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Article
Research on Aging
2022, Vol. 44(9-10) 709–723
© The Author(s) 2022
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DOI: 10.1177/01640275211070001
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Prospective Associations between Physical
Activity and Memory in the Canadian
Longitudinal Study on Aging: Examining
Social Determinants
Nicole G. Hammond
1
and Arne Stinchcombe
2
Abstract
Objectives: To examine associations between physical activity (PA) and prospectively assessed memory in a cohort of
cognitively healthy adults, after accounting for understudied social determinants.
Methods: We used data from the Canadian Longitudinal Study on Aging (CLSA). PA (exposure) and memory (outcome) were
assessed using validated measures in 2013–2015 and 2015–2018, respectively. Respondents reported their daily number of
hours spent engaging in five different PAs. We conducted multiple imputation and used linear regression (n= 41,394), adjusting
for five categories of covariates: demographics, sensory health characteristics, health behaviors, health status, and social
determinants (sex/gender, education, income, social support, perceived social standing, race, and sexual orientation).
Results: In crude models, nearly every intensity and duration of PA was associated with better memory. In fully adjusted
models, protective associations were attenuated; however, some associations held: all durations of walking, most durations of
light activities, moderate activities for ≥1 hour, and strenuous activities for 1 to <2 hours.
Discussion: Some forms of PA may be associated with better memory. The benefits of higher intensity PA may only be realized
after social determinants are addressed.
Keywords
exercise, social determinants, memory, Canadian Longitudinal Study on Aging, epidemiology
Introduction
Identifying modifiable lifestyle activities which may reduce or
slow memory decline among older adults is important to
promoting healthy aging and cognitive function. Recent ob-
servational research suggests that a greater number of weekly
steps may be protective against cognitive decline (Rabin et al.,
2019). In systematic reviews and meta-analyses of randomized
controlled trials (RCTs), the protective effects of physical ac-
tivity on cognitive function in older adults have also been
identified (Falck et al., 2019;Northey et al., 2018). In these trial
reviews, clinical samples (Northey et al., 2018) and participants
with clinical disorders (Falck et al., 2019), including depression
(i.e., a risk factor for cognitive decline), were excluded to meet
the study aims but reduce the generalizability of the findings.
Other reviews of interventional research have mixed findings
and cite short follow-up periods (Hoffmann et al., 2021;You n g
et al., 2015). However, Hoffman and colleagues (2021) ob-
served that aerobic exercise had a large effect on memory (g
= 0.80). In a review of RCTs with at least 6 months of follow-
up, a protective effect of physical activity against cognitive
decline or dementia was not consistently demonstrated
(Brasure et al., 2018). The authors summarized the evidence as
insufficient or of low-strength, potentially attributable to
between-study heterogeneity and common methodological
challenges, including sample size issues and short follow-up
(Brasure et al., 2018).
Regular physical activity is recognized for its potential to
improve cognitive function and reduce dementia risk, in-
cluding Alzheimer’s disease (Piercy et al., 2018). Partici-
pation in physical activity may protect against dementia
1
School of Epidemiology and Public Health, University of Ottawa, Ottawa,
ON, Canada
2
School of Psychology, University of Ottawa, Ottawa, ON, Canada
Corresponding Author:
Arne Stinchcombe, Assistant Professor, School of Psychology, University of
Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N 6N5, Canada.
Email: astinchc@uottawa.ca
through cardiovascular, neurogenesis, or anti-inflammatory
mechanisms (Valenzuela et al., 2020). For example, physical
activity has cardiovascular benefits and may affect cognitive
health by promoting cerebral blood flow (Valenzuela et al.,
2020). Exercise may also increase neurogenesis, production
of brain-derived neurotrophic factor in the hippocampus, and
circulation of anti-inflammatory cytokines (IL-10βand IL-4β),
actions that may improve cognitive function (Valenzuela et al.,
2020). Long-term memory has specifically been identified for
its potential to be modified by engagement in physical activity
(Pontifex et al., 2016).
Observational studies of cohorts often demonstrate positive
associations between physical activity and cognitive function.
For example, physical activity (vs. non-activity) (Beckett
et al., 2015) and high (vs. low) physical activity (Beydoun
et al., 2014) were associated with a reduced risk of dementia in
reviews published in the past decade. Blondell et al. (2014)
demonstrated a longitudinal relationship between greater
physical activity and a reduced risk of cognitive decline and
dementia. More recently, Erickson and colleagues (2019)
published a review of the physical activity literature and found
strong observational evidence for beneficial, prospective as-
sociations between greater physical activity and cognitive
decline and dementia. However, the individual prospective
cohort studies available for inclusion in the review differed
markedly in their range of adjustment for potential con-
founders (Beckett et al., 2015;Sofiet al., 2011).
Sofiand colleagues (2011) found that their meta-analyzed
studies included differing numbers of confounders, with some
including two or less. Similarly, Beckett et al. (2015) reported
that studies in their review usually included a selection of
certain confounders, a list that omitted race, sexual orientation,
income, and baseline cognitive function. In an earlier cited
review (Blondell et al., 2014), the authors conducted a sen-
sitivity analysis of studies (n= 9) that controlled for 10 or more
confounders, finding evidence of attenuation of the strength of
the associations between physical activity and cognition. Two
of the sensitivity analysis studies adjusted for race, one ac-
counted for social support, none considered sexual orientation,
and some considered certain social determinants (Blondell
et al., 2014). Race and sexual orientation have both been
excluded by studies (Laurin et al., 2001;Rovio et al., 2005)in
other review work (Beydoun et al., 2014), or sometimes race
but not sexual orientation is captured (Larson et al., 2006;
Podewils et al., 2005). Of interest, Podewils et al. (2005)
included race and adjusted for several health characteristics
and behaviors (e.g., apolipoprotein E genotype, hormone
replacement therapy, and alcohol use), and the lesser-studied
social support. While some longitudinal associations between
physical activity and dementia risk persisted after their ex-
tensive multivariable adjustment, the statistical associations
were reduced in magnitude as the number of adjustment
factors increased, and some statistical relationships dis-
appeared (Podewils et al., 2005). The extent to which social
determinants of cognitive aging (e.g., race, social standing,
and social support) may attenuate the prospective relation-
ships between health behaviors such as physical activity and
memory remains largely understudied.
