Does Physical Activity Protect Against Drug Abuse Vulnerability?

Abstract

The current review examined recent literature to determine our state of knowledge about the potential ability of physical activity serve as a protectant against drug abuse vulnerability.
Both preclinical and clinical studies were examined using either associational or random assignment study designs. In addition to examining drug use as an outcome variable, the potential neural mediators linking physical activity and drug abuse vulnerability were examined.
Several important conclusions may be drawn. First, the preclinical evidence is solid in showing that physical activity in various forms is able to serve as both a preventive and treatment intervention that reduces drug use, although voluntary alcohol drinking appears to be an exception to this conclusion. Second, the clinical evidence provides some evidence, albeit mixed, to suggest a beneficial effect of physical activity on tobacco dependent individuals. In contrast, there exists only circumstantial evidence that physical activity may reduce use of drugs other than nicotine, and there is essentially no solid information from random control studies to know if physical activity may prevent initiation of problem use. Finally, both preclinical and clinical evidence shows that various brain systems are altered by physical activity, with the medial prefrontal cortex (mPFC) serving as one potential node that may mediate the putative link between physical activity and drug abuse vulnerability. It is concluded that novel neurobehavioral approaches taking advantage of novel techniques for assessing the physiological impact of physical activity are needed and can be used to inform the longitudinal random control studies that will answer definitively the question posed.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Full text

Drug
and
Alcohol
Dependence
153
(2015)
3–13
Contents
lists
available
at
ScienceDirect
Drug
and
Alcohol
Dependence
j
ourna
l
h
o
me
pa
ge:
www.elsevier.com/locate/drugalcdep
Review
Does
physical
activity
protect
against
drug
abuse
vulnerability?
Michael.
T.
Bardoa,∗,
Wilson.
M.
Comptonb
aDepartment
of
Psychology
and
Center
for
Drug
Abuse
Research
Translation,
University
of
Kentucky,
Lexington,
KY
40536-0509,
USA
bNational
Institute
on
Drug
Abuse,
6001
Executive
Boulevard,
MSC
9581,
Bethesda,
MD
20892-9581,
USA
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
8
January
2015
Received
in
revised
form
5
May
2015
Accepted
22
May
2015
Available
online
8
June
2015
Keywords:
Physical
activity
Exercise
Drug
abuse
Drug
reward
Impulsivity
Stress
a
b
s
t
r
a
c
t
Background:
The
current
review
examined
recent
literature
to
determine
our
state
of
knowledge
about
the
potential
ability
of
physical
activity
serve
as
a
protectant
against
drug
abuse
vulnerability.
Methods:
Both
preclinical
and
clinical
studies
were
examined
using
either
associational
or
random
assign-
ment
study
designs.
In
addition
to
examining
drug
use
as
an
outcome
variable,
the
potential
neural
mediators
linking
physical
activity
and
drug
abuse
vulnerability
were
examined.
Conclusions:
Several
important
conclusions
may
be
drawn.
First,
the
preclinical
evidence
is
solid
in
show-
ing
that
physical
activity
in
various
forms
is
able
to
serve
as
both
a
preventive
and
treatment
intervention
that
reduces
drug
use,
although
voluntary
alcohol
drinking
appears
to
be
an
exception
to
this
conclu-
sion.
Second,
the
clinical
evidence
provides
some
evidence,
albeit
mixed,
to
suggest
a
beneficial
effect
of
physical
activity
on
tobacco
dependent
individuals.
In
contrast,
there
exists
only
circumstantial
evidence
that
physical
activity
may
reduce
use
of
drugs
other
than
nicotine,
and
there
is
essentially
no
solid
infor-
mation
from
random
control
studies
to
know
if
physical
activity
may
prevent
initiation
of
problem
use.
Finally,
both
preclinical
and
clinical
evidence
shows
that
various
brain
systems
are
altered
by
physical
activity,
with
the
medial
prefrontal
cortex
(mPFC)
serving
as
one
potential
node
that
may
mediate
the
putative
link
between
physical
activity
and
drug
abuse
vulnerability.
