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The association between self-reported physical activity and cognition in elderly clinical trial participants

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The association between self-reported physical activity and cognition in elderly clinical trial participants

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The Association between Self-Reported Physical Activity and
Cognition in Elderly Clinical Trial Participants

by

Yi Wan

A Thesis Presented to the
FACULTY OF KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BIOSTATISTICS

August 2015

Copyright 2015 Yi Wan
2

Table of Contents
Abstract ............................................................................................................................................3
Introduction ......................................................................................................................................5
Methods............................................................................................................................................7
Study Population ..........................................................................................................................7
Assessment of Physical Activity ..................................................................................................9
Assessment of Cognition ..............................................................................................................9
Assessment of Depression and Demographic Factors ...............................................................11
Statistical Analysis .....................................................................................................................12
Results ............................................................................................................................................15
Analysis sample ..........................................................................................................................15
Association of Global Cognition with Physical Activity ...........................................................16
Association of Executive Function with Physical Activity ........................................................23
Association of Verbal Memory with Physical Activity .............................................................29
Discussion ......................................................................................................................................36
References ......................................................................................................................................40

3

ABSTRACT
Objectives: To use data from three randomized clinical trials to determine whether physical
activity is associated with cognitive function in elderly healthy individuals after adjustment for
the effects of depression, age, gender, race, trial intervention, years of education and income.
Methods: In three randomized, double-blind, placebo-controlled trials, 1499 participants
including 309 men and 1190 postmenopausal women were assessed with measurements of
cognition and physical activity; assessment occurred prior to randomization (baseline
assessment) and at scheduled periods during each trial follow-up. Depressed mood (measured by
the Center for Epidemiologic Studies Depression Scale) and demographics were also measured.
The association between cognition and physical activity was statistically evaluated using
longitudinally collected data. The dependent cognitive variables were composite scores
assessing: global cognition, executive function and verbal memory. The association with
physical activity was evaluated with the dependent cognitive variable modeled as absolute level
(cross-sectional) using generalized estimating equations (GEE) and change (end point minus
baseline cognitive evaluation) measured over an average of 2.5 years. Associations were
adjusted for CES-D score, age, gender, race, intervention of trial, years of education, and
income.
Results: We found three statistically significant positive relationships between cognition and
physical activity (adjusted for CES-D score, age, gender, race, intervention of trial, years of
education, and income): (1) the 2.5-year change in the verbal memory composite score and
average number of weekly hours spent in moderate and higher physical activity (p=0.003), (2)
the weighted global cognitive score and the categorized number of hours of vigorous-intensity
physical activity (p=0.01), and (3) the 2.5-year change in the verbal memory composite score
4

and categorical number of hours in vigorous-intensity physical activity (p=0.002). In the
multivariate analysis, each of the three cognitive composite scores were strongly negatively
associated with age (P<0.0001). Gender, race and years of education were also significantly
related to the three cognitive composite scores (all P<0.05). CES-D score and annual income
level were not significantly related to any of the three cognitive composite scores, after
adjustment for other factors.
Conclusion: In this sample of healthy individuals with an average age of 60 years, 2.5-year
change in verbal memory was positively associated with both a continuous measure of weekly
hours in moderate and higher intensity physical activity and a categorical variable of number of
weekly hours in vigorous physical activity. The global cognitive test score (weighted summary
of all 14 cognitive tests) was also significantly positively associated with vigorous-intensity
physical activity compared to those who had no vigorous-intensity physical activity.
Keyword: cognition, physical activity, depression

5

INTRODUCTION
It is unquestionable that cognition is closely related to many aspects of human life. At the
individual level, cognition enables educational achievement
1
and occupational success—and has
far-reaching effects on quality of life.
2
It is therefore a key point of scholarly interest that
cognitive function declines as a normal consequence of the aging process,
3-4
because age-related
cognitive decline inevitably affects and alters the lives of all individuals as they approach
senescence. Aging is associated with decreases in mental efficiency and impairments in
memory—the primary cognitive complaints of older adults.
5
Age-related decline in cognitive
function often places a burden on individuals and their family members because of the
association between poor cognition and functional impairment in older people. More broadly,
age-related cognitive decline is of significant clinical and public health importance because of
the burdens placed on caregivers and the growing financial costs of providing adequate
healthcare and functional caretaking to rapidly aging populations.
Populations of the United States and other countries are experiencing a demographic transition
characterized by increasingly higher proportions of their populations aged 65 or older. In the
United States, an estimated 20% of the population will be 65 years or older by 2030—up from
4.1% in the early 20
th
century.
6
Similar transitions are noted in Europe,
7
China
8,9
, and many
other countries
9
. Aging of the population is determined almost entirely by two rates–fertility
rates and mortality rates
8
. Both of these rates have tended to decline with national economic
development, leading to the changing age structures and economic challenges that come with
them.
8

Population aging led to investigation into the best ways to respond to its associated challenges.
Concerns over the sustainability of health and welfare systems and expectations of a gradual
6

increase in retirement age have both spurred efforts to increase the chance that additional years
of life are spent in good health
7
and with the capacity to provide self-care
10
. To this end, the
promotion of physical activity has been identified as a possible way to maintain physical and
cognitive well-being into old age while lowering the risk of dementia, degenerative disease, and
other problematic conditions.
Cognition appears to play an important part in the well-being of the elderly, and declines in
cognition have been shown to be associated with a higher risk for functional limitations and
disability
11-13
. Cognitive decline can cause mild disability—referred to as mild cognitive
impairment (MCI)—even if a state of dementia is not reached. Effective interventions for
prevention or slowing of the rate of cognitive decline in older adults are therefore of great
interest to affected individuals, those at risk, and the broader public concerned with the health of
the elderly
14
.
A number of studies and meta-analytic reviews have reported beneficial effects of physical
activity and physical fitness on cognition in cognitively healthy older adults
15-17
. Physical
activity is also being increasingly recognized as a valuable strategy in reducing the risk of
depression and other mood disorders
18-19
. However, an appreciation of the linkage between
physical activity and cognition is complicated by the fact that cognitive function is also predicted
by various other factors.
19
Importantly, declines in cognition have been found to be associated
with mood disorders such as depression. More specifically, the presence of depression is
associated with subtle declines in verbal memory and executive function
20
. While previous
publications have attempted to disentangle the contribution of physical activity to cognition in
the elderly while considering associated factors such as socioeconomic status, gender, and race,
very few of these studies have sought to control for the beneficial effects of physical activity in
7

its alleviation of depression and other factors at the same time. This research, on the other hand,
assesses the entangled relationship of depression (CES-D score) as a confounder to the
documented relationship between physical activity and cognition. The main objective of this
research is to assess the cross-sectional and longitudinal associations of physical activity with
cognition adjusted for CES-D score, age, gender, race, intervention of trial, years of education,
and income by using data from BVAIT, WISH, and ELITE clinical trials.