Existing evidence recognizes race and gender as important
social determinants associated with disparities in physical
activity. Race and gender are also related to the risk of de-
mentia (Ferretti et al., 2018;Steenland et al., 2015). Sturman
et al. (2005) found that in a biracial community in Chicago,
physical activity was no longer associated with a slower rate of
cognitive change after accounting for demographics and often
excluded covariates of race, baseline cognitive function, and
cognitive stimulation. Thus, the additional factors (e.g., race)
entirely explained their associations. The authors concluded
that there was no evidence to suggest that physical activity
solely protects against cognitive decline in adults ≥65 years
(Sturman et al., 2005). In the same study, older women were
documented to engage in 2 hours less of physical activity, on
average, per week, when compared to older men (Sturman
et al., 2005). Forrester and colleagues’(2019) biopsychosocial
model of minority cognitive aging delineates the relationship
between minority stress and cognitive aging outcomes. The
authors suggest that the accumulation of social disadvantage
and stressors stemming from minoritized identities (e.g.,
racism) are associated with cognitive aging and increased risk
of dementia (Forrester et al., 2019). The model further posits
that psychosocial factors (e.g., discrimination, education, and
socioeconomic status) lead to unhealthy behavioral determi-
nants such as poor diet and physical inactivity, which in turn
lead to manifestations of physical/biological conditions, ul-
timately increasing risk for cognitive decline (Forrester et al.,
2019).
Physical activity disparities by sexual orientation are ev-
ident as early as adolescence (Mereish & Poteat, 2015). This is
of concern as physical activity is associated with numerous
health benefits (Piercy et al., 2018), and among lesbian, gay,
and bisexual (LGB) adults has been linked to better mental
health (Pharr et al., 2021). Although Nelson and Andel (2020)
found little evidence to support a relationship between sexual
orientation and physical activity, and there were no observed
differences between heterosexual persons and sexual minorities
on self-reported memory. The authors concluded their work by
calling for more representative, longitudinal, and cognitive re-
search on sexual orientation to address the dearth of literature in
the area (Nelson & Andel, 2020). Systemic and structural issues
may prevent engagement in sufficient amounts of physical
activity (Bantham et al., 2021), a positive health behavior that
may help prevent or slow dementia (Valenzuela et al., 2020).
However, with sufficient knowledge and resources, these
barriers may be addressed and surmounted (Bantham et al.,
2021). In short, there is a large literature base tying physical
activity to better cognitive health; however, the extent to which
a range of social determinants influence findings has not been
comprehensively explored. Prospective research with adequate
sample sizes which permit adjustment for a range of potential
confounders and with a broad spectrum of studied ages is
710 Research on Aging 44(9-10)
needed to determine whether physical activity may confer a
protective memory benefitacrosstime.
We expand the previous body of work by investigating
several understudied determinants of health in a prospective
examination of the relationship between physical activity and
memory. To do so, we used a large Canadian cohort study of
community-dwelling older adults between the ages of 45–85
years at baseline. Using the Canadian Longitudinal Study on
Aging (CLSA) we sought to: (1) confirm a prospective,
protective association between physical activity and objec-
tively assessed memory, and (2) determine whether a pro-
tective association between physical activity and memory
persists after adjusting for known confounders and under-
studied social determinants.
Methods
Study Sample
The CLSA is extensively described elsewhere (Raina et al.,
2009,2019). The CLSA is a longitudinal study of aging and
general well-being. Over 50,000 community-dwelling adults
between the ages of 45–85 were recruited at baseline. Pro-
spective measurements will be collected every 3-years until
2033 or participant death (Raina et al., 2019). Baseline data
collection was completed in 2015 and first follow-up in 2018
(Raina et al., 2019). The CLSA is comprised of two study
cohorts that required differing amounts of participation.
Participants recruited into the comprehensive cohort (n=
30,097) were required to live within a certain radius (25–
50 km) of one of 11 national data collection sites (Raina et al.,
2019). In-person participation for the comprehensive cohort
was required for some samples not used here (e.g., blood). The
tracking cohort (n= 21,241) was interviewed using a
computer-assisted telephone interview (CATI) system, but
both cohorts share overlapping core CLSA content, including
measures of health determinants and memory (Raina et al.,
2019). The cohorts were designed to be complementary so that
they can be combined for researchers to have access to a larger
national population-based sample (Raina et al., 2019).
Participants of the tracking cohort were recruited via the
Canadian Community Health Survey (CCHS) –Healthy
Aging Component (Raina et al., 2008). The eligibility criteria
for the CCHS then defined the characteristics of the Canadian
population eligible for participation in the CLSA, a process
that enables merging of the two cohort’s data (Raina et al.,
2008). Provincial health care registration databases and ran-
dom digit dialing were used to recruit the comprehensive
cohort and supplement recruitment into the tracking cohort
(Raina et al., 2008). In accord with the sampling frame of the
CCHS, certain persons or groups of persons were not eligible
for CLSA survey participation: Canadian persons living in the
territories or select remote regions, persons living on First
Nations reserves and settlements, full-time members of the
Canadian Armed Forces, and incarcerated or institutionalized
persons (Raina et al., 2008). Long-term care residents are
included in the definition of institutionalized persons, and
more information is available in the CLSA Study Protocol
(Raina et al., 2008). CLSA participants voluntarily provided
written informed consent (Raina et al., 2009). These analyses
received ethical clearance from the University of Ottawa
Research Ethics Board (REB).
Measures
Memory. Memory was objectively ascertained using the Rey
Auditory Verbal Learning Test (RAVLT) (Rey, 1964) in both
the tracking (Tuokko et al., 2017) and comprehensive cohorts
(Tuokko et al., 2020). Trained interviewers followed a stan-
dardized protocol and administered the first of the five RAVLT
learning trials, followed by a delayed recall trial administered
five minutes later (Tuokko et al., 2017,2020). For the first
trial, participants listened to an audio recording of 15 words
and were asked to immediately recall as many responses as
they could within 90 seconds (Canadian Longitudinal Study
on Aging, 2019). Delayed recall was assessed 5 minutes later
when participants were asked to recall as many words from the
earlier list within 60 seconds (Canadian Longitudinal Study on
Aging, 2019). A score of 0 was assigned if the interviewer had
to prompt the participant for the delayed recall trial (Canadian
Longitudinal Study on Aging, 2019). One point was given for
each correctly recalled response, up to a maximum of 15
(Canadian Longitudinal Study on Aging, 2019); thus, raw
scores for both the immediate and delayed recall trials range
from 0 to 15 (Tuokko et al., 2020). More information on the
RAVLT scoring and data quality checks are described else-
where (Canadian Longitudinal Study on Aging, 2019). The
RAVLT is regularly used clinically and in research (Tuokko
et al., 2020) and was demonstrated to perform similarly in the
CLSA as it has in other French and English speaking samples,
supporting its utility as a marker of memory in the present
study (Tuokko et al., 2017,2020). For purposes of this study,
we combined the raw scores from the immediate and delayed
recall trials to have a single continuous measure of memory
(range: 0–30) at baseline (covariate) and follow-up (outcome).
Visual inspection confirmed that the baseline and follow-up
measures of memory approximated a normal distribution.