It
is
concluded
that
novel
neuro-
behavioral
approaches
taking
advantage
of
novel
techniques
for
assessing
the
physiological
impact
of
physical
activity
are
needed
and
can
be
used
to
inform
the
longitudinal
random
control
studies
that
will
answer
definitively
the
question
posed.
©
2015
Elsevier
Ireland
Ltd.
All
rights
reserved.
Contents
1.
Introduction
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3
2.
Does
physical
activity
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against
drug
abuse?
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Preclinical
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Clinical
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3.
Neural
changes
mediate
the
protective
effect
of
physical
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7
3.1.
Preclinical
evidence.
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3.2.
Clinical
studies
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4.
Concluding
remarks
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9
Contributors
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10
Disclaimer
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10
References
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10
1.
Introduction
There
is
a
widespread
recognition
that
physical
activity
and
vol-
untary
exercise
are
important
components
of
a
healthy
lifestyle.
∗Corresponding
author.
Tel.:
+1
859
257
6456.
E-mail
address:
mbardo@uky.edu
(Michael.T.
Bardo).
During
early
stages
of
life,
physical
activity
enhances
social
devel-
opment
and
learning
(Parcel
et
al.,
1989),
whereas
later
in
life,
it
can
help
slow
the
physical
and
cognitive
decline
associated
with
aging
(Cotman
and
Berchtold,
2007).
In
addition,
physical
activ-
ity
is
useful
in
the
prevention
and
treatment
of
various
disease
states,
including
Alzheimer’s
disease
(Cotman
and
Berchtold,
2007),
obesity-related
metabolic
diseases
(Bensimhon
et
al.,
2006;
Brown
and
Summerbell,
2009;
Qin
et
al.,
2010),
and
psychiatric
disorders
http://dx.doi.org/10.1016/j.drugalcdep.2015.05.037
0376-8716/©
2015
Elsevier
Ireland
Ltd.
All
rights
reserved.

4
Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
such
as
anxiety,
depression
and
schizophrenia
(Brown
et
al.,
2013;
Holley
et
al.,
2011;
Strohle,
2009).
The
beneficial
effects
of
physical
activity
on
health-related
outcomes
are
thought
to
be
mediated
by
a
wide
range
of
long-term
biological
alterations,
which
likely
explain
the
multiple
benefits
across
various
disease
states.
For
example,
exercise
reduces
the
incidence
of
obesity-related
diseases,
at
least
in
part,
by
altered
lipoprotein
levels
(Ainslie
et
al.,
2005;
Craig
et
al.,
1996).
In
contrast,
the
ability
of
physical
activity
to
enhance
learn-
ing
during
development
may
be
mediated
by
the
proliferation
of
glial
and
endothelial
cells,
as
well
as
an
increases
in
neurogenesis
and
neuronal
connectivity,
in
brain
regions
critical
for
learning
and
memory
(Eckert
and
Abraham,
2013;
Gelfo
et
al.,
2009;
Mandyam
et
al.,
2007;
Viola
et
al.,
2009).
Given
the
widespread
benefits
with
various
disease
states,
there
has
been
a
recent
initiative
to
determine
whether
physical
activ-
ity
and
exercise
have
utility
in
the
prevention
and
treatment
of
substance
use
disorders.
In
2008,
the
National
Institute
on
Drug
Abuse
convened
a
meeting
in
Bethesda,
Maryland
around
the
topic
entitled
“Can
Physical
Activity
and
Exercise
Prevent
Drug
Abuse”?
Shortly
afterwards,
a
Request
for
Applications
(RFA)
was
issued
tar-
geting
this
important
area
of
investigation.
More
recently,
a
funding
opportunity
announcement
(FOA)
subsequently
was
released
in
August,
2014
by
the
NIH
Office
of
Disease
Prevention
entitled
“Developing
and
Testing
Interventions
for
Health-Promoting
Physi-
cal
Activity”
to
help
address
this
continuing
challenge,
with
the
goal
of
achieving
the
2008
Physical
Activity
Guidelines
for
Americans
(www.health.gov/paguidelines/guidelines/default.aspx).
While
some
success
has
been
achieved
in
our
understanding
of
the
influence
of
physical
activity
on
drug
abuse
prevention
and
treatment
(Linke
and
Ussher,
2015;
Lynch
et
al.,
2013;
Smith
and
Lynch,
2011),
notable
gaps
persist.