METHODS
Study Population and Clinical Trial Descriptions
The B-Vitamin Atherosclerosis Intervention Trial (BVAIT) was a randomized, double-blind,
placebo-controlled trial conducted from November 6, 2000, to June 1, 2006. Participants were
men and postmenopausal women ≥40 years old with fasting total homocysteine (tHcy) ≥8.5
µmol/L and no clinical signs/symptoms of CVD. Eligible participants were randomly assigned to
daily supplementation with 5 mg folic acid + 0.4 mg vitamin B12 + 50 mg vitamin B6 or
matching placebo in one of 2 strata defined by baseline carotid artery intima media thickness
(CIMT; <0.75 mm, ≥0.75 mm). Participants were initially followed 2.5-year and treatment
period was extended on average 1–2 years by the External Data and Safety Monitoring Board
based on evolving results from secondary prevention B-vitamin trials.
21

The Women’s Isoflavone Soy Health (WISH) trial was a randomized, double-blind, placebo-
controlled trial conducted from April 12, 2004, to March 19, 2009. WISH assessed health
outcomes (atherosclerosis, osteoporosis, cognition, and breast density) in a healthy population of
8

postmenopausal women without pre-existing CVD. Participants were postmenopausal women
without vaginal bleeding >1 year and serum estradiol <20 pg/mL. Participants were randomly
assigned in a 1:1 ratio to daily 25 g soy protein containing 91 mg aglycon equivalents of
naturally occurring isoflavones and its glycosides (154 mg total isoflavone conjugates plus
aglycons): genistein 52 mg aglycon equivalents (88 mg total), daidzein 36 mg aglycon
equivalents (61 mg total), and glycitein 3 mg aglycon equivalents (5 mg total) or daily total milk
protein-matched placebo (0 isoflavones) within 2 strata of carotid artery intima-media thickness
(CIMT; <0.75 mm, ≥0.75 mm).
22
Participants were followed an average of 2.7-year.
The Early versus Late Intervention Trial with Estradiol (ELITE) trial targeted two groups of
women: women in early postmenopause and women in late postmenopause. Between July 13,
2005 and September 30, 2008, 643 women were recruited into early and late postmenopause
groups. The randomized interventions were oral 17β-estradiol (E2; 1 mg daily) or matched
placebo. Women with a uterus also received cyclic micronized progesterone (45 mg) as a 4%
vaginal gel or matched placebo (one application daily for 10 day per 30-day cycle). The ELITE
trial objective was to assess whether time since menopause (as represented by postmenopause
group) modifies the effect of E2 therapy on specified health outcomes, including atherosclerosis
and cognitive change (ELITE-Cog).
23
All participants were recruited from the general
population from the Greater Los Angeles area predominantly through media advertisement.
Recruitment was initially based on a 5-year trial with a planned treatment period of 2-5 years
depending on when the participant was randomized. The trial was extended with supplemental
funding for an additional 2.5 years of treatment.
The University of Southern California Institutional Review Board approved the study protocol
for all trials; all participants provided written informed consent.
9

Assessment of physical activity
In each trial, the Stanford Seven-Day Physical Activity Recall was completed at baseline and
every six months during trial follow-up.
24
This questionnaire is a self-report recall instrument
assessing physical activity during the previous week.
25
Participants were asked to report all
activities of moderate and vigorous activity, as well as the duration of each reported activity.
Each reported activity was coded for its metabolic equivalents of energy expenditure (METs)
using the coding scheme provided by Ainsworth et al.
26
For each week recalled, the energy
expenditure of each activity was calculated by multiplying the number of hours of participation
by the activity’s MET. Any self-reported activity that required three to six METs was considered
to be of moderate intensity, and any that required more than six METs was considered a
vigorous-intensity physical activity. Energy expenditure in sleep was calculated by multiplying
the sleep time by 1 MET. Time spent in light activity was computed by subtracting the total
reported time spent in sleep moderate activity and vigorous activity from the total weekly hours
of 168. The metabolic cost of light activity was computed by multiplying the number of hours
spent in light activities by 1.5 MET.
27
Total energy expenditure was the sum of energy
expenditure (MET-hours) in sleep and in doing light, moderate, and vigorous activities.

Assessment of cognition
The cognitive assessments were completed in an average of 2.5 years. A comprehensive
neuropsychological battery assessed a broad spectrum of cognitive skills, emphasizing
standardized tests sensitive to age-associated change in middle-aged and older adults. Cognitive
ability was estimated by neuropsychological tests and corresponding cognitive skills.
28
Cognitive
10

tests included the following: Symbol Digit Modalities Test
29
(complex scanning and visual
tracking, attention, psychomotor speed); Trail Making Test, Part B
30
(visuomotor tracking,
planning, cognitive flexibility, psychomotor speed); Shipley Institute of Living Scale,
Abstraction scale
31
(concept formation); Letter-Number Sequencing (working memory, attention,
concentration) and Block Design (visuospatial perception, planning, visuoconstructive ability)
subtests from the Wechsler Adult Intelligence Scale, 3
rd
edition;
32
Judgment of Line Orientation,
Form H
33
(visuospatial perception); animal naming
34
(category fluency, semantic memory);
Boston Naming Test, 30 item version
35
(naming, semantic memory); California Verbal Learning
Test, 2
nd
edition,
36
immediate (3 trials) and delayed (1 trial) recall (verbal episodic memory,
word list learning, concept formation); East Boston Memory Test,
37
immediate and delayed
recall (verbal episodic memory, logical memory); and Faces I (immediate recall) and II (delayed
recall) from the Wechsler Memory Scale 3
rd
edition
38
(visual episodic memory, memory for
faces, visuoperceptual processing). These tests were used to calculate three cognitive composite
scores, which were global cognition, verbal memory, and executive function. Each of the
composite scores was calculated as a sum of the standardized scores for contributing cognitive
tests, weighted by the inverse correlation matrix of the contributing test scores. All cognitive
tests contributed to the global cognitive composite score. The California Verbal Learning Test
36

and East Boston Memory Test37, including immediate and delayed recall scores, contributed to
the verbal memory composite score. The Symbol Digit Modalities
29
, the Trail Making Test, Part
B
30
, the Shipley Abstraction
31
, Letter-Number Sequencing, and Category Fluency tests
contributed to the executive function composite.
39
BVAIT trial and WISH trial had one follow-
up cognitive assessment at 2.5 years, while ELITE trial had two follow-up cognitive assessments
at 2.5 and 5 years. We only used the cognitive score at 2.5 years in the study for ELITE trial.
11