Exposure. Physical activity was assessed using the Physical
Activity Scale for the Elderly (PASE), developed by Washburn
and colleagues (Washburn et al., 1993). The psychometric
properties of the scale were originally studied in a community-
dwelling sample of adults aged 65 or older, where the PASE
was found to have adequate reliability and validity, and was
ultimately recommended for use in epidemiologic surveys
(Washburn et al., 1993). A modified version of the PASE
(Canadian Longitudinal Study on Aging, 2015) was admin-
istered as part of a short telephone interview designed to help
mitigate CLSA attrition by maintaining participant engage-
ment (maintaining contact questionnaire) (Raina et al., 2009).
Respondents were asked to report their past-week frequency
Hammond and Stinchcombe 711
of engagement in five different intensities of physical activity:
walking, light activities, moderate activities, strenuous ac-
tivities, and strength-based activities. Specifically, respon-
dents were asked: “over the past 7 days, how often did you…”
“…take a walk outside your home or yard for any reason? For
example, for pleasure or exercise, walking to work, and walking
the dog”,“…engage in light sports or recreational activities
such as bowling, golf with a cart, shuffleboard, badminton,
fishing or other similar activities?”,“…engage in moderate
sports or recreational activities such as ballroom dancing,
hunting, skating, golf without a cart, softball or other similar
activities?”,“…engage in strenuous sports or recreational
activities such as jogging, swimming, snowshoeing, cycling,
aerobics, skiing, or other similar activities?”, and “…how
often did you do any exercises specifically to increase muscle
strength and endurance, such as lifting weights or push-ups?”
Response options were “never,”“seldom (1–2 days),”
“sometimes (3–4 days),”and “often (5–7 days).”Respondents
who answered “never”were not asked the follow-up average
daily duration question, whereas those who reported that they
had engaged in each respective activity at least 1–2 days over
the past week were subsequently asked to report “on average,
how many hours per day did you”:“spend walking?”,“engage
in these…” “…light sports or recreational activities?”,
“moderate sports or recreational activities?”,“strenuous sports
or recreational activities?”,and“engage in exercises to increase
muscle strength and endurance?”Response options ranged
from “less than 30 minutes,”“30 minutes but less than 1 hour,”
“1 hour but less than 2 hours,”“2 hours but less than 4 hours,”
and “4 hours or more.”Due to a small number of respondents in
the upper most duration category (4 hours or more per day), for
each intensity of physical activity (e.g., light and moderate
activities), we collapsed the two highest average daily duration
categories to represent a combined 2 or more hours per day
category. It is important to note that while the two overall PASE
questions both pertain to past-week transportation and leisure
time physical activity, they can be differentiated by the fact that
the former asks about frequency (days) whereas the latter asks
about average daily duration (time per day).
Covariates. Potential covariates were measured at baseline
and grouped into five categories: demographics, sensory health
characteristics, health behaviors, health status, and social
determinants.
Demographics. Demographic measures consisted of par-
ticipant age (continuous), marital status, cohort (tracking
[referent]/comprehensive), language of test administration
(English [referent]/French), and baseline memory (described
above). Marital status was classified as single/never married
(referent), married/common-law, divorced/separated/widowed.
Cohort status was adjusted for given the previously outlined
differences in participant recruitment and data collection
methodology. We included the language of administration of
the baseline memory test since others have demonstrated that
memory scores in the CLSA vary according to language
(Tuokko et al., 2017,2020). We confirmed that those
respondents retained for data analysis completed both the
baseline and follow-up memory tests in the same language
(French/English).
Sensory health characteristics. At baseline, participants
self-reported the quality of their hearing and vision on a five-
point scale from “poor”to “excellent.”Specifically, partici-
pants were asked: “Is your hearing, using a hearing aid if you
use one…” and “Is your eyesight, using glasses or corrective
lens if you use them…”. For both sensory health character-
istics, responses were collapsed to represent the presence
(poor/fair) or absence (good/very good/excellent) of low
hearing and vision, respectively.
Health behaviors. Participants self-reported the frequency
of their alcohol consumption, their fruit and vegetable intake,
and how often they smoke cigarettes. Frequency of alcohol
consumption was captured by a single question: “About how
often during the past 12 months did you drink alcohol?”
Participant responses ranged from “never, “once a week,”to
“almost every day/every day”. Response options were cate-
gorized as never (referent), infrequent (≤2–3 times/month),
regular (1–3 times/week), and frequent (≥4 times/week). As
part of an eating and nutrition screener (SCREEN-II) (Keller
et al., 2005), CLSA participants reported how many servings
of fruits and vegetables they generally eat per day, from ≤2
servings to ≥7 servings. We categorized fruit and vegetable
consumption as <5 servings (referent) and ≥5 servings to align
with recent evidence, which suggests that approximately 5
servings are most protective against reducing mortality risk
(Wang et al., 2021). Participants indicated whether they had
ever smoked 100 or more cigarettes in their lives (yes/no). We
classified those who responded “no”as never smokers (refer-
ent). Respondents who positively endorsed having ever smoked
100ormorecigarettesweresubsequentlyasked,“At the present
time, do you smoke cigarettes daily, occasionally or not at all?”
Respondents who had smoked 100 or more cigarettes in their
lifetime but had not smoked in the past month were considered
former smokers, while the remaining respondents (daily/
occasional smokers) were considered current smokers.
Health status. We used four measures of health status:
body mass index (BMI: weight in kilograms/height in meters
2
[continuous]), health-professional diagnosed mood and anxiety
disorders, neurological disorders, and cardiac/cardiovascular
health disorders. Participants were asked to report whether
“…a doctor ever told you that you have…” a mental health
disorder (“anxiety disorder”or “mood disorder”), a neuro-
logical disorder (“a memory problem?”,“dementia or Alz-
heimer’sdisease?”,“Parkinsonism or Parkinson’sdisease?”,
“multiple sclerosis?”,“epilepsy?”,or“migraine head-
aches?”), and/or a cardiac/cardiovascular disorder (“stroke or
CVA? [cerebrovascular accident]?”,“ministroke or TIA
[transient ischemic attack]?”,“high blood pressure or hy-
pertension?”,“heart disease [including congestive heart
failure or CHF]?”,“heart attack or myocardial infarction?”,
“diabetes, borderline diabetes or that your blood sugar is
high?”,“angina [or chest pain due to heart disease]?”,or
712 Research on Aging 44(9-10)
“peripheral vascular disease or poor circulation in your
limbs?”). Our disorder groupings overlap with the CLSA
groupings (Canadian Longitudinal Study on Aging, 2018),
also reported in Zhang and Sun (2020),exceptthatpersons
with a memory problem or dementia/Alzheimer’stypede-
mentia were excluded (see analytic sample). For mental
health disorders, we created a single variable to represent
neither diagnosis (referent), anxiety disorder only, mood
disorder only, or both. For the neurological and cardiac/
cardiovascular diagnosed conditions, we summed the
number of disorders for each respective category and then
reclassified them into none (referent), one, or two or more.
