 10 
tablet-operated, interactive prototypes created through an application called Axure (http://www.axure.com/).  
Third, the design was evaluated by an outside expert team including human factors and software engineers 
and design researchers.  Finally, the MindSpeed app was evaluated in a different group of five users for one 
week (ages 52-76yrs).  The pilot study involved a combination of interview sessions, iterative design and 
usability evaluation of the prototype, food delivery and reporting, and game play (BrainHQ).  Some older adults 
with low education have limited reading skills, low computer familiarity, and limited eyesight.  Thus, a major 
objective of the design work was to make the application easy for the target group to use.  This appears to 
have been achieved.  First time users reported "this is self-explanatory" and "I will know how to use after first 
time, pretty simple."  Findings from the pilot indicate that participants will shop through the app, accept home 
delivered foods, play speed training games, and track incentives.  
Speed of processing training is provided by the Internet-based BrainHQ program from Posit Science, Inc., 
which we are using in older adults with MCI in our ongoing NIH-funded CARB trial (R01 AG045157).  BrainHQ 
contains an enhanced version of the original ACTIVE trial speed of processing module.  Five different speed 
training modules (Hawk Eye, Visual Sweeps, Fine Tuning, Eye for Detail, Sound Sweeps) tap time-order 
judgment, visual discrimination, spatial-match, forward-span, instruction-following, and memory.(93)  The Posit 
program systematically reduces stimulus presentation time to maintain an 85% accuracy rate and launches 
each new session based on the stopping point of the preceding session.  Participants receive performance 
ratings relative to their prior trial upon completion of each “exercise.”  Participants will be rewarded for their 
weekly game play. The goal is 75 minutes per week, but subjects will be compensated as such: $5 for 
completing at least 30 minutes of weekly game play, or $10 for at least 60 minutes of weekly game play. The 
cumulative incentive amount is displayed at the top of the home screen in the app (see image on Response to 
Reviewer’s page).  Through Posit Science research platform, we have real-time, objective records of training 
time per module, per session, for each participant (see letter from Posit’s Dan Tinker). 
 
The logic of making MIND food “easy” was 
noted earlier.  Many FQHC patients 
experience monthly food insecurity and are 
on the supplemental nutrition assistance 
program (SNAP).  We know from decades of 
work that every food dollar is stretched in this 
population and that locating affordable MIND 
food is a serious challenge.  In fact, it has 
been demonstrated that the urban poor travel 
to many locations within a city to access lower 
priced food stores and “on-sale” items.(94) 
The frail in this population may even receive 
home-delivered meals.  Our app is designed 
to be consistent with this behavior, USDA 
efforts to make produce more accessible,(95) and the Older Americans Act Nutrition services program.   

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We refer to our MIND intervention as MIND foods rather than MIND diet to emphasize that participants 
randomized to this arm will shop for and receive MIND food through the app.  Participants will select foods 
from within the app (options can be modified based on reported food allergies).  Selected foods will be 
delivered to the participant’s place of residence or available for pick-up at Eskenazi Health locations once per 
week.  The food options within the app will be restricted to those created from the Phenol-Explorer 
database(96) and approved by Dr. Tangney.  Having ordered the week’s food, participants will be prompted 
each day of the week to report the type and amount of purchased food consumed.  An incentive of $1 for each 
daily report will be added to the cumulative incentive total.   
The Control interventions are designed to mimic the 
activities of the MIND and Speed interventions.  The food 
control participants will experience all of the features of 
the MIND food participants with the exception that the 
foods available to order will be limited to whole foods 
very low in polyphenols (e.g., corn, rice, potatoes, 
iceberg lettuce, pears, etc).  Speed control participants 
will experience the same app features as Speed 
participants but in place of speed training will play inert 
games such as tic-tac-toe.  All participants will be eligible 
for up to $17/wk of incentives. 
All participants will be asked to sign an iPad user 
agreement when they receive the iPad at the 
Randomization Visit. Intervention calendars and other 
“reminder” tools will be utilized to increase engagement 
with weekly game play and food reporting.  