Assessment of depression and demographic factors
Depressed mood was assessed with the Center for Epidemiologic Studies Depression Scale
(CES-D). The CES-D is a 20-item scale for epidemiological research assessing the frequency of
depressive symptoms. Respondents are asked to choose from four possible responses in a Likert
format, where “0” is “rarely or none of the time (less than 1 day)”, and “3” is “almost or all of
the time (5-7 days).” Scores range from 0 to 60 with higher scores reflecting greater levels of
depressive symptoms. The CES-D has 4 separate factors: depressive affect, somatic symptoms,
positive affect, and interpersonal relations. The CES-D has very good internal consistency with
alphas of 0.85 for the general population and 0.90 for a psychiatric population.
40

Demographic factors including age, race (White Non-Hispanic/Black Non-
Hispanic/Hispanic/Asian or Pacific Islander/Native American/Other), education (8th grade or
less/Some high school/High school graduate/Trade or business school/Some college/Bachelor's
degree/Graduate or professional/Other) and income (Under $10,000/$10,000 to $19,999/$20,000
to $29,999/$30,000 to $39,999/$40,000 to $49,999 /$50,000 to $59,999/$60,000 to
$69,999/$70,000 to $79,999/$80,000 to $89,999/$90,000 to $99,999/$100,000 or higher) were
assessed by structured questionnaires completed at baseline. The ordinal variables (years of
study and income) resulted from categorical measures of variables that were conceptually
continuous. Based on the categories of education that participants reported, we assigned a
category-median years of study (8 years, 10 years, 12 years, 14 years, 16 years, and 20 years) to
each participant. We also assigned a midpoint value of reported income to each subject.

12

Statistical analysis
The analysis included 506 participants (with 885 baseline and follow-up cognitive testings) from
the BVAIT trial, 350 participants (580 baseline and follow-up cognitive testings) from the WISH
trial, and 643 participants (1140 baseline and follow-up cognitive testings) from the ELITE trial
who had data available for demographics, CES-D score, physical activity and cognitive data for
multiple visits. We computed a total CES-D score of 20 items (reverse scoring for items 4, 8, 12,
and 16) for each participant on each test date; missing items were replaced by the average value
of non-missing items. State here first WHAT physical activity variables you used in the analysis.
As a measure of routine physical activity, we used an average measure of each physical activity
variable for each participant (averaged over all of their study visits). Physical activity and
cognition were collected by the same participant on the same test date. Demographics were only
collected at baseline. The CES-D data collected closest to the date of (and at least within 6
months of) cognitive testing was merged with the cognitive data.
Because we analyzed associations between physical activity and cognitive performance collected
over multiple test dates (at trial baseline and follow-up) within participants, the method of
generalized estimating equations (GEE) was used to analyze these longitudinal correlated
response data using a linear model with a working independence variance structure.
41
As the
primary independent variable of interest, physical activity was modeled in two ways to assess the
qualitative and quantitative associations with cognition. Physical activity as a continuous
variable was modeled as the total hours of moderate and vigorous intensity physical activity in
one week. Because a large proportion of participants reported 0 hours of vigorous physical
activity, this variable was also modeled as a categorical variable in three levels. The lowest level
represented participants reporting no vigorous activity, whereas the upper two levels were
13

determined using a median split of weekly hours of vigorous activity (among participants
reporting at least one vigorous activity).
We evaluated possible confounders by first testing the relationship between physical activity and
potential confounders and the relationship between cognition and potential confounders. These
confounders included CES-D score, age, gender, race, trial interventions, education and income.
Confounding was also assessed by comparing the unadjusted to the covariate-adjusted change in
the β estimate of the main effect model. Wald tests were used to assess effect modification by
these covariates, testing product interaction terms of physical activity variables with age, gender,
race, CES-D score, trial intervention, education and income. None of the interaction tests were
statistically significant (all P>0.10). Our final models included adjustment for CES-D, age, race,
education and income. We checked the linearity of the relationship between cognition and all
other independent variables. The linearity assumption of ordinal variables treated as continuous
variables (years of education and income) was also satisfied. We fitted general linear models
using generalized estimation equations to assess the continuous relationship between three
cognition measures (global cognition, verbal memory and executive function composite scores)
and the continuous physical activity variable, adjusting for CES-D score, age, gender, race,
education and income. We also tested the relationship between cognition and the categorical
vigorous physical activity variable, adjusting for the same confounders.
We also evaluated the association of the same physical activity variables with the change in
global cognition, executive function, and verbal memory evaluated at baseline and follow-up.
Cognitive change was computed by subtracting the cognitive score at baseline from the score at
the follow-up cognitive test (score II – score I). In addition to the confounders detailed above,
these models adjusted for the baseline value of the cognitive variable. Linear regression was used
14

to evaluate relationship between cognitive function and physical activity, adjusted for age,
gender, race, scale of depression, education, income, intervention of trial, and baseline of
cognitive score. All statistical analysis used SAS software 9.3 (SAS Inc., Cary, NC), and all
tests for significance were conducted at a two-sided 0.05 level.

15

RESULTS
Analysis Sample. Table 1 provides descriptive statistics of demographics for the entire sample
(combined trials), and separately by the three trials. A total of 1499 participants including 506
participants from the BVAIT trial, 350 participants from the WISH trial, and 643 participants
from the ELITE trial were included. By trial design, only the BVAIT trial included male
participants (n=309). The average age was about 60 years in all three trials. The majority of
participants were White non-Hispanic (66% of the sample). Participants had approximately 16
years of education on average. The average income was between 65,000 to 69,000 dollars.
Table 1 Participant characteristics at baseline

Total sample BVAIT WISH ELITE
(n=1499) (n=506) (n=350) (n=643)
Age at baseline 59.9 (6.9) 60.4 (8.1) 60.8 (10.0) 60.6(7.1)
Gender
Male 309 (20.6) 309 (61.1) 0 (0.0) 0 (0.0)
Female 1190 (79.4) 197 (38.9) 350 (100.0) 643 (100.0)
Race
White Non-Hispanic 1991 (66.1) 328 (64.8) 223 (63.7) 440 (68.4)
Black Non-Hispanic 156 (10.4) 75 (14.8) 21 (6.0) 60 (9.3)
Hispanic 200 (13.3) 55 (10.9) 55 (15.7) 90 (14.0)
Asian/Pacific Islander 136 (9.1) 45 (8.9) 38 (10.9) 53 (8.3)
Native American 3 (0.2) 3 (0.6) 0 (0.0) 0 (0.0)
Other 13 (0.9) 0 (0.0) 13 (3.7) 0 (0.0)
Baseline CES-D score 7.4 (7.6) 6.3 (6.6) 7.4 (6.9) 8.2 (8.4)
Years of Education
†
16.6 (2.8) 16.4 (2.8) 16.5 (2.8) 16.8 (2.7)
Annual Income
♯
6.8 (3.2) 6.6 (3.1) 6.7 (3.3) 6.7 (3.3)
(Per $10,000)
16

Notes: Numbers are mean (SD) or n (%)
† There was 1 missing value from the BVAIT trial.
♯ There were 107 (7.14%) missing values.