This follows a similar practiceusedinotherliteratureex-
amining the potential dose–response relationship of a det-
rimental health exposure (Felitti et al., 1998) and shares
overlap with the deficit accumulation conceptualization of
frailty markers (Rockwood & Mitnitski, 2007).
Social determinants. We included seven recognized social
determinants (Government of Canada, 2020): sex/gender
(men [referent]/women), education, income, social support,
perceived social standing, race, and sexual orientation. Par-
ticipants were categorized as men and women. Unfortunately,
we could not determine if participants responded based on
their sex (i.e., biological attributes) or gender (i.e., socially
constructed roles, behaviors, expressions, and identities).
Thus, in this study, we refer to sex/gender. Respondents’
educational attainment was classified as <secondary school
(referent), secondary school graduation, some post-secondary,
and post-secondary graduation. Participants were asked to
report their household’s income: “What is your best estimate
of the total household income received by all household
members, from all sources, before taxes and deductions, in the
past 12 months?”Responses were categorized as <$20,000 (ref-
erent), $20–49,999, $50–99,999, $100–149,999, and ≥$150,000.
Social support was measured using the Medical Outcomes
Study-Social Support Survey (MOS-SSS) (Sherbourne &
Stewart, 1991).TheMOS-SSSiscomprisedof19items
ratedonafive-point Likert scale which altogether produce a
total index of functional social support ranging from 0 to 100,
with higher scores indicating a greater perceived availability
of social support (Sherbourne & Stewart, 1991). The scale
has good psychometric properties, and the total score rep-
resents a multidimensional social support construct that
captures elements of emotional and informational, tangible,
positive social, and affectionate social support (Sherbourne
& Stewart, 1991). We computed tertile cut-offs and recoded
social support as low, medium, and high. The recoding
addressed the substantial negative skew in the measure in-
dicating a large buildup of scores at the higher end of the
distribution, as has been documented by the scale developers
(Sherbourne & Stewart, 1991) and others who have exam-
ined the social support-memory association in the CLSA
(Oremus et al., 2020). Perceived social standing was mea-
sured using the MacArthur Scale of Subjective Social Status
(Adler et al., 2000), a well-used measure which is related to
health behaviors, mental health, physical health, and self-
rated health even after controlling for objective socioeco-
nomic indicators (Zell et al., 2018). For this measure, par-
ticipants are presented with an image of a 10-rung ladder
representing social status and asked to indicate the rung
which corresponds to their perceived social standing within
their community (Adler et al., 2000). Higher scores reflect
greater perceived social standing. Participants were also
askedtoreporttheirrace:“People living in Canada come
from many different cultural and racial backgrounds. Are
you…”. Answer options included “White,”“Black,”
“Korean,”“Filipino,”etc. The information was used to
inform a CLSA derived variable which indicated whether
respondents self-identified as White, Black, or of other
cultural/racial backgrounds (e.g., “multiple racial or cul-
tural origins”). Due to some small cell sizes, we collapsed
some categories in order to have a three-level measure of
race: White, Black, and other non-White. Finally, sexual
orientation was captured using one item: “Do you consider
yourself to be:”“Heterosexual?”,“homosexual?”,or
“bisexual?”.
Statistical Analyses
Basic descriptive statistics were used to describe respondent
characteristics at baseline (percentage [%], mean [M] and
standard deviation [SD]). Respondents lost to follow-up and
those with missing data were compared with retained re-
spondents and those without missing data, respectively. To
do so, we used chi-square tests of independence (χ
2
)and
independent sample t-tests. For our primary analyses, we ran
crude (unadjusted) linear regression models between phys-
ical activity and memory at follow-up, followed by multi-
variable linear regression models adjusted for covariates and
social determinants. Therefore, there is a crude and multi-
variable model for each physical activity-memory relation-
ship. Physical activity, covariates, and social determinants
were all measured at baseline. We conducted multiple im-
putation using multivariate imputation by chained equations
(MICE) (van Buuren et al., 1999)withanaugmentedap-
proach (White et al., 2010) and m = 28 imputations.
Unimputed (complete case) results are presented as a
supplementary. Missing on the exposures (measures of PA)
ranged from 0.07 to 0.40%, missing on the outcome
(memory at first follow-up) was 6.98%, and missing on the
covariates was in most cases ≤3% (range: 0.03–3.21%)
except for income (6.12%), language of test administration
(5.95%), and baseline memory (6.73%). There was no
missing data for cohort status, age, and sex/gender. We did
not use CLSA-derived survey weights. Others in the field
have found that when using CLSA data, unweighted and
weighted regression models are highly similar when mea-
sures of cognition, including memory measured by the
RAVLT, are the outcome (O’Connelletal.,2019). Findings
were considered statistically significant at p<0.05. All
Hammond and Stinchcombe 713
analyses were conducted using Stata (release 15: College
Station, TX: StataCorp LLC).
Analytic Sample
While participants were free of cognitive impairment at the
time of recruitment into the CLSA (Raina et al., 2009), some
respondents self-reported a health-professional diagnosis of
dementia or Alzheimer’s type dementia (n= 111), or memory
problems (n= 893), at baseline. We removed these partic-
ipants (Figure 1) to ensure that our analytic sample was
cognitively healthy at baseline. After removal of respondents
who did not participate in the initial maintaining contact
questionnaire, when baseline physical activity was captured,
exclusion of respondents lost to follow-up and those with
missing data, the final analytic sample size was n= 30,173
before multiple imputation. Baseline data were collected
between September 2011 and May 2015. The maintaining
Figure 1. Flow diagram of respondents participating in the Canadian Longitudinal Study on Aging (CLSA) and followed from baseline to first
follow-up.
714 Research on Aging 44(9-10)
contact questionnaire was administered between baseline
and follow-up (September 2013 to December 2015). First
follow-up data were collected between December 2015 and
July 2018.
Results
Respondents Lost to Follow-Up
A comparison of respondents lost to follow-up versus retained
in the CLSA at first follow-up revealed that those lost to
follow-up were more likely to be members of the tracking
cohort (62.2%) (χ
2
= 801.57, p<0.001), men (51.9%) (χ
2
=
12.91, p<0.001), and of older age (M = 66.35, SD = 11.48) (t=
20.57, p<0.001).
Respondents with Missing Data
Respondents with missing data were more likely to be part of
the comprehensive cohort (55.7%) (χ
2
= 173.88, p<0.001),
women (54.7%) (χ
2
= 88.91, p<0.001), and of older age (M =
65.22, SD = 10.52) (t=32.20, p<0.001).
Table 1. Participant characteristics (n
unimputed
= 30,173).
