A Novel Mobile Health Exercise Intervention in Aging: Brain Perfusion and Cognition
Phase: NA
Conditions: Physical Activity, Aging
Sponsor: University of California, San Diego

Study Title: A Novel Mobile Health Exercise Intervention in Aging: Brain Perfusion and Cognition 
NCT03058146 
Date protocol last approved by the IRB: 06/15/2022 
 
This protocol contains only the relevant portions of the IRB approved study protocol. 
 
OBJECTIVE 
Physical activity interventions with older adults can improve brain health; however, most 
interventions have been performed in gym-like settings that reach a small sector of the senior 
population. Since not everyone can access a gym, it is important to study whether brisk walking 
in real-world environments can also help brain health. This study will use mobile health devices 
to help older adults independently walk for brain health, thus representing a critical step 
towards the dissemination of physical activity intervention programs aimed at preserving 
cognitive function in healthy aging. 
 
SPECIFIC AIMS 
Aim 1: To perform a randomized controlled trial of walking (N=60) to investigate if the use of 
mobile health (mHealth) technology in real-world environments will help increase 
cardiorespiratory fitness, time spent in moderate to vigorous physical activity (MVPA), cerebral 
blood flow (CBF) and resting state brain connectivity, and cognitive function, and decrease 
sedentary time compared to a healthy aging education control condition in cognitively normal 
older adults aged 65-80 over a 3 month period. 
Hypothesis 1: Compared to the healthy aging education control condition (N=30), older 
adults assigned to the mHealth walking condition (N=30) will have greater increase in 
cardiorespiratory fitness, MVPA, regional CBF (hippocampal, frontal, anterior and 
posterior cingulate cortex, and inferior parietal lobule), cognitive function, and 
strengthened resting state connectivity, as well as greater decreases in sedentary time, 
relative to baseline. 
Aim 2: To investigate if changes in regional CBF (frontal and hippocampal) mediate the 
relationship between changes in MVPA/sedentary time and changes in cognitive function 
(executive functions and memory) for those assigned to the mHealth walking condition. 
 

Hypothesis 2: Changes in CBF will partially mediate the relationship between changes in 
MVPA/sedentary time and changes in cognitive functions within the mHealth walking 
condition. 
Exploratory Aim: To explore whether the effects of the mHealth walking intervention on the 
outcomes of interest differ as a function of genetic risk for Alzheimer’s disease (apolipoprotein E 
[APOE] ε4 carriers versus noncarriers). We believe that those at risk for AD (APOE ε4 carriers) will 
display greater increases in CBF compared to noncarriers as a function of the walking mHealth 
intervention.   
 
METHODS 
MEASURES  
Fitness Testing (30 minutes): Fitness will be measured before (baseline) and after the 
intervention (3 months) by trained personnel at UCSD EPARC. We will measure fitness using a 
Submaximal-Graded Exercise Test (GXT). The GXT will be conducted on a treadmill at UCSD 
EPARC. Heart rate will be plotted against the workload and its corresponding estimated VO2 
(American College of Sports Medicine, 2006), and maximal VO2 will be estimated plotting the 
slope of the line to estimated maximal heart rate (220-age). Although the research team 
recognizes that direct measurement of VO2max is considered the criterion measure of 
cardiorespiratory fitness, a sub maximal exercise test with estimations of oxygen consumption is 
appropriate for this study for several reasons. The proposed intervention is designed to promote 
regular, moderate-intensity physical activity, which may have minimal effect on maximal aerobic 
power, but significant effects on submaximal endurance capacity. Thus, we would expect to see 
an increase in total treadmill time and a decrease in heart rate at a given submaximal workload. 
By eliminating the need to wear a mask to directly gather metabolic data, the degree of subject 
burden, and the investigators’ effort to avoid stressful procedures relevant to the exercise 
experience, are minimized. Both of these are important considerations for this older, sedentary 
population. 
Four outcome measures will be determined from this test: 1) Total time to 85% of estimated 
maximal heart rate (with the exception of participants taking beta blockers—see below); 2) heart 
rate corresponding to a moderate (i.e. 3+ METS) workload; 3) heart rate recovery (HRR) 
measured at 1, 2, and 3 minutes of recovery; and 4) rate-pressure product (RPP), which is the 
product of heart rate and systolic blood pressure [SBP x HR] / 100 measured toward the end of 
each of the first three stages of exercise. RPP is an indirect assessment of cardiac work. A 
decrease in RPP for a given amount of aerobic work signifies an improvement in cardiac function. 
The heart rates measured during the final ten seconds of minutes seven (7) and/or nine (9) will 
be utilized to determine heart rate zones for exercise prescription during the intervention 
period. These measurement times occur during stage 3 (3.0 mph) at which participants are 