Intervention Maintenance.  After the 12-week post-training assessment, incentives and food deliveries will 
cease but participants will have continued access to BrainHQ.  Those in the MIND or MINDSpeed arm will be 
encouraged to maintain MIND food consumption with utilization of the MINDSpeed Cookbook which details the 
foods and recipes used in the trial.  Maintenance was chosen over total cessation of the intervention because it 
holds potential to maximize treatment potency, provides information on the rates of self-managed adherence 
(speaks to reach and effectiveness of the interventions), and should enhance subject retention.  This phase of 
the study will provide information on the degree to which the trainings were incorporated into subjects’ lifestyle 
in a sustainable manner.  We will use intent-to-treat principles when analyzing the 6 month follow-up data.  The 
adherence information from this phase of the study will be used in exploratory analyses.  Subjects in the Food 
Control arms will receive the MINDSpeed Cookbook at the end of study participation.  
Strategies for Treatment and Measurement Fidelity.  Treatment fidelity and monitoring as defined by the 
NIH Behavior Change Consortium Strategies(97) and checklist(98) is enhanced by app-delivery and daily food 
and objective game/video data.  A help call button on the app allows the participant to text research staff or 
request a call back (8 to 5, M-F).  Staff will have access to the participant’s treatment assignments.  The staff 
on call will conduct supervised practice with the BrainHQ programs, and MIND food and food control 
interventions with access to our UX designer, Dr. Srinivas, clinical nutritionist, Christy Tangney, and training 
expert, Dr. Unverzagt and, if needed, Mr. Tinker. The staff will complete a 3-step certification process prior to 
working with enrolled subjects including:  written test (passing score of 80%), individual module check out, and 
proctored mock counseling sessions with older adults using structured performance rating forms to judge 
quality.  After certification, ongoing quality assurance checks will include observed counseling sessions once 
per month for first 1 month and every 3 months thereafter.  A similar program of training, certification, and 
ongoing quality will occur for the Assessor, who will be blinded to treatment. 
Safety.  We have outlined our protocols for identifying and responding to events and injuries in the Protection 
of Human Subjects section of the proposal (see Adequacy of Protection Against Risk).  We have described 
standard regulatory reporting procedures in the Data and Safety Monitoring Plan (DSMP) and the operation of 
our Data Safety and Monitoring Board (DSMB).  Cognitive training is relatively safe but anxiety and burden can 
occur and will be monitored.  MRI is a safe procedure for people who do not have metal implants or other 
contraindications. Study personnel will screen participants for these risk factors prior to enrollment.  Before 

Page 12 
entering the MRI environment, participants will be re-screened for MRI contraindications per IU standards.  
Physical confinement or noise in MRI can be uncomfortable.  All efforts are made to assure participant’s 
comfort.  Blood draws will be conducted by trained and certified staff.  Finally, a small number of participants 
may develop depression, dementia, or physical dysfunction during the study.  Should this occur, we will direct 
the subject to appropriate clinical care (i.e., PCP or specialty clinic as needed). We will also direct subjects to 
appropriate clinical care based on blood pressure and pulse values obtained during Baseline, Post-Training, or 
Follow-Up appointments in clinic. This is not a study procedure, but rather a process for addressing potential 
safety issues within the hospital environment. 
Measures.  All assessments will be completed by a trained, certified, and blinded Assessor; all data will be 
entered into Research Electronic Data Capture (REDCap).  We have decades of experience completing 
assessments with older adult participants.  
Unless otherwise noted, measures are taken at baseline (BL), immediate post-training (PT), and 6 month 
follow-up (FU).  BL, PT and FU assessments may be conducted at Eskenazi, IU Health Neuroscience Center, 
participant’s homes, or via telephone with abbreviated outcome measures as necessary. [During the COVID-
19 health crisis, all study visits will be completed by phone or Zoom, and biospecimen collection, imaging, and 
Viromeaging substudy enrollment will cease.] 