Characteristic n(%) Characteristic n(%)
Demographics Health status measures continued
Age 61.63 (9.93) Neurological disorders
Cohort None 25,905 (85.6)
Tracking 11,233 (37.2) One 4162 (13.8)
Comprehensive 18,940 (62.8) ≥Two 106 (0.4)
Marital status Cardiac and cardiovascular disorders
Single 2294 (7.6) None 15,822 (52.4)
Married/common-law 22,103 (73.3) One 8788 (29.1)
Widowed/divorced/separated 5776 (19.1) ≥Two 5563 (18.4)
Language Social determinants
English 24,296 (80.5) Sex/gender
French 5877 (19.5) Men 15,238 (50.5)
Baseline memory 10.42 (4.02) Women 14,935 (49.5)
Sensory health characteristics Education
Low vision <Secondary school 1528 (5.1)
No 28,133 (93.2) Secondary school 3072 (10.2)
Yes 2040 (6.8) Some post-secondary 2168 (7.2)
Low hearing Post-secondary 23,405 (77.6)
No 26,192 (89.2) Income
Yes 3261 (10.8) <$20K 1254 (4.2)
Health behaviors $20–49,999 6581 (21.8)
Fruit and vegetable intake $50–99,999 11,015 (36.5)
<5 servings 17,739 (58.8) $100–149,999 6081 (20.2)
≥5 servings 12,434 (41.2) ≥$150K 5242 (17.4)
Smoking status Social support
Never 14,217 (47.1) Low 10,023 (33.2)
Former 13,509 (44.8) Medium 9185 (30.4)
Current 2447 (8.1) High 10,965 (36.3)
Alcohol use frequency Perceived social standing [M(SD)] 6.13 (1.90)
Never 3066 (10.2) Race
Infrequent 9180 (30.4) White 28,931 (95.9)
Regular 10,109 (33.5) Black 1082 (3.6)
Frequent 7818 (25.9) Other non-White 160 (0.5)
Health status measures Sexual orientation
Body mass index (BMI) 27.81 (5.27) Heterosexual 29,521 (97.8)
Mood and anxiety disorders Lesbian/gay 514 (1.7)
None 24,537 (81.3) Bisexual 138 (0.5)
Anxiety disorder only 1045 (3.5)
Mood disorder only 3382 (11.2)
Both mood and anxiety disorders 1209 (4.0)
Hammond and Stinchcombe 715
Respondent Characteristics
Respondents retained in the analytic sample are described in
detail in Table 1. At baseline, respondents were on average
61.63 years (SD = 9.93), most participants were married or in a
common-law relationship (73.3%), and English speaking
(80.5%). In terms of social determinants, there was approxi-
mately an equal representation of men and women, though
slightly more men (50.5%) were included in the sample. Most
respondents held a post-secondary degree (77.6%), had a total
household income within the range of $50,000 to $99,999
(CAD) (36.5%), had high social support (36.3%), perceived
themselves to have an average social standing approximately
one point higher than the social ladder’s middle rung (M = 6.13,
SD = 1.90), were White (95.9%), and heterosexual (97.8%). In
terms of physical activities, walking was the most frequently
reported activity (85.3%) (Tabl e 2). Only 14.7% of the sample
reported never walking, whereas most respondents reported a
lack of daily engagement in light (77.9%), moderate (85.7%),
strenuous (66.6%), and strength-based activities (70.5%).
Linear Regression Models
In crude models, nearly all intensities and durations of physical
activity were associated with better memory at first follow-up
(Table 3). At the highest duration (≥2 hours) of two types of
physical activity, light and strength-based activities, there was a
negative association with memory. For strength-based activi-
ties, the association was not statistically significant.
After adjustment for all covariates, prospective associa-
tions between physical activity and memory were greatly
attenuated (Table 4). In adjusted models, all walking durations
were associated with better memory at follow-up when
compared to those who never walked (Brange: 0.13–0.21).
Engagement in any duration of light activities was almost
always associated with better memory at follow-up, except for
the 30 minutes to <1 hour category, when compared to those
who never engaged in light activities (B= 0.09, p= 0.328).
Only the two highest moderate activity duration categories
were statistically associated with better memory at follow-up,
when compared to those who never engaged in moderate
activities (1 hour to <2 hours: B= 0.34, p<0.001; ≥2 hours: B
= 0.30, p<0.001). Engagement in moderate activities for less
intensive durations was not prospectively related to better
memory (<30 minutes: B=0.02, p= 0.909; 30 minutes to <1
hour: B= 0.18, p= 0.131). For strenuous activities, we ob-
served one statistical association with better memory at
follow-up. Respondents who reported engaging in strenuous
activities for 1 to ≤2 hours per day, when compared to those
who reported never taking part in strenuous activities, had
better memory at follow-up (B= 0.19, p<0.001). Therefore,
there was no statistical evidence that engagement in strenuous
activities at the shortest durations (<30 minutes: B=0.10,p=
0.216; 30 minutes to <1 hour: B=0.11,p= 0.066) and the
longest (≥2hours:B=0.06,p=0.479)wereprospectively
associated with memory at follow-up. In multivariable models,
there were no statistical relationships between strength-based
activities and memory.
Across all physical activity models, all social determinants
of health were statistically associated with memory except for
sexual orientation. There were no differences between lesbian/
gay and bisexual respondents when compared to heterosexual
respondents. However, there was consistent evidence of an in-
verse association between lesbian/gay sexual orientation identity
and memory, suggesting that while not statistically significant,
lesbian/gay respondents tended to show poorer memory scores at
follow-up. For both education and income, there was a protective
relationship with greater educational attainment and higher total
household income associated with better memory. As both social
support and perceived social standing increased, so too did
memory. Across all models, Black and other non-White re-
spondents had lower memory scores at follow-up when com-
pared to White respondents; the strength of associations was
noticeably stronger for Black respondents. Specifically, the
magnitude of observed associations for Black respondents was
more than twice the magnitude for other non-White respondents.
Table 2. Baseline distribution of engagement in different intensities
and durations of physical activity in the Canadian Longitudinal Study
on Aging (CLSA) (n= 30,173).