Table 2 displays the baseline (the first visit) descriptive statistics of physical activity and
cognition.
Table 2 Descriptive Statistics of Baseline Physical Activity, and Cognition
Total sample BVAIT WISH ELITE
(n=1499) (n=506) (n=350) (n=643)
Moderate physical activity 6.0 (7.1) 6.5 (7.7) 5.7 (6.0) 5.9 (7.1)
(>3 MET, <6 MET), h/wk
Vigorous physical activity 0.5 (1.7) 0.8 (2.1) 0.4 (1.4) 0.3 (1.3)
(>6 MET), h/wk
Global cognitive score 3.5 (1.8) 1.1 (1.7) -2.1 (1.7) 0.3 (1.8)
(weighted), /1000
Executive function composite -8.4 (1.4) -1.7 (1.4) -8.3 (1.3) -1.9 (1.4)
score (weighted), /1000
Verbal memory composite 1.0 (1.3) 0.7 (1.3) 6.1 (1.4) 6.4 (1.3)
score (weighted), /1000

Association of Global Cognition with Physical Activity. Table 3 shows the relationship
between weighted global cognitive score and total hours of moderate or higher physical activity.
The modeling accounted for correlated outcome data using generalized estimating equations and
17

adjusted for CES-D score, age, gender, race, trial, test number, years of education, and income.
Each participant had one or two follow-up cognitive tests following baseline cognitive testing.
Table 3 Association of Global Cognitive Score with Hours of Moderate or Higher Physical
Activity and Confounders
Variable β estimate (SE) Z P
At least moderate (>3 MET) 0.01 (0.008) 1.41 0.16
physical activity, h/wk*
CES-D score -0.004 (0.004) -0.99 0.32
Age -0.05 (0.005) -9.64 <.0001
Gender 0.01
Male -0.37 (0.15) -2.52
Female - - -
Race <.0001
Black Non-Hispanic -1.09 (0.14) -7.81
Hispanic -1.06 (0.14) -7.74
Asian/Pacific Islander -0.90 (0.15) -6.03
Native American/Other -0.92 (0.34) -2.58
White Non-Hispanic - - -
Trial 0.02
BVAIT 0.06 (0.14) 0.40
ELITE -0.25 (0.11) -2.37
WISH - - -
Test number 0.28 (0.03) 9.18 <.0001
Years of Education 0.12 (0.02) 7.67 <.0001
Income, per $10,000 0.08 (0.01) 5.78 <.0001
Notes: * The measure of moderate- and vigorous-intensity physical activity used in this study
was averaged over all physical activity assessments obtained throughout trial follow-up.
18

Among all three trials, the weighted global cognitive score was positively but not statistically
significantly associated with the number of total weekly hours of moderate- and vigorous-
intensity physical activity (modeled as a continuous variable, P=0.16), adjusting for age, gender,
race, trial, CES-D score, years of education and income. Among the demographic variables
tested, age, gender, race, year of education, and income were all significantly related to the
response variable on multivariate modeling (all P<0.05). Age was negatively related to the
weighted global cognitive score (P<0.0001). Average global cognition significantly differed by
race (all P≤0.01). Compared to the non-Hispanic White reference group, the Black non-Hispanic,
Hispanic, Asian or Pacific Islander, and Native American groups had significantly lower global
cognitive test scores. Compared to females, males had significantly lower mean global cognitive
score (P=0.01). Higher education and income were each positively associated with the global
cognitive score (both P<0.0001). The CES-D score was inversely associated with global
cognitive function; however, the relationship did not reach statistical significance (P=0.32).
Since each participant was cognitively tested one or two more times after their baseline cognitive
test score, the number of tests was also significantly related to the global cognitive score
(P<0.0001), reflecting a likely practice effect.

Table 4 shows the association of the global cognitive score change (score II – score I) by the
averaged moderate and vigorous physical activity hours per week of moderate or higher intensity
using a linear model, adjusting for age, gender, race, CES-D score, trial, years of education,
income, and the baseline global cognitive score (score I). The physical activity hours variable
was calculated by averaging over all the reported weekly hours of moderate- and vigorous-
intensity physical activity for each participant over the trial.
19

Table 4 Association of the Change in Global Cognitive Score (weighted) with Physical Activity
and Confounders
Variable β estimate (SE) t value P
At least moderate (>3 MET) 0.003 (0.005) 0.64 0.52
physical activity, h/wk
Age -0.04 (0.003) -11.40 <.0001
Gender 0.0006
Male -0.29 (0.08) -3.42
Female - - -
Race 0.003
Black Non-Hispanic -0.27 (0.08) -3.19
Hispanic -0.22 (0.08) -2.81
Asian/Pacific Islander -0.15 (0.08) -1.83
Native American/Other -0.09 (0.25) 0.36
White Non-Hispanic - - -
CES-D score 0.003 (0.003) 1.07 0.28
Trial 0.87
BVAIT -0.03 (0.08) -0.32
ELITE -0.03 (0.06) -0.53
WISH - - -
Baseline Global Cognitive score -0.25 (0.02) -16.45 <.0001
Years of Education 0.05 (0.01) 5.01 <.0001
Income, per 10,000 0.006 (0.009) 0.66 0.51

In the combined trials, the change of global cognitive score was not significantly associated with
physical activity (P=0.52), adjusting for age, gender, race, trial, CES-D score, baseline global
cognitive score, years of education and income. The CES-D score was also not significantly
related to global cognitive change (P=0.28). Age, race, years of education, and baseline cognition
20

were all highly significantly related to the change in the weighted global cognitive score (all
P<0.01). Age and baseline cognitive test score were negatively related to the change of weighted
global cognitive score, while years of education was positively related. Compared to females,
males had significantly less change in global cognition (P<0.05). Compared to the White non-
Hispanic group, the Black non-Hispanic and Hispanic groups had significantly less change in
global cognition (both P<0.01), while the Asian or Pacific Islander and Native American or other
groups did not significantly differ from White non-Hispanics in change of global cognitive test
score (both P>0.05). The relationship between the change of global cognitive test score and
income was not significant (P=0.51). There was also no significant difference in change in global
cognition among the three trials (P=0.87).