“Off-line” training is tracked via subject-report at PT and FU of all non-study training, either cognitive or 
physical.  Participants will indicate frequency and duration of leisure cognitive, physical, and formal educational 
activities.(99)   
Feasibility Outcome Measures.  We will track the number of potential participants approached, screened, and 
enrolled to gauge recruitment success using ≥ 50% of eligible participants enrolled as the criterion.  We will 
also monitor the safety, acceptability, and adherence of the app-delivered interventions and outcome 
measurements.  Safety will be assessed by comparing adverse event rates, both serious (i.e., hospitalizations, 
falls, episodes of angina) and non-serious (i.e., anxiety) across treatment groups.  The acceptability of the 
experimental and control interventions will be measured using self-report surveys assessing satisfaction (i.e., 1 
= very satisfied, 3 = neutral, 5 = very dissatisfied) with scores from 1-2 indicating acceptability.  Adherence will 
be documented by dates, episodes, and time spent in completing speed training modules and food reports.  
Maintenance will be determined from training data and diet scores from PT to FU.  Creation of RCT logistical 
structure will be measured as printed manuals for app delivery and support (experimental and control 
conditions) and Assessor certification and quality assurances.   
Cognitive Outcome Measures.  Consistent with the conceptual model, the primary outcome is a cognitive 
function composite.  Our executive cognitive composite (ECC) consists of verbal fluency (phonemic and 
semantic fluency),(103) Symbol Search from the WAIS-IV, (104) Trail Making Test (Parts A & B, sec to 
complete),(105) and Stroop Interference (number completed in 45 seconds on color-word trial)(106) for each 
participant at each occasion of measurement.  Raw scores for each test are standardized using the baseline 
mean and standard deviation.   A composite is then formed as an average of z-scores.  In addition, List 
Learning from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) will also be 
administered to assess new learning and memory. Cognitive outcome measures may be modified to 
accommodate illiterate participants, including but not limited to, removal of Stroop and COWA and modification 
of MMSE.  
MIND Diet Score.  At each assessment (BL, PT, and FU), we will complete an interviewer-administered food 
frequency questionnaire (FFQ).  At BL, study personnel will use the subject’s FFQ report generated by Viocare 
to guide a discussion on incorporating selected study foods into their diet during the 12 week intervention. 
In addition, we will use urine samples to obtain “biomarkers of compliance” as was done in the recently 
completed PREDIMED multisite RCT.(109)  We will use the “fast and simple” rapid Folin-Ciocalteu method to 
determine total polyphenols at BL and PT.(110)   
Participants may be asked to repeat the urine collection at any assessment if the first collected samples 
become compromised (i.e., not useable) in any way. 

Page 13 
Self-reported Outcomes.  Geriatric Depression Scale (GDS) will measure depression, a modified CSRQ-25 will 
measure cognition and engagement, and portions of the SF-36(112) will measure health status and/or QOL.  
IADL will be measured by self-report using portions of MDS-Home Care.(22)  These will be assessed at BL, 
PT, and FU.  
APOE Collection and Assay.  Genomic DNA will be extracted from blood samples using the DNeasy Blood & 
Tissue Kit (Qiagen, Inc., Valencia, CA) according to the manufacturer's protocol.  Approximately 50ng of 
genomic DNA will be used for amplification. APOE genotypes will be determined by restriction enzyme 
digestion of amplified DNA.(114) 
Treatment and Adherence Modifiers. Individual difference factors with potential to modify response to 
treatment and adherence will be measured at BL including:  age, race, gender, years of education, 
employment status and history, current exercise, APOE ε4 allele status, baseline cognitive status (via the 
MMSE), height & weight to compute body mass index, blood pressure & pulse, Charlson Comorbidity Index 
(collected/calculated by health system data manager via EMR;115), and reading ability (via Wide Range 
Achievement Test-IV Reading).   
Blood Biomarkers.  Fasting blood samples will be obtained at BL, PT, and FU in all subjects to explore the 
mechanisms of action.  The conceptual model holds that the cognitive benefit of combined training is achieved 
through modulation of physiologic process related to neural proliferation, oxidative balance, and metabolic 
function.  As a result, we will measure circulating levels of the pro-inflammatory cytokines IL-1α, IL-1β, IL-6, 
and TNF-α; the anti-inflammatory cytokines IL-1ra and IL-10; the acute-phase reactant CRP; VEGF and BDNF; 
and also metabolic processes reflected in insulin resistance and blood glucose. We will also measure plasma 
biomarkers for levels of amyloid-β as a predictor for amyloid-β deposition in the brain, which is the earliest 
pathological sign of Alzheimer’s disease. 