Percentage (%)
Walking
Never 14.7
<30 minutes 17.7
30 minutes to <1 hour 37.9
1 hour to <2 hours 22.8
≥2 hours 6.9
Light physical activities
Never 77.9
<30 minutes 5.3
30 minutes to <1 hour 4.4
1 hour to <2 hours 5.8
≥2 hours 6.6
Moderate physical activities
Never 85.7
<30 minutes 1.00
30 minutes to <1 hour 2.2
1 hour to <2 hours 4.5
≥2 hours 6.6
Strenuous physical activities
Never 66.6
<30 minutes 4.8
30 minutes to <1 hour 11.9
1 hour to <2 hours 11.9
≥2 hours 4.8
Strength based physical activities
Never 70.5
<30 minutes 13.5
30 minutes to <1 hour 9.9
1 hour to <2 hours 5.5
≥2 hours 0.6
716 Research on Aging 44(9-10)
Complete case (unimputed) results can be found in the
Supplemental eTables (1, 2) and were mostly similar to those
for our previously reported primary findings. In crude models,
unimputed results were nearly always weaker, except in some
cases. For example, for the two negative associations (light
and strength-based activities for ≥2 hours) and some inten-
sities (moderate and strenuous activities) at <30 minutes, the
results were somewhat stronger. In adjusted models, there
were some differences, including lack of statistical associa-
tions between walking and memory at the shortest (<30
minutes) and highest (≥2 hours) durations. The remaining
findings for social determinants and physical activity were
generally consistent, with some variability in the magnitude of
associations.
Discussion
We sought to confirm whether physical activity was pro-
spectively associated with better memory at first follow-up in
the CLSA. In unadjusted models and across nearly every
intensity and duration of physical activity, there was evidence
of a prospective relationship between physical activity and
better memory at follow-up. After adjusting for known co-
variates and lesser-studied social determinants, walking and
light activities remained almost always associated with better
memory. In contrast, only the highest durations of moderate
activities (>1 hour) and engaging in 1 to <2 hours of strenuous
activities were associated with better memory. Although in the
fully adjusted models, the observed associations were reduced
in size. As expected, our observation of some prospective,
protective associations between physical activity and memory
aligns with existing review evidence from cohort studies
suggesting that overall physical activity is beneficial (Beckett
et al., 2015;Beydoun et al., 2014;Blondell et al., 2014;
Erickson et al., 2019). This study adds to the existing literature
base by separating different intensities and durations of
physical activity. What physical activity “dose”is best for
cognitive health remains unknown, and whether it may vary
by population or age remains to be determined (Erickson et al.,
2019). Our results suggest that after accounting for numerous
health characteristics, behaviors, and social determinants, less
intensive physical activities may be of most benefit for memory
in this national sample of mid-life and older Canadians.
Our findings support recent advances in 24-hour movement
guidelines for Canadian adults (Canadian Society for Exercise
Physiology, 2021a;2021b) and United States (US) recom-
mendations for adults (Piercy et al., 2018). Recent guidelines
for both countries recognize the potential health benefits of
any physical activity, including light activities, over sedentary
behaviors (Canadian Society for Exercise Physiology, 2021b,
2021a;Piercy et al., 2018). Guidelines from the US (Piercy
et al., 2018) also address that not all older adults can meet
recommended moderate to vigorous activity recommenda-
tions due to health considerations but should strive to engage
in any amount of physical activity that they are able to. Such
advances are relevant because they may be considered more
accessibility and equity orientated. However, our findings also
share some similarities with those of Sturman and colleagues
(2005), who found that after adjustment for covariates often
missing from the existing literature (race, baseline cognition,
cognitive stimulation), there was no association between
physical activity and cognition. Consistent with their work, we
did not observe statistical relationships between shorter du-
rations of moderate intensity activities, and almost no asso-
ciations for strenuous and strength-based activities, in fully
adjusted models. There is some evidence to suggest that
among older adults, strength-based activities may be better
tied to executive function and overall cognition than memory
(Li et al., 2018).
In addition to physical activity, cognitive aging outcomes
have been linked to other health behaviors (e.g., fruit and
vegetable intake) and sensory health (e.g., hearing) (Stinchcombe
&Hammond,2021), mental health (e.g., depression), as well as
social determinants such as social support (Oremus et al., 2020).
The strength and direction of associations for our relation-
ships between social determinants and memory correspond
with earlier CLSA cross-sectional work, showing disparities
in memory by income, education, and race (Stinchcombe &
Hammond, 2021). In their review of how to address social
inequities in physical activity, Ball and colleagues (2015)
Table 3. Crude linear regression models of the prospective relationship between physical activity at baseline and memory at first three-year
follow-up in the Canadian Longitudinal Study on Aging (CLSA): multiple imputation results (n= 41,394).
Model 1 Model 2 Model 3 Model 4 Model 5
Walking Light activities Moderate activities Strenuous activities
Strength-based
activities
Characteristic B(SE)pB(SE)pB(SE)pB(SE)pB(SE)p
Physical activity
<30 minutes 0.59 (0.08) <0.001 0.50 (0.10) <0.001 0.44 (0.22) 0.046 0.51 (0.11) <0.001 0.35 (0.07) <0.001
30 minutes to <1 hour 0.95 (0.07) <0.001 0.89 (0.11) <0.001 0.91 (0.15) <0.001 1.16 (0.07) <0.001 0.64 (0.08) <0.001
1 hour to <2 hours 0.80 (0.07) <0.001 1.01 (0.10) <0.001 1.09 (0.11) <0.001 1.34 (0.07) <0.001 0.68 (0.10) <0.001
≥2 hours 0.68 (0.10) <0.001 0.47 (0.09) <0.001 0.32 (0.09) 0.001 0.99 (0.11) <0.001 0.01 (0.30) 0.971
Hammond and Stinchcombe 717
Table 4. Multivariable linear regression models of the prospective relationship between physical activity at baseline and memory at first three-year follow-up in the Canadian
Longitudinal Study on Aging (CLSA): multiple imputation results (n= 41,394).