Table 5 shows the relationship between the weighted global cognitive score and categorized
vigorous physical activity (no vigorous activity, below median vigorous-intensity hours, above
median vigorous-intensity hours). The model was adjusted for CES-D score, age, gender, race,
trial, test number, years of education, and income using GEE. Each participant had one or two
additional cognitive tests after baseline cognitive testing.
Table 5 Association of Global Cognitive Score (weighted) with Hours of Vigorous Physical
Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.01
Above median hours (0.40 h/wk) 0.28 (0.10) 2.80
Below median hours (0.40 h/wk) 0.21 (0.10) 2.01
No vigorous activity - - -
21

CES-D score -0.003 (0.004) -0.92 0.36
Age -0.05 (0.005) -9.27 <.0001
Gender
Male -0.37 (0.14) -2.59 0.01
Female - - -
Race <.0001
Black Non-Hispanic -1.07 (0.14) -7.64
Hispanic -1.06 (0.14) -7.73
Asian/Pacific Islander -0.87 (0.15) -5.85
Native American/Other -0.90 (0.35) -2.58
White Non-Hispanic - - -
Trial 0.02
BVAIT -0.001 (0.14) -0.01
ELITE -0.26 (0.11) -2.42
WISH - - -
Test number 0.28 (0.03) 9.09 <.0001
Years of Education 0.12 (0.02) 7.53 <.0001
Income, per 10,000 0.08 (0.01) 5.62 <.0001

Combining all three trials, the weighted global cognitive score was statistically significantly
associated with the categorized vigorous-intensity physical activity hours, adjusting for age,
gender, race, trial, CES-D score, test number, years of education and income (P=0.01).
Compared to the lowest level representing participants who reported no vigorous physical
activity, the other two levels had significantly higher mean global cognitive score (both P<0.05).

22

Table 6 shows the association of the global cognitive score change (score II – score I) by levels
of the vigorous-intensity physical activity variable. The generalized linear model (normal
distribution, identity link) was adjusted for age, gender, race, trial, CES-D score, years of
education, income, and the baseline global cognitive score (score I).
Table 6 Association of Change in Global Cognitive Score (weighted) with Vigorous Physical
Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.07
Above median hours (0.40 h/wk) 0.04 (0.06) 0.69 0.49
Below median hours (0.40 h/wk) 0.14 (0.06) 2.31 0.02
No vigorous activity - - -
CES-D score 0.003 (0.003) 1.00 0.32
Age -0.04 (0.003) -11.39 <.0001
Gender
Male -0.28 (0.08) -3.36 0.008
Female - - -
Race 0.003
Black Non-Hispanic -1.07 (0.14) -7.64
Hispanic -1.06 (0.14) -7.73
Asian/Pacific Islander -0.87 (0.15) -5.85
Native American/Other -0.90 (0.35) -2.58
White Non-Hispanic - - -
Trial 0.75
BVAIT -0.05 (0.08) -0.63
ELITE -0.04 (0.06) -0.68
WISH - - -
Baseline Global Cognitive score -0.25 (0.01) -16.47 <.0001
23

Years of Education 0.05 (0.01) 5.01 <.0001
Income, per 10,000 0.004 (0.009) 0.47 0.63

Combining all three trials, the change of weighted global cognitive score was positively
associated, with borderline statistical significance, with the categorized vigorous-intensity
physical activity hours, adjusting for age, gender, race, trial, CES-D score, years of education,
income, and baseline cognitive score (P=0.07). Compared to the lowest level representing
participants who reported no vigorous physical activity, the level of vigorous physical activity
with below median hours had significantly higher mean global cognitive score (P=0.02).

Association of Executive Function with Physical Activity. Table 7 shows the relationship
between the executive function composite score and number of hours of moderate and higher
physical activity. The modeling accounted for correlated longitudinal outcome data (repeated
cognitive scores) using GEE and adjusted for age, gender, race, trial, CES-D score, test number,
years of education, and income.
Table 7 Association of Executive Function Score with Physical Activity and Confounders
Variable β estimate (SE) Z P
At least moderate (>3 MET) -0.003 (0.006) -0.49 0.63
physical activity, h/wk
CES-D score -0.003 (0.003) -1.05 0.29
Age -0.05 (0.004) -12.87 <.0001
Gender
Male -0.23 (0.10) -2.28 0.02
Female - - -
24

Race <.0001
Black Non-Hispanic -0.79 (0.10) -8.13
Hispanic -1.10 (0.10) -10.58
Asian/Pacific Islander -0.66 (0.10) -6.36
Native American/Other -0.74 (0.24) -3.15
White Non-Hispanic - - -
Trial 0.01
BVAIT 0.06 (0.10) 0.59
ELITE -0.17 (0.08) -2.19
WISH - - -
Test number -0.03 (0.02) -1.60 0.11
Years of Education 0.10 (0.01) 8.19 <.0001
Income, per 10,000 0.08 (0.01) 7.71 <.0001
After adjustment for CES-D and demographic variables, the executive function composite score
was not statistically significantly associated with the continuous physical activity variable of
weekly hours in moderate or higher levels of activity (P=0.63). The CES-D score was negatively
associated with the executive function composite score; however, the relationship was not
significant (P=0.29). Among the demographic variables tested, age, gender, race, years of
education and income from combined three trials were all significantly related to executive
function (all P<0.05). Age was also negatively related to this measure of cognition (P<.0001).
Compared to females, males had significant lower executive function composite score (P=0.02).
Compared to the White non-Hispanic group, the Black non-Hispanic, Hispanic, Asian or Pacific
Islander, and Native American or other groups all had significant decreased executive function
test scores (all P<0.01). Executive function significantly differed among the three trials (P=0.01).