Oxidative stress will be determined from Thiobarbituric acid reactive substances (TBARS), a byproduct of lipid 
peroxidation.  We will obtain 3 six-ml purple top EDTA tubes of blood taken in the fasting state.  Blood samples 
will be centrifuged within 10 minutes of collection.  Plasma aliquots will be stored at -80°C until the time of 
assay.  Measures will be performed in the Analyte Lab (Considine Laboratory) at the Center for Diabetes and 
Metabolic Diseases at Indiana University (1P30DK097512 Mirmira R [PI]) using Human Quantikine ELISA Kits 
(R&D Systems, Minneapolis, MN). Both Drs. Clark and Considine are members of the Center, which provides 
analyte services at reduced rates.   
Participants may be asked to repeat the blood collection at any assessment if the first collected samples 
become compromised (i.e., not useable) in any way. 
Viromeaging procedures [only available to consented subjects through 5/31/22]. At Baseline only, we will 
obtain additional fasting blood in purple top EDTA tube(s) totaling 10 ml for participants consenting to this sub-
study. These samples will be delivered to Dr. Twigg’s lab for immunologic and inflammation assays. Dr. Twigg 
will send deidentified samples to The Jackson Laboratories for RNA and DNA Sequencing and virome 
analysis. Consented subjects will also complete measures of grip strength, repeated chair stand, and a 
modified CES-D (depression) questionnaire. 
MRI Acquisition Protocol. Participants agreeing to MRI will have two scans; one at BL and one at PT.  Scans 
will be read and interpreted by the IU Center for Neuroimaging team consisting of Dr. Saykin, Professor of 
Radiology, and his assistant, Dr. Shannon Risacher.  Clinically concerning findings will be communicated to 
the subject and their primary care provider by our study physician, Dr. Christopher Callahan.  In these cases, 
we will also send the patient’s physician a radiologist report.  Significant findings (i.e., tumor, hemorrhage, 
aneurysm, hydrocephalus, or other significant clinical finding) may lead to withdrawal of subject. The following 
scans will be performed on the same research-dedicated new generation Siemens Prisma 3T scanner with a 
64CH receiver-only head coil: 1) Sagittal survey 3-plane localizer; 2) ADNI T1-weighted MP-RAGE 3D 
anatomical volume; 3) ADNI High resolution T2-weighted hippocampal sequence; 4) ADNI Axial T2-weighted 
FLAIR sequence; 5) Diffusion tensor imaging (DTI); 6) Blood Oxygenation Level Dependent (BOLD) functional 
MRI (scene encoding); and 7) A GRE Field map. The first four sequences will be collected following ADNI 
protocols (http://adni.loni.usc.edu/methods/documents/mri-protocols/). For DTI, a single-shot spin-echo echo-
planar imaging (EPI) multi-band 3 sequence is used with standard parameters, 69 slices, 2mm3 voxels, 60 

Page 14 
diffusion directions, 3 b0 images, and b-value: 1000 s/mm2; parallel imaging with iPAT=2 will be used to reduce 
susceptibility artifact (116). The BOLD fMRI task protocol is a 2D T2*-weighted multiband EPI with 39 axial 
contiguous slices, 3.0x3.0x3.5 mm3 voxels, TR/TE=2250/29ms, parallel imaging with iPAT=2 and 32 reference 
lines to reduce susceptibility artifact (116), and 3D prospective motion correction algorithm to detect head 
motion (123). The GRE field map is obtained at the beginning of the functional MRI scans to correct for 
distortion due to field inhomogeneity with two TEs (3.98ms, 6.44ms).  Scene Encoding and Recognition, 
modified from Detre and colleagues(124-126) which elicits reliable bilateral MTL activation. Briefly, during the 
blocked-design encoding trial subjects encode complex scenes for later recognition. The control condition 
consists of repetition of a degraded version of one of the encoding stimuli.  During the event-related recognition 
trial the subject views target scenes intermixed with foils and is asked to decide which were presented during 
the encoding trial. Approximate scan time is 30 minutes. 