Model 1 Model 2 Model 3 Model 4 Model 5
Walking Light activities Moderate activities Strenuous activities Strength-based activities
Characteristic B(SE)pB(SE)pB(SE)pB(SE)pB(SE)p
Demographics
Age 0.09 (0.002) <0.001 0.09 (0.002) <0.001 0.09 (0.002) <0.001 0.09 (0.002) <0.001 0.09 (0.002) <0.001
Cohort—comprehensive 0.83 (0.04) <0.001 0.83 (0.04) <0.001 0.83 (0.04) <0.001 0.82 (0.04) <0.001 0.83 (0.04) <0.001
Marital status
Married/common-law 0.02 (0.07) 0.813 0.02 (0.07) 0.754 0.02 (0.07) 0.778 0.02 (0.07) 0.826 0.02 (0.07) 0.819
Widowed/divorced/separated 0.01 (0.08) 0.871 0.02 (0.08) 0.817 0.02 (0.08) 0.801 0.02 (0.08) 0.838 0.01 (0.08) 0.844
Language—French 0.09 (0.05) 0.044 0.10 (0.05) 0.045 0.10 (0.05) 0.027 0.11 (0.05) 0.026 0.10 (0.05) 0.040
Baseline memory 0.51 (0.005) <0.001 0.51 (0.005) <0.001 0.51 (0.005) <0.001 0.51 (0.005) <0.001 0.51 (0.005) <0.001
Sensory health characteristics
Low vision 0.08 (0.07) 0.239 0.08 (0.07) 0.225 0.08 (0.07) 0.236 0.08 (0.07) 0.239 0.08 (0.07) 0.228
Low hearing 0.31 (0.06) <0.001 0.31 (0.06) <0.001 0.31 (0.06) <0.001 0.31 (0.06) <0.001 0.31 (0.06) <0.001
Health behaviors
Fruit and vegetable intake: ≥5 servings 0.23 (0.04) <0.001 0.23 (0.04) <0.001 0.23 (0.04) <0.001 0.23 (0.04) <0.001 0.23 (0.04) <0.001
Smoking status
Former 0.21 (0.04) <0.001 0.22 (0.04) <0.001 0.22 (0.04) <0.001 0.21 (0.04) <0.001 0.21 (0.04) <0.001
Current 0.31 (0.07) <0.001 0.31 (0.07) <0.001 0.31 (0.07) <0.001 0.30 (0.07) <0.001 0.31 (0.07) <0.001
Alcohol use frequency
Infrequently 0.15 (0.06) 0.019 0.15 (0.06) 0.017 0.15 (0.06) 0.018 0.15 (0.06) 0.018 0.15 (0.06) 0.018
Regularly 0.22 (0.06) 0.001 0.22 (0.06) 0.001 0.22 (0.06) 0.001 0.22 (0.06) 0.001 0.23 (0.06) <0.001
Frequently 0.32 (0.07) <0.001 0.32 (0.07) <0.001 0.32 (0.07) <0.001 0.32 (0.07) <0.001 0.33 (0.07) <0.001
Health status measures
Body mass index 0.009 (0.004) 0.013 0.009 (0.004) 0.008 0.009 (0.004) 0.013 0.009 (0.004) 0.013 0.009 (0.004) 0.010
Mood and anxiety disorders
Anxiety disorder 0.20 (0.10) 0.042 0.20 (0.10) 0.040 0.20 (0.10) 0.042 0.19 (0.10) 0.044 0.20 (0.10) 0.042
Mood disorder 0.06 (0.06) 0.288 0.06 (0.06) 0.282 0.06 (0.06) 0.326 0.06 (0.06) 0.293 0.06 (0.006) 0.284
Both mood and anxiety disorders 0.04 (0.09) 0.678 0.04 (0.09) 0.660 0.04 (0.09) 0.689 0.03 (0.09) 0.698 0.04 (0.09) 0.667
Neurological disorders
One 0.02 (0.05) 0.744 0.02 (0.05) 0.744 0.02 (0.05) 0.775 0.02 (0.05) 0.734 0.02 (0.05) 0.760
≥Two 0.40 (0.29) 0.162 0.42 (0.29) 0.145 0.42 (0.29) 0.145 0.41 (0.29) 0.160 0.42 (0.29) 0.145
Cardiac and cardiovascular disorders
One 0.01 (0.04) 0.780 0.01 (0.04) 0.808 0.01 (0.04) 0.858 0.01 (0.04) 0.882 0.01 (0.04) 0.799
≥Two 0.20 (0.05) <0.001 0.20 (0.05) <0.001 0.20 (0.05) <0.001 0.20 (0.05) <0.001 0.21 (0.05) <0.001
Social determinants
Sex/gender—women 1.35 (0.04) <0.001 1.34 (0.04) <0.001 1.35 (0.04) <0.001 1.35 (0.04) <0.001 1.35 (0.04) <0.001
(continued)
718 Research on Aging 44(9-10)
Table 4. (continued)
Model 1 Model 2 Model 3 Model 4 Model 5
Walking Light activities Moderate activities Strenuous activities Strength-based activities
Characteristic B(SE)pB(SE)pB(SE)pB(SE)pB(SE)p
Education
Secondary school 0.20 (0.09) 0.033 0.21 (0.09) 0.028 0.21 (0.09) 0.027 0.20 (0.09) 0.029 0.20 (0.09) 0.030
Some post-secondary 0.39 (0.10) <0.001 0.39 (0.10) <0.001 0.39 (0.10) <0.001 0.39 (0.10) <0.001 0.39 (0.10) <0.001
Post-secondary 0.54 (0.08) <0.001 0.54 (0.08) <0.001 0.54 (0.08) <0.001 0.54 (0.08) <0.001 0.54 (0.08) <0.001
Income
$20–49,999 0.11 (0.09) 0.221 0.11 (0.09) 0.231 0.11 (0.09) 0.242 0.11 (0.09) 0.240 0.11 (0.09) 0.233
$50–99,999 0.39 (0.09) <0.001 0.38 (0.09) <0.001 0.38 (0.09) <0.001 0.38 (0.09) <0.001 0.38 (0.09) <0.001
$100–149,999 0.45 (0.10) <0.001 0.45 (0.10) <0.001 0.44 (0.10) <0.001 0.44 (0.10) <0.001 0.45 (0.10) <0.001
≥$150K 0.49 (0.11) <0.001 0.49 (0.11) <0.001 0.48 (0.11) <0.001 0.47 (0011) <0.001 0.48 (0.11) <0.001
Social support
Medium 0.16 (0.04) <0.001 0.16 (0.04) <0.001 0.15 (0.04) <0.001 0.16 (0.04) <0.001 0.16 (0.04) <0.001
High 0.22 (0.05) <0.001 0.22 (0.05) <0.001 0.21 (0.05) <0.001 0.22 (0.05) <0.001 0.22 (0.05) <0.001
Perceived social standing 0.07 (0.01) <0.001 0.07 (0.01) <0.001 0.07 (0.01) <0.001 0.07 (0.01) <0.001 0.07 (0.01) <0.001
Race
Other non-White 0.33 (0.09) <0.001 0.33 (0.09) <0.001 0.33 (0.09) <0.001 0.33 (0.09) <0.001 0.33 (0.09) 0.001
Black 0.67 (0.24) 0.004 0.69 (0.23) 0.003 0.68 (0.23) 0.004 0.70 (0.23) 0.003 0.70 (0.23) 0.003
Sexual orientation
Lesbian/gay 0.10 (0.14) 0.464 0.09 (0.14) 0.517 0.09 (0.14) 0.533 0.10 (0.14) 0.491 0.10 (0.14) 0.486
Bisexual 0.14 (0.26) 0.598 0.15 (0.26) 0.570 0.14 (0.26) 0.592 0.16 (0.26) 0.554 0.15 (0.26) 0.573
Physical activity
<30 minutes 0.15 (0.06) 0.021 0.17 (0.08) 0.032 0.02 (0.17) 0.909 0.10 (0.08) 0.216 0.09 (0.05) 0.091
30 minutes to <1 hour 0.19 (0.05) 0.001 0.09 (0.09) 0.328 0.18 (0.12) 0.131 0.11 (0.06) 0.066 0.07 (0.06) 0.237
1 hour to <2 hours 0.13 (0.06) 0.028 0.21 (0.08) 0.006 0.34 (0.09) <0.001 0.19 (0.06) 0.001 0.10 (0.08) 0.189
≥2 hours 0.21 (0.08) 0.008 0.15 (0.07) 0.037 0.30 (0.07) <0.001 0.06 (0.09) 0.479 0.03 (0.23) 0.905
Note. All models adjusted for the following sets of covariates: demographics (age, cohort, marital status, language of test administration, and baseline memory), sensory health characteristics (low vision and
hearing), health behaviors (fruit and vegetable intake, smoking status, and alcohol use frequency), health status measures (body mass index, health-professional diagnosed mood and anxiety disorders,
neurological disorders, and cardiac/cardiovascular disorders), and social determinants (sex/gender, education, income, social support, perceived social standing, race, and sexual orientation).