estimated to be working at a level equivalent to 3.3 METS; a threshold slightly higher than the 
minimum level to be considered “moderate” activity. Minutes three and five of that stage 
(minutes 7 and 9 of the total protocol) have been chosen because of the likelihood that the 
participant will have reached “steady state” after 3+ minutes at that workload. Specifically, 
steady state is the point at which the heart’s work (i.e. RPP) is equal to the workload, and is 
unlikely to change with increased exercise duration. For exercise prescription purposes, the two 
heart rate values gathered during stage 3 of the protocol (i.e. minutes 7 and 9 of the total 
protocol) will be averaged if the difference is three beats per minute (bpm) or less; and the 
greater of the two heart rates will be used if the difference is greater than three bpm. 
Quality assurance for this procedure is approached as follows: Conditions known to affect heart 
rate will be controlled to the best of our ability. Environmental conditions (room temperature; 
relative humidity) will be monitored and kept stable for repeated measures. Participants will be 
asked to refrain from caffeine or other stimulants on the day of their test. The treadmill will be 
calibrated monthly. Participants will not be asked to withhold beta blocker medications.  
Individuals who are taking beta-blockers will be asked to exercise to a workload corresponding to 
80% of their age predicted maximal heart rate. This precaution should account for the 
approximately 5% reduction in heart rate observed with beta blockade and will allow the 
research team to provide accurate/scaled exercise prescription. Furthermore, participants using 
beta blockers will also be coached on the Rating of Perceived Exertion (RPE) scale and will be 
counseled to exercise at a level corresponding to 5-7 (moderate exercise). The mHealth device to 
be used in the intervention, will also be used during fitness testing to compare heart rate 
accuracy. 
 
Physical activity (PA) assessment: Prior to participation, participants will be phone screened to 
evaluate inclusion criteria. Part of this initial evaluation includes a 1-week accelerometer 
monitoring period to ascertain sedentary status (< 10 minutes per day of moderate levels of 
physical activity as measured by accelerometry) and establish baseline levels of MVPA. 
Participants will be mailed research grade accelerometers with instructions (and will be coached 
via phone), which they will wear for 1 week while performing their everyday activities. 
Accelerometers will be brought back to the research group during visit 1 for analyses of the 
sedentary time data. Participants will also undergo cognitive and fitness testing during visit 1.  
During the RCT, the intervention and control conditions, participants will undergo 3, 1-week long 
PA measurement periods using accelerometers (baseline, mid-study, and post study). Triaxial 
accelerometers provide information regarding the frequency and intensity of PA and sedentary 
time and it is a small device that is worn comfortably on the hip, which has been validated for 
use with older adults. Accelerometers will allow us to determine if potential participants qualify 
as sedentary (< 10 minutes per day in moderate levels of PA) as well as to compare sedentary 
and physically active time between the intervention and control conditions at baseline, 6 weeks 
(mid), and 3 months (post). During each measurement period, participants will be asked to wear 

the accelerometer on their hip, for a minimum of 12 hours a day, for 1 week, while they perform 
their everyday activities. Accelerometers will not be worn during sleep or when there is a 
probability of getting the device wet (during showering, swimming, etc). Outcomes from this 
assessment include: daily average sedentary time and light, moderate, and vigorous physically 
active time. 
 
Cognitive Testing (~3 hours): All participants will undergo cognitive testing at baseline, mid, and 
post intervention assessments. To exclude individuals with dementia or mild cognitive 
impairment, those who do not meet established cutoffs on the Mattis Dementia Rating Scale and 
also show evidence of MCI (based on Jak et al. 2009 comprehensive criteria) on cognitive testing 
at baseline will be disqualified from further participation. The NIH Toolbox Cognition Battery will 
be implemented to measure 5 cognitive domains as the main cognitive outcomes: Memory 
(Picture Sequence), Executive Functions (Flanker Inhibitory Control & Dimensional Change Card 
Sort), Language (Picture Vocabulary & Oral Reading Recognition), Processing Speed (Pattern 
Comparison), Working Memory (List Sorting), and Attention (Flanker Inhibitory Control). Since 
Memory and Executive Functions are most sensitive to PA-related changes, we will supplement 
Executive Functions testing with the Delis Kaplan-Executive Function System: Trail Making, Color-
Word Interference, and Verbal Fluency Tests and Memory with the California Verbal Learning 
Test-II, the Wechsler Memory Scale (WMS-IV) Logical Memory Test, and a face recognition test 
developed by our research group. Individual test scores will be used to evaluate cognitive status 
for inclusion/exclusion criteria, and to create the composite scores for each of the 5 cognitive 
domains identified above by averaging their z-scores. Alternate forms of each test will be 
administered when available to avoid practice effects. Mental health symptoms will be assessed 
with the NIH Toolbox PROMISE measures available on REDCap.  
 