In
particular,
there
is
little
infor-
mation
about
the
neurobiological
mechanisms
that
specifically
mediate
the
relation
between
physical
activity
and
drug
abuse
vul-
nerability
in
humans.
Further,
while
exercise-induced
changes
in
reward-relevant
neural
systems
are
likely
important
(Lynch
et
al.,
2013),
there
has
been
little
consideration
of
other
neurobehavioral
processes
such
as
impulsivity,
stress,
executive
cognitive
function,
and
emotion
regulation.
In
the
current
review,
we
update
the
major
preclinical
and
clin-
ical
findings
that
have
emanated
since
the
NIDA
meeting
in
2008.
In
the
case
of
preclinical
research,
a
brief
summary
of
key
neu-
ral
mechanisms
thought
to
mediate
the
effects
of
physical
activity
and
enrichment
on
drug
reward
are
presented.
While
physical
activity
produces
global
changes
throughout
the
brain,
the
medial
prefrontal
cortex
(mPFC)
may
play
a
pivotal
role
in
the
relation
between
physical
activity
and
drug
abuse
vulnerability.
The
mPFC
is
known
to
have
a
role
in
various
addiction-related
processes,
including
reward
sensitivity,
inhibitory
control,
stress
reactivity
and
emotion
regulation
(Koob
et
al.,
2014;
Perry
et
al.,
2011;
Rive
et
al.,
2013).
Further,
the
current
review
highlights
a
few
recent
reports
that
illustrate
the
effectiveness
of
physical
activity
in
pre-
venting
and
treating
drug
use/abuse.
However,
some
significant
barriers
have
impeded
translation
of
preclinical
neurobiological
evidence
to
humans.
Recommendations
for
further
research
to
address
this
important
issue
are
offered.
2.
Does
physical
activity
protect
against
drug
abuse?
Although
there
has
been
some
success
in
developing
school-,
family-
and
media-based
preventive
interventions
to
reduce
drug
abuse
risk
among
at-risk
adolescents
(Griffin
and
Botvin,
2010;
Hansen,
2010;
Palmgreen
and
Donohew,
2010),
these
interven-
tions
are
not
fully
or
widely
effective
across
a
range
of
individuals.
Similarly,
for
treatment
interventions
among
individuals
with
sub-
stance
use
disorders,
novel
medications
and
immunotherapies
for
the
treatment
of
substance
use
disorders
have
been
pursued
(Koob
et
al.,
2009;
Montoya
and
Vocci,
2008;
Shen
and
Kosten,
2011),
but
these
medical
treatments
are
not
effective
across
a
wide
range
of
individuals
and
side
effect
profiles
are
sometimes
problematic.
As
discussed
below,
both
preclinical
and
clinical
results
are
now
begin-
ning
to
provide
evidence
that
an
effective
alternative
approach
is
to
implement
intervention
strategies
that
promote
physical
activity.
This
may
be
accomplished
by
evaluating
physical
activity
alone
or
in
combination
with
other
preventive
and/or
treatment
modalities.
2.1.
Preclinical
evidence
At
the
preclinical
level,
there
is
considerable
overlap
in
the
protective
effects
of
either
physical
activity
or
environmental
enrichment
on
drug
use,
most
likely
because
exposing
animals
to
enriching
stimulation
elevates
levels
of
physical
activity
and
reduces
body
weight
(Bardo
and
Hammer,
1991;
Diamond
et
al.,
1965).
For
the
purpose
of
this
review,
however,
we
focus
on
phys-
ical
activity
separately,
as
enrichment
in
the
absence
of
physical
activity
may
not
induce
all
of
the
neurogenic
factors
responsible
for
activity-dependent
brain
adaptations
(Kobilo
et
al.,
2011).
A
host
of
neural,
social
and
individual
difference
factors
are
known
to
play
a
role
in
drug
taking
behavior.
Regardless
of
the
mechanism,
however,
a
consistent
finding
among
preclinical
stud-
ies
is
that
physical
activity
reduces
drug
self-administration.