Hammond and Stinchcombe 719
draw attention to the social gradient. The authors (Ball et al.,
2015) cite Australian work (Australian Bureau of Statistics,
2013) that found that the most socioeconomically privileged
adults are more likely to engage in “sufficient physical ac-
tivity.”Health behavior change is already notoriously dif-
ficult at the level of the individual (Samdal et al., 2017)and
the population (Kelly & Barker, 2016). Adding to the challenge
of general public health promotion of healthy lifestyles, some
population groups are more likely to experience barriers to
participation in physical activity (Bantham et al., 2021). While
our findings support engagement in physical activity for
memory benefits in mid-life and older adulthood, particularly
the potentially more widely accessible walking and light ac-
tivities, some persons may be socially obstructed from par-
ticipating. Members of minority communities often experience
disadvantage and barriers to engaging in health promoting
behaviors that are linked to cognitive aging (Forrester et al.,
2019). Equitable ways to promote physical activity are needed
and may be realized through various methods, including
community-based approaches and engagement (Ball et al.,
2015;Bantham et al., 2021).
In the present study, we did not find a protective rela-
tionship for muscle strengthening activities, for engagement in
moderate activities <1 hour per day, or most durations of
strenuous activities. In terms of memory, the benefits of
aerobic activities may outweigh those of muscle strengthening
activities when activities are considered in isolation. A
combination of aerobic and muscle strengthening activities
may be best and is supported by clinical trial evidence
(Bossers et al., 2015). Our findings build on previous work by
including social determinants of cognitive aging and dem-
onstrate that in a real-world context, where participants may be
diverse in their sociodemographic profiles and experience of
inequality and disadvantage, the benefits of physical activity
may only be observed for some less intense physical activities,
or for certain durations. This is also after accounting for other
potential daily stressors: medical conditions and loss of a
partner (e.g., widowed).
Strengths and Limitations
Our study included the first available follow-up data from the
CLSA, yet a limitation is that the follow-up period may still be
considered short. While we were able to study how many
social determinants are related to cognitive aging, minority
stress experiences may be better captured through perceived
discrimination or other markers of social disadvantage. We
captured multiple minoritized identities in the present study,
but these persons comparatively made up a small proportion of
the sample. For example, <4.5% of participants in the sample
were racialized and <2.5% identified as LGB. These findings
require replication, and cognitive aging research that engages
members of minoritized communities is needed. Another
limitation is our use of a past-week self-report measure of
physical activity instead of physical activity data collected via
objective means. On average, self-report of physical activity is
over-estimated compared to data collected via accelerometer
(Prince et al., 2008). Strengths included the population-based
survey methodology and our inclusion of several health-
professional diagnosed neurological and cardiac/cardiovascular
health disorders. In accounting for health disorders, we took a
deficit accumulation type approach (Rockwood & Mitnitski,
2007), increasing confidence in the likelihood that frailty
may not explain our observed associations. Further, the pro-
spective relationship between physical activity and memory is
lesser studied in mid-life adults (Erickson et al., 2019), an age
group included here.
Conclusions
From a health equity perspective, population-level benefits of
physical activity on memory may only be observed for less
intense physical activities, especially walking. Our results
suggest that walking and light activities were associated with
better memory, and to a lesser extent, moderate physical ac-
tivity, but only at high levels (>1 hour per day), after accounting
for social determinants. Less intensive activities may be more
broadly accessible to a wider range of the older adult general
population. Therefore, more people may be able to engage in
light activities more regularly, increasing their potential long-
term cognitive health benefits. The benefit of light exercise may
be underestimated.
Acknowledgments
The authors would like to thank the participants of the Canadian
Longitudinal Study on Aging (CLSA) for their contributions to
research.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This research
was made possible using the data/biospecimens collected by the
Canadian Longitudinal Study on Aging (CLSA). Funding for the
CLSA is provided by the Government of Canada through the Canadian
Institutes of Health Research (CIHR) under grant reference: LSA
94473 and the Canada Foundation for Innovation. This research has
been conducted using the CLSA Baseline Tracking Dataset version
3.6, Baseline Comprehensive Dataset version 4.2, Follow-up 1
Tracking Dataset version 2.1, Follow-up 1 Comprehensive Dataset
version 3.0, under Application Number 190238. The CLSA is led by
Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland. This
work was supported by a grant from the Alzheimer’s Society of Canada
Research Program awarded to Dr. Arne Stinchcombe. Ms. Nicole G.
Hammond is funded by the Frederick Banting and Charles Best Canada
Graduate Scholarship Doctoral Awards (CGS-D) program.
720 Research on Aging 44(9-10)
Disclaimer
The opinions expressed in this manuscript are the author’s own and
do not reflect the views of the Canadian Longitudinal Study on Aging
(CLSA).
Data Availability Statement
Data are available from the Canadian Longitudinal Study on Aging
(www.clsa-elcv.ca) for researchers who meet the criteria for access to
de-identified CLSA data.
ORCID iDs
Nicole G. Hammond https://orcid.org/0000-0001-9404-8416
Arne Stinchcombe https://orcid.org/0000-0002-2101-3535
Supplementary Material
Supplementary material for this article is available online.
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Author Biographies
Dr. Arne Stinchcombe: Dr. Arne Stinchcombe is an assistant
professor at the University of Ottawa in the School of Psy-
chology. He maintains expertise in cognitive aging and the
psychosocial aspects of health, aging, and older adulthood. He
has a particular interest in the social determinants of health in
older adulthood.
Nicole G. Hammond: Ms. Hammond is a PhD candidate in
the School of Epidemiology and Public Health at the Uni-
versity of Ottawa and a Psychiatric Epidemiologist. Her
doctoral research focuses on family-level risk and protective
factors for adolescent onset self-harm and suicidality. She also
studies behavioral determinants of health.
Hammond and Stinchcombe 723