25

Table 8 shows the association of the change in the executive function composite score (score II
– score I) by the trial-averaged weekly hours of moderate and higher physical activity hour after
adjustment for age, gender, race, trial, years of education, income, CES-D score, and the baseline
executive function composite score (score I).
Table 8 Association of the Change in Executive Function Composite Score with Average
Weekly Hours of Moderate and Higher Physical Activity and Confounders
Variable β estimate (SE) t value P
At least moderate (>3 MET) -0.004 (0.003) -1.16 0.24
physical activity, h/wk
CES-D score -0.002 (0.002) -1.15 0.25
Age -0.02 (0.002) -8.91 <.0001
Gender
Male -0.19 (0.05) -3.68 0.0002
Female - - -
Race 0.001
Black Non-Hispanic -0.15 (0.05) -2.77
Hispanic -0.18 (0.05) -3.58
Asian/Pacific Islander -0.008 (0.05) -0.14
Native American/Other 0.05 (0.16) 0.34
White Non-Hispanic - - -
Trial 0.69
BVAIT 0.04 (0.05) 0.69
ELITE -0.004 (0.04) -0.10
WISH - - -
Baseline Executive Function score -0.16 (0.01) -11.72 <.0001
Years of Education 0.02 (0.006) 3.13 0.002
Income, per 10,000 0.007 (0.006) 1.22 0.22
26

In the combined trials, the change in the executive function composite score was inversely but
not significantly associated with physical activity (P=0.24). The CES-D score was not
significantly related to change in executive function (P=0.25). Age, race, years of education, and
baseline cognition were all highly significantly related to the change in executive function (all
P<0.01). Among them, age and baseline cognitive test score were negatively related to the
change in executive function, while race and years of education were positively related.
Compared to males, females showed significantly higher changes in executive function (P<0.05).
Income was not related to change in executive function (P=0.22). Compared to the White non-
Hispanic group, the Black non-Hispanic and Hispanic group had significantly less change in
executive function (both P<0.01), while the Asian or Pacific Islander and Native American or
other groups did not significantly differ f= (both P>0.05). There was also no significant
difference in change in executive function among the three trials (P=0.69). The baseline
executive function composite score was inversely associated with the change of this measure of
cognition (P<0.0001).

Table 9 shows the relationship between the weighted executive function composite score and
vigorous physical activity modeled as a categorical variable (no vigorous activity, below median
vigorous-intensity hours, above median vigorous-intensity hours). The model was adjusted for
age, gender, race, trial, CES-D score, test number, years of education, and income by using
generalized estimating equation.

27

Table 9 Association of Executive Function Composite Score with Hours of Vigorous Physical
Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.11
Above median hours 0.14 (0.08) 1.90 0.06
Below median hours 0.12 (0.07) 1.63 0.10
No vigorous activity - - -
CES-D score -0.002 (0.003) -0.97 0.33
Age -0.05 (0.004) -12.43 <.0001
Gender
Male -0.26 (0.10) -2.62 0.009
Female - - -
Race <.0001
Black Non-Hispanic -0.77 (0.10) -7.88
Hispanic -1.09 (0.10) -10.54
Asian/Pacific Islander -0.64 (0.10) -6.17
Native American/Other -0.72 (0.23) -3.14
White Non-Hispanic - - -
Trial 0.02
BVAIT 0.03 (0.10) 0.32
ELITE -0.18 (0.08) -2.26
WISH - - -
Test number -0.03 (0.02) -1.65 0.10
Years of Education 0.10 (0.01) 8.21 <.0001
Income, per 10,000 0.08 (0.01) 7.60 <.0001

Combining all three trials, the executive function composite score was positively but not
statistically significantly associated with the categorized vigorous-intensity physical activity
28

hours, adjusting for age, gender, race, trial, CES-D score, test number, years of education and
income (P=0.11). Compared to the lowest level representing participants who reported no
vigorous physical activity, the other two levels with some vigorous physical activity were on
average non-significantly higher in executive function.

Table 10 shows the association of change in the executive function composite score (score II –
score I) by hours of vigorous-intensity physical activity. The generalized linear model (normal
distribution, identity link) was adjusted for age, gender, race, trial, CES-D score, years of
education, income, and the baseline executive function composite score (score I).
Table 10 Association of Change in Executive Function Composite Score with Hours of Vigorous
Physical Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.10
Above median hours (0.40 h/wk) -0.03 (0.04) -0.75
Below median hours (0.40 h/wk) -0.08 (0.04) -2.12
No vigorous activity - - -
CES-D score -0.002 (0.002) -1.05 0.29
Age -0.02 (0.002) -8.70 <.0001
Gender
Male -0.21 (0.05) -3.98 <.0001
Female - - -
Race 0.001
Black Non-Hispanic -0.15 (0.05) -2.82
Hispanic -0.18 (0.05) -3.50
Asian/Pacific Islander -0.003 (0.05) -0.05
29

Native American/Other 0.06 (0.16) 0.38
White Non-Hispanic - - -
Trial 0.53
BVAIT 0.05 (0.05) 0.98
ELITE 0.001 (0.04) 0.02
WISH - - -
Baseline Executive Function score -0.16 (0.01) -11.58 <.0001
Years of Education 0.02 (0.006) 3.19 0.002
Income, per 10,000 0.008 (0.006) 1.37 0.17

Combining all three trials, the change in the executive function composite score was inversely
but not statistically significantly associated with the categorized hours of vigorous-intensity
physical activity, adjusting for age, gender, race, trial, CES-D score, baseline cognitive score,
years of education, and income (P=0.10).

Association of Verbal Memory with Physical Activity. Table 11 shows the relationship
between the verbal memory composite score and hours of moderate or higher physical activity.
The modeling accounted for correlated outcome data using GEE and adjusted for age, gender,
race, trial, CES-D score, test number, years of education, and income. Each participant had one
or two additional cognitive tests after baseline cognitive testing.
Table 11 Association of Verbal Memory Score with Physical Activity and Confounders
Variable β estimate (SE) Z P
At least moderate (>3 MET) 0.008 (0.006) 1.23 0.22
physical activity, h/wk
CES-D score -0.006 (0.003) -1.81 0.07
30

Age -0.04 (0.004) -9.55 <.0001
Gender
Male -0.70 (0.11) -6.23 <.0001
Female - - -
Race <.0001
Black Non-Hispanic -0.40 (0.10) -3.92
Hispanic -0.56 (0.10) -5.57
Asian/Pacific Islander -0.58 (0.11) -5.06
Native American/Other -0.71 (0.20) -3.60
White Non-Hispanic - - -
Trial 0.001
BVAIT 0.32 (0.11) 2.96
ELITE -0.05 (0.08) -0.62
WISH - - -
Test number 0.18 (0.03) 5.78 <.0001
Years of Education 0.07 (0.01) 5.72 <.0001
Income, per 10,000 0.05 (0.01) 4.47 <.0001

Among all three trials, the verbal memory composite score was positively but not significantly
associated with the number of weekly hours of moderate and higher physical activity ( P=0.22),
adjusting for age, gender, race, trial, CES-D score, test number, years of education and income.
Among the demographic variables tested, age, gender, race, years of education, and income were
all significantly related to the response variable (all P<0.05). Age was negatively related to
verbal memory (P<0.0001). Compared to females, males had significantly lower mean verbal
memory (P<0.0001). Compared to the White non-Hispanic group, the Black non-Hispanic,
Hispanic, Asian or Pacific Islander, and Native American or other groups had significantly lower
verbal memory scores (all P<0.01). Higher education and income were positively associated with
31

verbal memory (both P<0.0001). The CES-D score was inversely associated with verbal memory
cognitive function; however, the relationship did not reach statistical significance (P=0.07). The
cognitive test number was positively and significantly related to the verbal memory score
(P<0.0001).