This
basic
finding
has
been
reported
across
various
laboratories
and
methodologies,
as
well
as
across
different
developmental
periods
(adolescent
and
adult)
and
across
both
sexes
(see
Table
1);
how-
ever,
some
sex
differences
exist
in
the
literature,
as
described
previously
(Lynch
et
al.,
2013).
In
both
rats
and
mice,
the
most
com-
mon
method
for
promoting
physical
activity
is
to
provide
access
to
a
running
wheel,
although
a
treadmill
or
swimming
regimen
also
have
been
used.
The
beneficial
effect
of
physical
activity
is
most
notable
when
access
to
a
running
wheel
occurs
during
the
self-administration
session
(Cosgrove
et
al.,
2002;
Kanarek
et
al.,
1995;
Zlebnik
et
al.,
2012).
However,
when
access
to
a
running
wheel
is
implemented
either
before
or
after
daily
operant
condi-
tioning
sessions,
a
decrease
in
intravenous
self-administration
also
is
obtained
across
different
various
drugs,
including
cocaine
(Smith
et
al.,
2008b;
Smith
et
al.,
2011),
methamphetamine
(Engelmann
et
al.,
2013;
Miller
et
al.,
2012),
heroin
(Smith
and
Pitts,
2012)
and
morphine
(Hosseini
et
al.,
2009).
Physical
activity
also
decreases
the
escalation
of
self-administration
(Engelmann
et
al.,
2013;
Zlebnik
et
al.,
2012)
and
reinstatement
(Lynch
et
al.,
2010;
Sanchez
et
al.,
2013;
Smith
et
al.,
2012;
Thanos
et
al.,
2013;
Zlebnik
et
al.,
2010).
Especially
relevant
to
human
tobacco
cessation
treatments,
physical
activity
also
decreases
reinstatement
of
nicotine
seeking
following
a
period
of
extinction
(Sanchez
et
al.,
2013).
Thus,
regard-
less
whether
applied
concomitantly
or
non-concomitantly
with
access
to
drug,
engagement
in
physical
activity
has
a
protective
effect
across
different
stages
of
the
addiction
cycle.
Since
wheel
running
alone
is
rewarding
(Rasmussen
and
Hillman,
2011;
Silva
and
Heyman,
2001;
Trost
and
Hauber,
2014),
one
potential
explanation
for
the
decrease
in
self-administration
is
that
access
to
a
running
wheel
serves
as
an
alternative
non-drug
reinforcer.
Based
on
principles
of
behavioral
economics,
physical
activity
can
be
viewed
as
a
non-drug
substitute
that
interacts
with
the
unit
price
of
a
drug,
similar
to
how
responding
for
drug
reward
will
decrease
in
humans
when
a
monetary
reward
is
made
avail-
able
concurrently
(Johnson
et
al.,
2004).
In
rats,
food
and
sweetened
solutions
are
alternative
commodities
that
can
decrease
demand
for
a
drug
(Comer
et
al.,
1996),
although
these
non-drug
commodi-
ties
may
enhance
elasticity
of
the
demand
curve
only
in
highly
addicted
animals;
i.e.,
the
addicted
animal
is
less
likely
to
defend
consumption
as
unit
price
increases
(Lenoir
and
Ahmed,
2008).
Fur-
ther,
when
a
substitution
relationship
exists,
increasing
the
value

Michael.T.
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Wilson.M.
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/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
5
Table
1
Recent
preclinical
studies
(2008–present)
assessing
the
effectiveness
of
physical
activity
on
drug
reward
in
laboratory
animals.
Subjects
Type
of
Activity
Test
Effect
Reference
Rat
(adolescent
female)
Running
wheel
Cocaine
SA
↓Acquisition
↓Escalation
Zlebnik
et
al.
(2012)
Rat
(adult
female)
Running
wheel
Cocaine
SA
↑Extinction
↓Reinstate
Zlebnik
et
al.
(2010)
Rat
(adolescent
female)
Running
wheel
Cocaine
SA
↓PR
breakpoint
Smith
et
al.
(2008b)
Rat
(adult
male)
Running
wheel
Cocaine
SA
↓Reinstate
Lynch
et
al.