MRI Analysis Methods. Scans will be processed using standard techniques and normalized to standard space 
for analysis (i.e. Montreal Neurologic Institute, MNI). Structural MRI scans will be processed using voxel-based 
morphometry (VBM) in Structural Parametric Mapping (SPM8 or SPM12 which we are testing now) and 
FreeSurfer as previously described.(88-90, 127) Each DTI image volume will be motion- and eddy current-
corrected prior to preprocessing. The diffusion tensor, fractional anisotropy (FA), and mean diffusivity (MD) will 
be processed voxel-by-voxel. The directional measure will also be generated for white matter (WM) 
tractography and tract-specific ROI. Structural connectivity between gray matter regions with significant 
fMRI/rsfMRI signal will also be obtained. Task-related fMRI scans will be processed using methods described 
our prior work.(82, 91, 99) Briefly, contrast images of task-related activation relative to control conditions will be 
generated using pre-processing techniques implemented in SPM8.(82, 91) These contrast images will be used 
for further analysis. (99) Briefly, pre-processed RS-fcMRI scans will be temporally concatenated and analyzed 
using independent component analysis (ICA) to generate whole group-level components using FSL.(128) 
Reconstructed participant-specific component maps will be calculated using dual regression (129-134) with the 
20 group components as templates. The resulting maps for each component of interest (i.e., DMN, and other 
cognitive networks), will be further analyzed.  ASL images will be corrected for motion, pair-wise subtracted 
between label and control images and averaged to generate a mean difference image. Quantitative CBF maps 
will be calculated using a single compartment model with in vivo measurement of blood T1 and labeling 
efficiency.(121, 122)  CBF maps will be co-registered with MPRAGE, which will be segmented into gray (GM), 
white matter (WM), and CSF compartments and normalized to the MNI template. CBF in standard regions of 
interest (ROIs) will be extracted for further statistical analysis to test study hypotheses. 
VBM grey matter density (GMD) images, DTI FA and MD images, task-related contrast images, RS-fcMRI 
component maps, and ASL CBF images will be compared between those receiving the combined intervention, 
a single intervention, and control cross-sectionally and longitudinally on voxel-wise and ROI levels. Cross-
sectional voxel-wise analyses will compare the scans between groups at BL, PT, and FU. Longitudinal voxel-
wise analysis will model time-by-group interactions using a two-way Analysis of Covariance (ANCOVA) model. 
All voxel-wise analyses will be performed in SPM8 (or SPM12). For regional analysis, we will extract a mean 
value for ROIs from the final stage preprocessed scans. Specifically, cross-sectional and longitudinal change in 
regional brain atrophy (i.e., MTL, frontal lobe), WM integrity (i.e., FA, MD from MTL WM tracts), task-related 
fMRI activation (i.e., activation from task-specific regions), resting-state connectivity (e.g., DMN connectivity 
from medial parietal lobe and MTL, etc.), and cerebral perfusion (i.e., whole brain, frontal lobe, etc.) will be 
compared between groups using values extracted from hypothesized target ROIs (see Analytic Plan). 
Cost. We will track all personnel, equipment, technology, and support costs associated with delivering the 
interventions thus allowing an estimation of intervention costs on a per subject basis.   