Table 12 shows the association of change in the verbal memory composite score (score II –
score I) and the trial-averaged weekly hours in moderate and higher physical activity with
adjustment for age, gender, race, trial, CES-D score, years of education, income, and the baseline
verbal memory composite score (score I).
Table 12 Association of the Change in Verbal Memory Composite Score with Weekly Hours of
Moderate and Higher Physical Activity and Confounders
Variable β estimate (SE) t value P
At least moderate (>3 MET) 0.02 (0.005) 2.97 0.003
physical activity, h/wk
CES-D score 0.005 (0.003) 1.68 0.09
Age -0.02 (0.003) -6.26 <.0001
Gender
Male -0.41 (0.08) -4.98 <.0001
Female - - -
Race 0.01
Black Non-Hispanic -0.09 (0.08) -1.09
Hispanic 0.01 (0.08) 0.14
Asian/Pacific Islander -0.28 (0.08) -3.38
Native American/Other -0.11 (0.24) -0.45
White Non-Hispanic - - -
32

Trial 0.01
BVAIT 0.20 (0.08) 2.50
ELITE 0.23 (0.06) 3.63
WISH - - -
Baseline Verbal Memory score -0.39 (0.02) -20.15 <.0001
Years of Education 0.04 (0.009) 4.81 <.0001
Income, per 10,000 0.01 (0.008) 1.50 0.13

In the combined trials, the change in the verbal memory composite score was highly significantly
positively associated with physical activity (P=0.003), adjusting for age, gender, race, trial, CES-
D score, years of education, income and baseline cognitive score. The CES-D score was not
significantly related to this measure of cognition change (P=0.09). Age, gender, race, years of
education, and baseline verbal memory cognition were all highly significantly related to the
change in verbal memory (all P<0.01). Among them, age and baseline cognitive test score were
negatively related to the change in verbal memory, while years of education was positively
related. Compared to females, males had significantly less change in verbal memory (P<0.0001).
Compared to the White non-Hispanic group, the Asian or Pacific Islander group had
significantly less change in verbal memory (P=0.001), while other race/ethnic groups did not
differ (all P>0.05). The relationship between the change in verbal memory and income was not
significant (P=0.13). There was a significant difference in change in verbal memory among the
three trials (P=0.01).

Table 13 shows the relationship between the verbal memory composite score and hours of
vigorous physical activity as a categorical variable (no vigorous activity, below median
vigorous-intensity hours, above median vigorous-intensity hours). The model was adjusted for
33

age, gender, race, trial, CES-D score, test number, years of education, and income by using GEE.
Table 13 Association of Verbal Memory Composite Score with Hours of Vigorous Physical
Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.20
Above median hours (0.40 h/wk) 0.14 (0.08) 1.78 0.07
Below median hours (0.40 h/wk) 0.05 (0.08) 0.59 0.56
No vigorous activity - - -
CES-D score -0.006 (0.003) -1.77 0.08
Age -0.04 (0.004) -9.26 <.0001
Gender
Male -0.70 (0.11) -6.26 <.0001
Female - - -
Race <.0001
Black Non-Hispanic -0.40 (0.10) -3.87
Hispanic -0.56 (0.10) -5.60
Asian/Pacific Islander -0.56 (0.12) -4.90
Native American/Other -0.71 (0.20) -3.61
White Non-Hispanic - - -
Trial 0.02
BVAIT 0.30 (0.11) 2.75
ELITE -0.05 (0.08) -0.62
WISH - - -
Test number 0.17 (0.03) 5.71 <.0001
Years of Education 0.07 (0.01) 5.59 <.0001
Income, per 10,000 0.05 (0.01) 4.39 <.0001

34

Combining all three trials, the verbal memory composite score was not statistically significantly
associated with the hours of vigorous-intensity physical activity, adjusting for age, gender, race,
trial, CES-D score, test number, years of education and income (P=0.20).

Table 14 shows the association of the change in the verbal memory composite score (score II –
score I) by hours of vigorous-intensity physical activity. The generalized linear model was
adjusted for age, gender, race, trial, CES-D score, years of education, income, and the baseline
verbal memory composite score (score I).
Table 14 Association of Change in Verbal Memory Composite Score with Hours of Vigorous
Physical Activity and Confounders
Variable β estimate (SE) Z P
Categorical vigorous hours 0.002
Above median hours (0.40 h/wk) 0.10 (0.06) 1.66
Below median hours (0.40 h/wk) 0.20 (0.06) 3.47
No vigorous activity - - -
CES-D score 0.005 (0.003) 1.52 0.13
Age -0.02 (0.003) -6.35 <.0001
Gender
Male -0.36 (0.08) -4.45 <.0001
Female - - -
Race 0.01
Black Non-Hispanic -0.08 (0.08) -0.96
Hispanic -0.006 (0.08) -0.09
Asian/Pacific Islander -0.28 (0.08) -3.45
Native American/Other -0.12 (0.24) -0.51
35

White Non-Hispanic - - -
Trial 0.002
BVAIT 0.16 (0.08) 1.93
ELITE 0.22 (0.06) 3.48
WISH - - -
Baseline Verbal Memory score -0.39 (0.02) -19.85 <.0001
Years of Education 0.04 (0.009) 4.56 <.0001
Income, per 10,000 0.01 (0.008) 1.19 0.23

Combining all three trials, the change in the verbal memory composite score was highly
statistically significantly and positively associated with the categorized vigorous-intensity
physical activity hours, adjusting for age, gender, race, trial, CES-D score, years of education,
income, and baseline cognitive score (P=0.002). Compared to the lowest level representing
participants who reported no vigorous physical activity, participants reporting <0.40 hours of
physical activity had highly significant higher mean verbal memory composite score (P=0.0005).
However, compared to the lowest level representing participants who reported no vigorous
physical activity, participants reporting >0.40 hours of physical activity had no significant mean
verbal memory composite score (P=0.10).