(2010)
Rat
(adolescent
male
and
female)
Running
wheel
Cocaine
SA
↓Escalation
Smith
et
al.
(2011)
Rat
(adolescent
male
and
female) Running
wheel Cocaine
SA ↓Reinstate Smith
et
al.
(2012)
Rat
(adolescent
male) Running
wheel
Cocaine
SA
↓Acquisition
Smith
et
al.
(2011)
Rat
(adolescent
female)
Running
wheel
Cocaine
SA
↓PR
breakpoint
Smith
and
Witte
(2012)
Rat
(adult
male)
Treadmill
Cocaine
SA
↓Reinstate
Thanos
et
al.
(2013)
Rat
(adult
male)
Running
wheel
Cocaine
SA
↓Reinstate
Peterson
et
al.
(2014)
Rat
(adolescent
female) Running
wheel Cocaine
CPP ↑Acquisition Smith
et
al.
(2008a)
Rat
(adult
male
and
female)
Treadmill
Cocaine
CPP
↓Acquisition
Thanos
et
al.
(2010)
Mouse
(adult
male)
Running
wheel
Cocaine
CPP
↑Acquisition
Mustroph
et
al.
(2011)
Mouse
(adolescent
male)
Running
wheel
Cocaine
CPP
↔Acquisition
Geuzaine
and
Tirelli
(2014)
Rat
(adult
male) Swimming
Amphetamine
CPP ↓Reinstate Segat
et
al.
(2014)
Rat
(adult
male)
Treadmill
Amphetamine
CPP
↓Acquisition
Fontes-Ribeiro
et
al.
(2011)
Rat
(adult
male)
Running
wheel
Methamphet
SA
↓Acquisition
Miller
et
al.
(2012)
Rat
(adult
male)
Running
wheel
Methamphet
SA
↓Acquisition
↓Escalation
Engelmann
et
al.
(2013)
Rat
(adolescent
male)
Running
wheel
Nicotine
SA
↓Reinstate
Sanchez
et
al.
(2013)
Mouse
(adult
male)
Treadmill
MDMA
CPP
↓Acquisition
Chen
et
al.
(2008)
Rat
(adolescent
male) Running
wheel Heroin
SA
(after
cocaine) ↓Maintenance
Smith
and
Pitts
(2012)
Rat
(adolescent
female)
Running
wheel
Heroin/cocaine
SA
↓PR
breakpoint
Lacy
et
al.
(2014)
Rat
(adult
male)
Treadmill
Morphine
SA
↓Acquisition
Hosseini
et
al.
(2009)
Rat
(adult)
Running
wheel
Morphine
CPP
↓Stress-potentiated
CPP
Rozeske
et
al.
(2011)
Mouse
(adult
female)
Running
wheel
Ethanol
drinking
↔Maintenance
Ozburn
et
al.
(2008)
Mouse
(adult
female
and
male) Running
wheel Ethanol
drinking ↓Acquisition
(female)
↔Acquisition
(male)
Gallego
et
al.
(2015)
CPP
=
conditioned
place
preference;
PR
=
progressive
ratio;
SA
=
self-administration;
↓
=
decrease;
↑
=
increase;
↔
=
no
change.
of
a
non-drug
commodity
reduces
drug
choice
(Heishman
et
al.,
2000;
Stitzer
et
al.,
1983).
From
a
clinical
perspective,
these
latter
results
suggest
that
individuals
who
are
inclined
to
find
physical
activity
to
be
highly
reinforcing
may
show
the
greatest
reduction
in
drug
self-administration.
While
one
can
conclude
firmly
that
physical
activity
has
a
pro-
tective
effect
on
drug
self-administration
in
laboratory
animals,
there
appears
to
be
at
least
three
important
caveats
to
this
conclu-
sion.
First,
in
contrast
to
stimulant
and
opiate
self-administration,
oral
consumption
of
alcohol
is
not
decreased
by
access
to
a
running
wheel
(Crews
et
al.,
2004;
Ozburn
et
al.,
2008;
Werme
et
al.,
2002a),
and
at
least
one
study
found
that
exercise
increases
alcohol
prefer-
ence
and
intake
in
mice
(Werme
et
al.,
2002a).