Buccal Swab for Genotyping (5 minutes): For participants whose genotype is unknown, Dr. Zlatar 
or a research assistant will collect necessary buccal swabs (saliva samples) using the Oragene 
DNA sample collection kit for PCF analysis of Apolipoprotein (APOE) genotype. PCR analysis for 
APOE genotyping. Buccal swabbing will be done by the project research technician from 
participants recruited from sources outside of the UCSD Alzheimer’s disease research center 
(ADRC). All samples will be stored in a -80° freezer located in a locked laboratory at the Stein 
Institute for Research on Aging. All samples will be labeled using the unique study identifier 
assigned to each patient, visit date, and visit number. They will be logged using this information 
and no other identifiers. The results of any genetic analysis will be kept confidential in a secured, 
locked location and used for research purposes only. No identifiable information is part of the 
information. Genotyping information will not be shared with research participants.  
 

Brain Imaging Assessment (60 minutes): Arterial Spin Labeling (ASL) MRI will be used to measure 
whole-brain resting CBF before and after the intervention. Analyses will focus on the following 
regions of interest (ROIs), which are sensitive to changes in PA: bilateral hippocampi 
(hippocampal ROI), inferior/middle frontal gyri and frontal pole (frontal ROI), anterior and 
posterior cingulate cortex (ACC/PCC ROI), and the angular and supramarginal gyri (inferior 
parietal lobule ROI). Right and left ROIs will be averaged to reduce the number of comparisons, 
with exception of the hippocampus. To acquire resting CBF, the MPPCASL sequence developed 
at the UCSD Keck Center (GE MR 750: 3 Tesla scanner) for fMRI will be utilized. It is optimal for 
white and gray matter perfusion measurement since it reduces some of the previous limitations 
and has improved tagging efficiency (which translates to higher temporal signal to noise ratio 
and well-controlled temporal bolus leading to robust CBF quantification) tagging duration=2 sec, 
TI=3.6 sec, TR=4.2 sec, TE=minimum, reps=64, FOV=22x22 cm, 20 5 mm axial slices with a single 
shot spiral acquisition, collecting 8 cycles where each cycle consists of 8 images acquired with 
unique phase offsets, acquisition time =4:46 minutes. The structural MRI sequence includes a 
high resolution T1-weighted Fast Spoiled Gradient Recall (3D FSPGR) scan: 172 1 mm contiguous 
sagittal slices, FOV=25 cm, TR=8 ms, TE=3.1 ms, flip angle=12, T1=600, 256 x 192 matrix, 
Bandwidth=31.25 kHZ, frequency direction= S-I, NEX=1, scan time=8 min 13 sec, as well as a T2-
weighted FLAIR sequence for assessment of white matter hyperintensities (to be used as a 
covariate) collected in the axial plane: TR=8650 msec, TE=136 msec, TI=2250 msec, FOV=24 cm, 
27 x 5mm slices, Bandwidth=31.25, 1.00 NEX, Acquisition time=4 min.  
 
 
DESIGN & PROCEDURES 
Randomized Controlled Trial (RCT): All potential participants will be pre-screened over the phone 
to determine if eligibility criteria are met (obtain physician’s clearance and assess MRI scanning 
eligibility and medical history which could exclude them from participation). They will then 
provide verbal feedback to undergo the physical activity assessment (1-week accelerometer 
measurement) to determine sedentary status and provide baseline physical activity levels. 
Participants will bring the accelerometers back to the team during session 1 where they will be 
consented and enrolled in the study. Following cognitive and fitness testing (session 1), eligible 
participants will be randomly assigned to one of two conditions (“mHealth walking” or “healthy 
aging education” control conditions). During session 1, if participants were unable to complete 
the fitness or cognitive protocols, did not meet the cognitive criteria, or if there was indication of 
high risk to perform fitness testing (based on cardiovascular risk assessment), participation will 
be discontinued. Since this intervention is individualized and takes place in their real-world 
environment, group sessions or synchronized start/end points are not needed (there is rolling 
recruitment and enrollment).  
 