36

DISCUSSION
This study evaluated relationships between self-reported levels of physical activity and three
composite measures of cognitive ability (general cognition, verbal memory, and executive
function), adjusting for multiple confounders such as age, gender, race, CES-D score, trial
intervention, years of education, and income. We evaluated these associations in a population of
men and postmenopausal women with an average age of 60 years. We found three statistically
significant positive relationships between cognition and physical activity: (1) the weighted global
cognitive score and the categorized number of hours of vigorous-intensity physical activity
(p=0.01), (2) the 2.5-year change in the verbal memory composite score and average number of
weekly hours spent in moderate and higher physical activity (p=0.003), and (3) the 2.5-year
change in the verbal memory composite score and categorical number of hours in vigorous-
intensity physical activity (p=0.002). Executive function was not associated with any measure
of physical activity in our data.
Our findings are consistent with other studies. The Nurse’s Health Study showed that higher
levels of activity were associated with better cognitive status.
42
In this cohort study of 18766 US
female nurses aged 70 to 81 years, participants in the highest quintile of physical activity had a
20% lower risk of poor cognitive performance on global cognitive test, relative to women in the
lowest quintile of physical activity. The study also found that more active women exhibited less
cognitive decline, and this was especially so for women in the two highest quintiles of energy
expenditure. Women in the fourth and fifth quintiles had mean changes in global scores that were
0.04 (95% confidence interval, 0.02-0.10) and 0.06 (95 % confidence interval, 0.02-0.11)
standard units better than those in the lowest quintile.
37

A recent systematic meta-analysis also supported the findings of our study,
43
and included data
from available prospective studies published in 2010 that investigated the association between
physical activity and risk of cognitive decline in non-demented subjects. After the review
process, 15 prospective studies (12 cohorts) were included in the final analysis. Those studies
included 33816 non-demented subjects followed for 1-12 years. A total of 3210 patients showed
cognitive decline during the follow-up. The cumulative analysis of all the studies using a
random-effects model indicated that subjects who engaged in a high level of physical activity
had a lower (-38%) risk of cognitive decline during the follow-up (hazard ratio 0.62, 95%
confidence interval 0.54-0.70; P < 0.00001).
43

A randomized controlled trial testing the combined effects of physical plus mental activity on
cognitive function in older adults reported inconsistent results.
44
All 126 inactive, community-
residing older adults with cognitive complaints engaged in home-based mental activity (1 h/d, 3
d/wk) plus class-based physical activity (1 h/d, 3 d/wk) for 12 weeks. A 2×2 factorial design was
used. Among inactive older adults who had a mean age of 73.4 years with cognitive complaints,
global cognitive scores improved significantly over two years (mean, 0.16 SD; P<0.001), but did
not differ between groups in the comparison between mental activity intervention and control
(ignoring exercise, P=0.17), the comparison between exercise intervention and control (ignoring
mental activity, P=0.74), or across all 4 randomization groups (P=0.26).
44
The reason behind the
inconsistency of our findings might be related to the small sample size and short observation
duration of 12 weeks.
The strength of our study was apparent in many respects. Concerning the hypothesis that
physical activity may be more strongly associated with improvement in certain cognitive abilities
than others, we analyzed composite scores that included both a global cognitive test score as well
38

as two domain-specific composites, verbal memory and executive function. In this study, verbal
memory, but not executive function, was positively related to time spent in physical activity of at
least moderate intensity. Verbal memory is an important area of cognitive function, as it is
inversely associated with the risk of Alzheimer disease. The most common early deficit in
patients with Alzheimer's disease (AD) is in recent memory (reference). The validity of our
results is also supported by the low rate of participant withdrawal (<10%). Repeated measures of
physical activity allowed us to obtain a more representative measure of a participant’s average
physical activity profile over time than did a single measurement.
However, the study did suffer from several shortcomings. First, this study relied completely on
self-reported levels of physical activity. As a result, the study may have suffered from response
bias or error in recollection; errors in recollection of specific physical activities were in part
minimized by the fact that activities were recalled for the immediately previous 7 days. It was
unclear whether participants reported physical activity intensity correctly. Some studies have
shown that irrespective of gender, race, or BMI classifications, participants correctly estimated
physical activities of light effort but underestimated the intensity of moderate and vigorous
effort, even after being given commonly used exercise intensity descriptors.
46
Some participants
who performed less-intense physical activity recorded the hours as moderate or vigorous
intensity hours. As a result, some participants overestimated the intensity of their physical
activities. Therefore, the observed relationship between cognition and physical activity could be
an underestimation.
Another study showed that self-reported physical activity was significantly negatively related to
self-reported symptoms of depression, anxiety, and burnout,
47
which indicated that there may be
confounding bias related to the CES-D score. Although the nature and degree of the association
39

between lowered depression and more favorable cognition is still unclear, participants who
reported more physical activity tended both to be less depressed and to score higher on cognitive
measures. As such, the possibility of a partial depression-mediated relationship between
physical activity and cognition cannot be ruled out completely. Therefore, the association
between physical activity and depression may positively bias the association between physical
activity and cognition. To avoid the bias caused by CES-D score, we included it as a confounder
in the study. Without the adjustment for CES-D score, the relationship between cognition and
physical activity is notably stronger.
Finally, some forms of physical activity (e.g. sustained aerobic) may be more associated with
cognition than others.
48
Data from the Flemish Policy Research Centre Sport suggested that the
relationship between physical activity and cognitive health may differ across different types of
physical activity with different contents: housework, leisure active transportation, and sports
participation, etc. Therefore, specific types of physical activity (e.g. aerobics, high-intensity
interval training) could be more or less related to cognition. The code for different types of
physical activity should be accessed and included to model types of physical activity in relation
to cognition.
In conclusion, in this sample of healthy individuals with an average age of 60 years, verbal
memory was positively associated with both weekly hours in moderate and higher intensity
physical activity and number of weekly hours in vigorous physical activity. The global cognitive
test score was also significantly positively associated with vigorous-intensity physical activity as
compared to no vigorous-intensity physical activity. Physical activity was not associated with
executive functions. Future work should go beyond the limitations of our study and seek to
address sources of bias in the association between various forms of physical activity and specific
40

cognitive ability subareas. Undoubtedly, more careful and detailed analyses would benefit public
efforts aimed at increasing physical activity and curbing age-related cognitive decline.

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Abstract (if available)

Abstract Objectives: To use data from three randomized clinical trials to determine whether physical activity is associated with cognitive function in elderly healthy individuals after adjustment for the effects of depression, age, gender, race, trial intervention, years of education and income. ❧ Methods: In three randomized, double‐blind, placebo‐controlled trials, 1499 participants including 309 men and 1190 postmenopausal women were assessed with measurements of cognition and physical activity

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