While
it
is
not
clear
what
factors
account
for
the
difference
between
alcohol
and
other
drugs
of
abuse,
the
route
of
administration
needs
to
be
considered
(oral
vs.
injection),
as
well
as
the
caloric
content
of
alcohol,
which
may
play
a
role
in
intake
following
physical
exertion.
Second,
in
contrast
to
drug
self-administration,
the
results
obtained
with
conditioned
place
preference
(CPP)
are
mixed.
Some
studies
have
found
that
physical
activity
decreases
stimulant
and
opiate
CPP
(Chen
et
al.,
2008;
Fontes-Ribeiro
et
al.,
2011;
Hosseini
et
al.,
2009;
Rozeske
et
al.,
2011;
Thanos
et
al.,
2010),
while
others
have
reported
either
an
increase
or
no
effect
(Geuzaine
and
Tirelli,
2014;
Mustroph
et
al.,
2011;
Smith
et
al.,
2008a).
Multiple
pro-
cedural
variations
among
these
studies
do
not
allow
for
a
cogent
explanation
for
the
discrepant
findings.
However,
one
notable
fea-
ture
is
that
all
of
the
mixed
results
are
clustered
into
studies
that
have
examined
cocaine
CPP
rather
than
amphetamine,
MDMA
or
morphine
CPP.
Since
cocaine
has
vasoconstrictive
properties
not
shared
by
these
other
drugs,
perhaps
differential
vascular
changes
induced
by
the
various
exercise
regimens
used
across
studies
may
play
a
role
in
the
lack
of
consistent
findings
using
cocaine.
Third,
the
beneficial
effect
of
physical
activity
may
not
be
con-
ferred
when
exercise
is
forced,
rather
than
voluntary,
during
the
later
stages
of
the
addiction
cycle
(Lynch
et
al.,
2013).
For
example,
when
rats
are
primed
with
cocaine
following
a
period
of
extinction,
forced
physical
activity
on
a
treadmill
increases
cocaine
seeking
(Thanos
et
al.,
2013).
However,
this
same
study
found
that
forced
physical
activity
decreases
cue-induced
reinstatement,
which
indi-
cates
the
physical
activity
differentially
activates
neural
systems
involved
in
relapse
to
either
cocaine
or
cocaine-associated
cues.
2.2.
Clinical
evidence
In
contrast
to
preclinical
work,
one
limitation
of
many
clinical
studies
relates
to
the
cross-sectional
designs
used
for
data
collec-
tion.
As
a
case
in
point,
it
is
known
from
cross-sectional
data
that
there
is
an
inverse
relation
between
physical
activity
and
smok-
ing
behavior
in
American
adolescents
(Wilson
et
al.,
2005).
While
this
might
suggest
that
interventions
that
promote
physical
activity
would
reduce
smoking
behavior,
there
are
at
least
two
important
shortcomings
with
this
logic.
First,
it
is
not
clear
if
physical
activity
precedes
or
results
from
a
reduction
in
smoking,
or
whether
the
relation
exists
due
to
a
third
factor
(e.g.,
peer
affiliation).
Second,
as
mentioned
previously,
since
physical
activity
and
smoking
are
incompatible
behaviors,
it
may
be
that
choosing
a
physical
activity
is
analogous
to
choosing
an
alternative
reinforcer
to
reduce
drug
use.
Such
an
outcome
does
not
necessarily
imply
that
the
rela-
tion
is
mediated
by
a
long-term
neurobiological
protection
from
tobacco
use.
Without
inducing
long-term
neuroadaptive
mecha-
nisms
that
protect
against
drug
abuse
vulnerability,
any
beneficial
effects
of
physical
activity
might
be
expected
to
be
transient
and
thus
relatively
ineffective
in
the
long-term.
In
one
review
of
the
literature
up
to
2011
(Zschucke
et
al.,
2012),
a
few
randomized
control
trials
(RCTs)
assessing
the
effects
of
physical
activity
were
found
to
be
effective
with
tobacco
smokers
attempting
to
quit;
otherwise
the
clinical
evidence
was
found
to
be
sparse.