 
 
A) MHEALTH WALKING CONDITION: Older adults randomized to this condition (N=30) will 
perform 3 months of prescribed brisk walking in their real-world environments, using mHealth 
during each exercise session, to objectively measure and help track activity, with the goal of 
increasing cardiorespiratory fitness and achieving/maintaining a minimum of 150 minutes of 
moderate to vigorous physical activity (MVPA) per week over time. The mHealth walking 
intervention condition will consist of the following active components: 1) an individualized and 
gradually increasing exercise prescription based on individual fitness testing; 2) study 
monitoring/compliance phone calls to provide motivation, track goal achievement, and 
assess/manage any barriers to adherence; 3) and the use of mHealth to objectively track 
adherence and measure PA behaviors as well as aid with maintaining MVPA by providing real-
time feedback about PA performance (heart rate target feedback). Each active component, all of 
which together comprise a novel and multifaceted PA intervention, are described in detail 
below: 
 
1. Exercise prescription: Based on their estimated maximum HR during fitness testing, each 
participant assigned to the mHealth walking condition will be prescribed a PA regimen targeting 
between 60-75% of their -estimated HR, which is equivalent to MVPA intensity (~3 METS). Since 
our participants will be sedentary at onset, the exercise prescription will gradually increase PA 
levels and HR targets by setting realistic goals in order to promote adherence and minimize risk 
until participants achieve at least 150 minutes of MVPA per week at 60-75% target HR. As such, 
prescribed walking times and target intensity will increase over time. Goals will be revised during 
monitoring/compliance calls (described below) according to participants’ performance and 
whether or not they achieved their prescribed goals for each stage. Goal revision will follow 
protocols successfully implemented by Dr. Kerr (Co-Investigator) and her group in previous 
community-based exercise interventions. Given the use of immediate targeted feedback from 
the mHealth device, it is expected that participants will be able to achieve and maintain 

prescribed levels of MVPA, since they will be able to immediately correct their walking pace 
when provided with a tactile vibration from the device. During each exercise session, the 
mHealth device will record participants’ heart rate which will be automatically saved to the 
smartphone application. Study staff will ask participants to log the data gathered by the app and 
share it with the study staff to provide targeted feedback during monitoring/compliance calls as 
well as to provide motivation and positive reinforcement when goals have been achieved.  
 
2. Monitoring/Compliance calls: Participants will receive monitoring/compliance calls from the 
research team which will vary in frequency throughout the length of the study. These calls are 
designed to measure participants’ progress, assess for barriers to exercise and problem-solve 
with participants, revise goals if needed, set new goals, provide positive feedback and motivation 
when goals have been reached, provide motivation when goals have not been reached, to 
implement incentives when goals are not being reached, and to review objective data gathered 
from the mHealth device directly with participants. These calls will also serve as an opportunity 
for participants to report any adverse events to the research team. Compliance calls, which are 
an active and important component of this novel intervention, will take place weekly during the 
first month and bi-weekly during months 2 and 3 with the goal of reducing contact with 
participants to a minimum to encourage sustained adherence and self-monitoring after contact 
with staff ends. Each phone call will last between 10 and 20 minutes. Participants in both groups 
will receive monitoring phone calls (to equate for attention from study staff). If participants 
cannot be reached by phone, follow-up emails will be sent to them. Similarly, weekly email 
surveys will be sent to obtain self-reported exercise data from the intervention participants and 
to ask questions about the readings for the control participants.  
 