Power and Sample Size.  The primary analysis of the study focuses on the evaluation of the effectiveness of 
the interventions on the cognitive outcome, ECC.  We therefore determined the sample size to ensure 
adequate power for this analysis.  In these prior studies, speed of processing training was found to have an 
effect size (ES) of 1.4 standard deviation (SD) at immediate post-training (PT) and 1.2 SD at one year PT on 
processing speed outcomes.(22)  MIND diet is a brain health optimized hybrid of the Mediterranean-DASH 
diets, and the latter were found to have an effect size of 0.44 to 0.56 SD on executive function and speed of 
processing(136). There are no published trials of speed of processing training combined with MIND diet but we 
expect a large effect size of the combined intervention.  With an overall alpha level of 0.05 for six Bonferroni-

Page 15 
adjusted two-sided comparisons, a conservative estimate of the correlation of 0.7 between baseline and follow-
up based on prior studies, and 15% attrition rate (in our recent GRACE trial of 951 older adults—62% had < 
12yrs of education—91% completed the 6-month follow-up assessment)(73), the sample size of up to 50 
participants per treatment arm will yield 80% power to detect an effect size of >0.5 SD based on the 
constrained longitudinal data analysis (cLDA).(137)  The cLDA (138) has been shown to produce greater 
efficiency and handle missing data more flexibly with valid inference when data are missing at random.(137, 
139).  Cognitive aging studies support an effect size of over one-half standard deviation as clinically 
meaningful.(140-142)  
Exploratory Aims and Power: Outcomes include inflammation, metabolic function, oxidative stress, and 
hippocampal structure and function (via MRI).  For non-imaging outcomes, we will have a full sample and 
adequate power to detect effect sizes of 0.6.  Imaging creates anxiety and discomfort for some, however.  
Thus, in addition to using information and communication strategies shown to reduce imaging anxiety(143) we 
will complete imaging on the minimum necessary.  From the literature, we anticipate large effect sizes on the 
brain outcomes of hippocampal structure and function.  Cognitive training has resulted in changes in brain 
structure with approximate effect size of 0.56 in left hippocampus volume following spatial navigation training 
among healthy younger and older men(144) and an approximate effect size of 0.47 in gray matter density 
(GMD) after the completion of a multidomain computerized cognitive training among healthy older adults.(20)  
In addition, our preliminary results showed an effect size of 0.5 in annual percent change in hippocampal 
volume and 0.8 in annual percent change in hippocampal GMD among older adults without MCI who are 
frequently engaged in reading and puzzle-solving compared to those who are not.  Food interventions alone 
have had an effect size of 0.7 on hippocampal volume.(9, 56)  Other studies support effect sizes of 0.64 to 
above 1.00 on functional connectivity.(145, 146)  A one-half subsample of each arm (n=22 per arm) with an 
alpha level of 0.05 and a correlation of 0.7 between baseline and post-training provides 80% power to detect 
an effect size of 0.7 or larger for the brain imaging outcome. 
In order to test the mediating effect of blood biomarkers, brain structure, and brain function variables we need 
to establish the association between the mediating variable and primary outcome, after controlling for the 
intervention effect. Such an association is evaluated using a partial correlation.  With our sample size, we have 
more than 80% power at the 0.05 alpha level to test a partial correlation of 0.3 between mediating variables 
and the primary memory composite outcome.  Effect modifiers include adherence, percent body fat, age, 
baseline cognitive status, comorbidity, and APOE ε4 carrier status.  Evaluation of moderating effects is an 
exploratory aim and detection of such effects requires large sample sizes. Our study is therefore only able to 
explore treatment moderators. In addition, our sample size provides accuracy with no more than 8% margin of 
error at the 95% confidence level for feasibility outcomes.       
Analytic Plan:   Preliminary Analyses.  Baseline characteristics of participants will be compared across 
treatment arms.  We will also compare the baseline scores on outcome measures across treatment arms.  For 
continuous variables, means and standard deviations will be reported.  Frequency tables will be reported for 
categorical variables.  Normality of continuous outcomes will be examined using a normal probability plot.  For 
highly skewed measurements, appropriate transformations will be applied to achieve approximate normality.  
Repeated measurements over time will be graphed as spaghetti plots and examined for outliers.  All data will 
be examined for completeness.  The baseline characteristics will be compared across treatment arms and 
between participants who do and do not complete the study.  If any significant differences exist, we will control 
for differences in the subsequent analyses.  Analysis will be performed under the intention-to-treat paradigm. 