Since
that
review,
several
new
reports
have
been
published

6
Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
to
provide
a
more
comprehensive
understanding
of
the
effect
of
physical
activity.
However,
relative
to
our
burgeoning
knowledge
in
the
preclinical
realm,
there
continues
to
be
a
lack
of
conclusive
information
from
clinical
studies
(Linke
and
Ussher,
2015).
For
the
most
part,
evidence
for
the
impact
of
physical
activity
on
drug
intake
comes
from
observational
studies,
including
both
cross-sectional
and
prospective
designs.
Survey-based
research
in
adolescent
populations
demonstrates
an
association
between
exer-
cise
and
drug
use,
with
higher
levels
of
physical
activity
related
to
lower
use
of
alcohol,
tobacco
and
marijuana
use
(Iannotti
et
al.,
2009;
Terry-McElrath
et
al.,
2011).
By
contrast,
distinct
from
exer-
cise
itself,
participation
in
athletic
team
activities
is
associated
with
higher
rates
of
alcohol
and
smokeless
tobacco
use
(Cych
et
al.,
2013;
Henchoz
et
al.,
2014),
although
participation
in
exercise
remains
associated
with
lower
rates
even
among
those
in
organized
athlet-
ics
(Terry-McElrath
et
al.,
2011).
These
findings
were
extended
in
a
longitudinal
study
which
showed
consistently
lower
substance
use
during
the
years
ensuing
after
high
school
and
early
adult
life
when
individuals
participate
in
exercise
(Terry-McElrath
et
al.,
2011).
Small
scale
studies
also
have
found
an
association
of
exercise
with
improved
outcomes
for
individuals
diagnosed
with
substance
use
disorders.
Some
early
studies
show
associations
between
higher
rates
of
contemporaneous
exercise
and
improved
outcomes
(Buchowski
et
al.,
2011;
Collingwood
et
al.,
1991;
Sinyor
et
al.,
1982;
Weinstock
et
al.,
2008),
and
a
recent
observational
study
of
mari-
juana
cessation
found
an
association
of
moderate
to
high
levels
of
physical
activity
with
lower
rates
of
relapse
compared
to
those
with
low
levels
of
physical
activity
(Irons
et
al.,
2014).
Further,
based
on
qualitative
interviews
of
former
and
relapsed
heroin
users
followed
up
after
treatment,
approximately
one-third
of
successful
abstain-
ers
mentioned
that
exercise
was
a
useful
method
for
facilitating
their
recovery
(Weiss
et
al.,
2014).
More
important,
as
the
“gold
standard”
for
efficacy
evaluation,
a
number
of
recent
studies
have
used
RCT
designs
(see
Table
2).
Unfortunately,
treatment
studies
to
date
have
shown
inconsistent
findings
about
the
impact
of
physical
activity
on
addictions,
likely
due
to
some
variation
in
quality
across
studies
(Linke
and
Ussher,
2015).
In
particular,
the
evidence
on
treatment
of
alcohol
abuse
has
been
mixed
(Giesen
et
al.,
2015).
A
recent
small
scale
study
found
exercise
to
facilitate
recovery
among
alcohol
dependent
patients
with
a
sedentary
lifestyle
(Brown
et
al.,
2014).
However,
other
studies
examining
the
effects
of
physical
activity
as
a
treat-
ment
for
heavy
alcohol
use
have
not
yielded
positive
findings
in
reducing
problematic
alcohol
use
(Kendzor
et
al.,
2008;
Weinstock
et
al.,
2014).
These
mixed
findings
parallel
the
preclinical
evidence
showing
that
physical
activity
does
not
reliably
decrease
volun-
tary
alcohol
consumption
(Crews
et
al.,
2004;
Ozburn
et
al.,
2008;
Werme
et
al.,
2002a)
and
they
highlight
the
need
for
determin-
ing
the
critical
conditions
under
which
treatment
of
alcohol
use
disorders
may
benefit
from
physical
activity.
With
tobacco
use,
several
reports
show
that
exercise
improves
tobacco
cessation
treatment
outcomes
(Bock
et
al.,
1999;
Horn
et
al.,
2011;
Marcus
et
al.,
1995,
2005;
Prochaska
et
al.,
2008).