3. Use of mHealth: Another active component of this novel PA intervention is the use of the Mio 
Fuse system to help track and objectively measure heart rate (HR) during the intervention. This 
device provides immediate tactile feedback to the user regarding their preferred heart rate zone 
to encourage the participant to remain in that specific zone as long as possible. A Polar HR band 
will also be used to track HR during fitness testing, which will be used for analyses of amount of 
time spent within the target heart rate zone. Previous intervention studies that have used 
mHealth devices have focused on providing the participants with personalized plans and exercise 
reminders, but they have not taken advantage of collecting objective data from these devices. 
Furthermore, the Mio Fuse will be individually programmed for each participant in accordance to 
their own prescribed HR target zones (based on estimated maximum heart rate from fitness 
testing) to alert them if they are deviating from their prescribed heart rate targets in real time. 
We believe this capability will aid participants in maintaining their prescribed HR target zones by 
adapting their behavior immediately. The Mio Fuse can be manually re-programmed in order to 
adjust HR goals according to the gradual nature of the intervention. Even though the use of 
mHealth is an important active component of the intervention, it has not been conceptualized as 

the sole agent of change in this study. Rather, all three components described in this section are 
expected to provide a propitious environment for increasing cardiorespiratory fitness and MVPA 
within this multifaceted framework. Information from the Mio Fuse application will be shared by 
the participants with the research staff on a weekly basis. If the Mio Fuse device poses significant 
difficulties, we will implement a similar, more user-friendly mHealth device to track heart rate 
activity.  
 
B) HEALTHY AGING EDUCATION CONTROL CONDITION: The education control condition (N=30) 
will provide participants with printed materials and homework assignments (quizzes) on issues 
related to successful aging, such as nutrition, social activity, cognitive and social engagement. 
This type of control condition was chosen rather than a no-contact condition in order to equate 
for attention from study staff. As such, participants in both conditions will undergo the same 
amount of contact time with staff members in person and during compliance phone calls. Topics 
will not cover PA to avoid changes in PA within the control group. Compliance phone calls for this 
group will focus on checking on homework assignments and briefly testing knowledge of the 
reading materials as a means to assess adherence to the control condition. Given that the 
education control condition will not be given a mHealth device to perform prescribed exercise, 
we will also conduct an accelerometer PA measurement period at three time points (baseline, 
mid, and post intervention) with both conditions. This measurement will allow us to assess 
whether individuals assigned to the control condition have incurred any changes in PA levels, 
above and beyond changes in cardiorespiratory fitness measured before and after the 
intervention.  Participants in this control condition will also receive monitoring phone calls at the 
same frequency as those in the mHealth condition to equate for attention from stud staff. 
Follow-up emails will be sent when no contact is achieved via phone and weekly surveys will be 
sent to assess knowledge of the reading materials.  
RCT Outcomes and Analyses 
 
Brain Image Processing: Neuroimaging data processing is performed with Analysis of Functional 
NeuroImages (AFNI), FMRIB Software Library (FSL, Oxford, United Kingdom), SPM5, FreeSurfer 
and locally created MatLab and R scripts.  
 
High Resolution Structural Scan: T1-weighted MRI is used for cortical and subcortical 
segmentation and parcellation to obtain volumetric data. Skull stripping of the high-resolution 
T1-weighted data image is performed using AFNI 3dSkullstrip. Scans are manually edited to 
remove residual non-brain material when necessary. Tissue segmentation is performed using 
FSL’s Automated Segmentation Tool (FAST) algorithm to define cerebrospinal fluid (CSF), gray 
matter (GM), and white matter (WM) regions. 

 
Whole Brain Resting Cerebral Blood Flow (CBF): For each subject, a mean arterial spin labeling 
(ASL) image is formed from the average difference of the control and tag images. This mean ASL 
image is then converted to absolute units of CBF (mL/100 g tissue/min) with use of the CSF 
image. To correct the CBF measures for partial volume effects, we use the method previously 
reported by Johnson and colleagues. These calculations assume that CSF has zero CBF and that 
CBF in GM is 2.5 times greater than that of WM. The following formula is used to compute 
partial volume corrected CBF signal intensities: CBFcorr = CBFuncorr/(GM + 0.4 * WM). CBFcorr 
and CBFuncorr are corrected and uncorrected CBF values, respectively. GM and WM are gray 
matter and white matter partial volume fractions, respectively. Information from the high 
resolution structural image and the FSL FAST tool are used to determine the tissue content of 
each perfusion voxel. To distinguish between GM and WM CBF, only voxels that survive masking 
after we apply the participant’s resampled probabilistic GM or WM mask created using FSL FAST 
are submitted to voxel-wise analysis. CBF images are then spatially normalized to MNI space to 
obtain regions of interest (ROIs) using the Harvard-Oxford cortical and subcortical brain atlases 
available in FSL. ROIs to be examined include: bilateral hippocampi, inferior/middle frontal 
gyri/frontal pole (prefrontal), anterior and posterior cingulate cortex (ACC/PCC), and the angular 
and supramarginal gyri (inferior parietal lobule) given their sensitivity to exercise-related 
changes. 
 