The primary analysis of the intervention effect on ECC will be performed using the constrained longitudinal 
data analysis (cLDA) proposed by Liang and Zeger.(138) Repeatedly measured ECC outcome at BL, PT, and 
FU will be modeled as dependent variables with the baseline mean responses constrained to be the same 
across intervention arms due to randomization. This model is flexible in handling missing data and has been 
shown to provide greater power than other models when testing intervention effects and yields valid results 
when data are missing at random. The clustering effect of repeated measures within a subject will be 
accommodated using a random subject effect. Time of measurement (BL, PT, FU) and its interaction with 
intervention arm will be included in the model. The time of measurement is considered as a categorical 
variable to allow non-linear time effect. Intervention effects are captured by the significant interaction effect. 
Comparison of outcome measures across intervention arms at PT and FU will be estimated based on the 

Page 16 
model using appropriate contrasts.  In order to assess whether the intervention effect is affected by physical 
activity change, weight change, or other factor, we will examine change patterns across intervention arms. If 
the change pattern differs across intervention arms, that factor will be controlled by including its main effect 
and interaction with the intervention arm in the primary analysis for intervention effect on memory. 
Exploratory Analyses. Feasibility hypotheses are 80% or greater intervention adherence and acceptability, and 
no serious adverse events.  These will be estimated using the 95% confidence intervals. To compare safety, 
we will estimate the difference in rates of adverse events across the intervention arms using 95% confidence 
intervals. Hypotheses of improved self-reported outcomes including depression, cognition and engagement, 
quality of life, and IADL will be examined using the cLDA methods described immediately above.   
Mechanism Analyses. Mechanistic hypotheses are that, relative to control, intervention arm participants will 
have greater hippocampal volume, enhanced DMN and CEN connectivity at PT, better metabolic function, 
higher levels of neurotrophic growth factors, and lower levels of inflammation and oxidative stress.  
Hypotheses will be analyzed using cLDA.  With regard to MRI, scan analysis described immediately above will 
be performed on targeted pre-specified ROIs and followed by spatially unbiased whole brain voxel-based 
analyses.  Supplemental analyses will compare across scan modalities to address the relative incremental 
contribution of each modality (e.g. hippocampal volume) to capturing the overall neuroimaging outcome. 
Moderator and Mediator Analyses. Analyses will be performed to determine the role of adherence, years of 
education, age, baseline cognitive scores, comorbidity, and APOE ε4 carrier status (an indicator of elevated 
ADRD risk) in moderating intervention outcomes using cLDA.  We hypothesize in particular that participants 
with greater adherence, more education, and lower comorbidity will experience larger ECC score gains.  A 
three-way interaction between potential moderators, intervention arm, and time of measurement will be 
included in the model and a significant three-way interaction indicates the moderating effect.  To evaluate 
hypothesized mediators of the speed training and MIND foods (e.g., inflammation, growth factors, oxidative 
stress, hippocampal volume), we will use the approach proposed by Krull and Mackinnon (2011) (147) that 
allows the testing of mediating effects in clustered data.  Using the change in ECC outcome from baseline as 
the dependent variable, a series of three mixed effects models, as outlined by Krull and Mackinnon, will be fit 
and regression coefficients from these models will be used to estimate the direct and indirect treatment effects.  
 
The cLDA provides valid inference when data are missing due to random dropouts. Sensitivity analyses will be 
performed to examine to what extent the results are affected when dropouts are not missing at random. 
Analyses based on complete cases, mean imputation, and worst case imputation will be performed.  In 
addition, a pattern mixture model will be used to allow different intervention effects for subjects with different 
missing data patterns.  Results based on these models will be compared to results from cLDA. 
Study Considerations.  A key feasibility challenge is treatment and assessment adherence. If adherence via 
the MINDSpeed app drops below 80%, we will institute, first, increased incentives and, second, telephone 
reminder and support calls.  Study retention is also a feasibility challenge.  With fair subject payments in past 
work, we have completed one-half day assessments at the IU Center for Neuroimaging.  We have a “step-
down” assessment battery if a participant is not able to tolerate the full outcome assessments.  The timeline 
shown below will allow us to accomplish the aims of our proposed MINDSpeed pilot trial, which includes time 
in Year 5 for development of a later stage AD prevention trial should feasibility and efficacy be supported.