 
Data Analyses: Data will be screened for outliers, missing values, and normality of variable 
distribution. IBM SPSS, R statistical software, and MATLab, will be used to analyze the data. 
Preliminary analyses will be used to assess the influence of outliers and non-normally distributed 
variables for possible exclusion or data transformation. Linear mixed effects models, within an 
intent to treat framework, will be used to test specific hypotheses. Mixed effects models account 
for both fixed and random effects and are less susceptible to missing data. For outcomes with 2 
time points, mixed effects models are conceptually equivalent to a two-sample t-test on change 
scores, thus we will use mixed effects models for all outcomes to maintain a unified analytical 
framework. When multiple analyses are conducted for a given hypothesis, correction for family-
wise Type I error rates will be made using Bonferroni adjustments for the number of analyses 
being performed.  
 
Hypothesis 1: Compared to the healthy aging education control condition (N=30), older adults 
assigned to the mHealth walking condition (N=30) will have greater increase in fitness levels, 
MVPA, regional CBF (hippocampal, prefrontal, anterior and posterior cingulate, and inferior 
parietal lobule), and cognitive function, strengthened resting state connectivity, and greater 
decreases in sedentary time, relative to baseline, after 3 months. 

 
Analysis 1: For the MVPA/sedentary time and cognitive testing outcomes (for which 3 time 
points will be collected), we will investigate the longitudinal effect of intervention using linear 
mixed effects models. We will include fixed effects for condition (mHealth walking or healthy 
aging education control), time (0, 6 weeks, and 3 months, treated as a continuous variable), and 
their interaction (condition X time). To account for within subject correlations, we will include a 
random intercept over time term. Prior to analyses, trajectory plots for each of the outcome 
variables will be created as a function of time to check for linearity and to determine whether it 
is necessary to allow for individual rates of change (random slopes). This would allow us to 
investigate if there are different individual rates of cognitive change between the intervention 
and control conditions over time. Similarly, for fitness level and regional CBF, (for which 2 time 
points will be collected), we will investigate the longitudinal effect of intervention using linear 
mixed effects models by including fixed effects for condition (mHealth walking or healthy aging 
education control), time (0 and 3 months, treated as a continuous variable), and their interaction 
(condition X time). Inference will be focused on the condition X time interaction term for all 
analyses. 
 
Hypothesis 2: Changes in regional CBF in the hippocampus and prefrontal cortex will partially 
mediate the relationship between changes in MVPA/sedentary time and changes in cognitive 
functions (memory and executive functions) within the mHealth walking condition. 
 
Analysis 2: We will use the methods described by Preacher and Hayes to perform a mediation 
regression between the difference of MVPA/sedentary time and cognitive functions at baseline 
and 3 months, with the difference between CBF at baseline and 3 months acting as the 
mediator. 
 
Exploratory/Training aim: This aim is not powered to detect significance but rather it is purely 
formative in nature given the PI’s research trajectory and future interests. This training aim will 
explore if APOE genotype modified the relationship between changes in MVPA and CBF and 
changes in MVPA and cognition. For this purpose, two multiple regression models will be 
conducted. 1) Change in MVPA (post-pre from accelerometers) and changes in sedentary time 
(post-pre from accelerometers), APOE genotype (e4 carrier versus non-carrier), and the 
interaction term between MVPA/sedentary time change and APOE genotype will be entered as 
predictors of changes in CBF (post-pre) in those assigned to the mHealth walking intervention 
group only. 2) For the second regression model, the same predictors will be entered, but with 
changes in cognition as the outcome variable (post-pre). These analyses will allow us to detect 

effect sizes (beta coefficients) to power future studies to further examine the effects of APOE 
genotype on intervention outcomes. 
 
Power Analysis: Large effect sizes have been observed in the literature for CBF (Cohen’s d=1.34) 
and cognitive function (d=.75) following a 3 month-long PA intervention; however, this was a 
supervised intervention with a no-contact control group, thus we expect our effect sizes to be in 
the medium to large range. The proposed sample size of 30 participants per group will have 80% 
power to detect a medium to large effect size=.65 on CBF with a one-tailed test and α=.05 and 
80% power to detect an effect size=.75 on cognitive performance with α=.05.