Unfortu-
nately,
other
clinical
trials
have
yielded
no
significant
benefit
as
adjunct
cessation
therapies,
perhaps
because
they
were
under-
powered
(Abrantes
et
al.,
2014;
Ussher
et
al.,
2012,
2014).
The
apparent
beneficial
effect
on
smoking
cessation
observed
in
at
least
some
clinical
trials
may
be
due
to
a
diminution
in
cue-induced
crav-
ing
(Van
Rensburg
et
al.,
2009),
thus
reducing
the
relapse
rate.
In
two
recent
meta-analyses,
it
was
concluded
that
the
majority
of
studies
support
a
beneficial
effect
of
physical
activity
on
craving
and
withdrawal
symptoms,
at
least
for
a
duration
of
up
to
30-min
post-exercise
(Haasova
et
al.,
2013;
Roberts
et
al.,
2012).
Because
of
these
promising
(though
limited)
findings,
there
has
been
a
call
for
implementing
physical
activity
as
an
adjunct
for
smoking
cessation,
especially
among
women
(Linke
et
al.,
2013).
However,
there
is
a
lack
of
clarity
about
the
type
and
intensity
of
physical
activity
that
yields
the
optimal
outcome
on
smoking-related
behaviors
(Roberts
et
al.,
2012).
Further,
clinical
studies
of
exercise
for
tobacco
use
are
rare
and
need
larger
samples
with
greater
control
of
intervention
parameters
(Ussher
et
al.,
2014).
For
drugs
other
than
tobacco
and
alcohol,
studies
have
been
sparse.
A
multisite
study
is
underway
on
the
impact
of
exercise
on
stimulant
abuse
or
dependence
(Walker
et
al.,
2014).
In
other
recent
studies
among
methamphetamine
dependent
individuals
treated
in
either
inpatient
or
outpatient
settings,
moderate
resis-
tance
training
or
treadmill
walking/jogging
were
both
shown
to
improve
cardiovascular
health
within
8
weeks
(Dolezal
et
al.,
2014,
2013),
suggesting
that
different
methods
for
enhancing
physical
activity
can
be
tailored
to
different
settings
during
the
early
treat-
ment
phase.
No
specific
improvements
in
outcomes
have
been
demonstrated
in
RCTs
using
exercise
as
an
in-patient
treatment
for
drugs
other
than
tobacco
and
alcohol.
There
also
has
been
little
clinical
work
addressing
the
specific
role
for
exercise
in
substance
use
prevention.
This
is
partly
due
to
the
effort
and
expense
required
to
conduct
randomized
longi-
tudinal
studies
in
large
samples.
While
drug
use
as
an
outcome
measure
has
not
been
examined
thoroughly,
the
effect
of
physical
activity
has
been
examined
for
its
immediate
effects
on
childhood
executive
cognitive
functioning,
which
includes
processes
such
as
self-regulation
and
self-control.
Given
the
importance
of
execu-
tive
cognitive
functioning
in
predicting
later
life
addictive
disorders
and
multiple
social
outcomes
(Moffitt
et
al.,
2011),
studies
which
document
the
impact
of
physical
exercise
on
improved
executive
functioning
in
children
may
be
illustrative
of
the
potential
impact
of
exercise
on
proximal
indicators
of
drug
abuse
risk.
Two
recent
studies
with
pre-adolescent
school
age
children
show
a
modest
impact
of
aerobic
exercise
on
executive
functioning,
especially
for
more
intense
exercise
(Davis
et
al.,
2011;
Kamijo
et
al.,
2011).
However,
while
exercise
may
have
only
a
modest
direct
benefit
on
executive
functioning,
the
impact
may
be
enhanced
by
addi-
tional
interventions
such
as
mindfulness
training
(Diamond
and
Lee,
2011),
parental
modeling
(Pentz
and
Riggs,
2013)
or
musical
training
(Hudziak
et
al.,
2014).
Most
recently,
an
afterschool
exer-
cise
program
with
7
to
9
year
old
children
was
shown
to
enhance
executive
control
in
a
well-designed
trial
with
221
participants
(Hillman
et
al.,
2014).
In
this
study,
children
were
randomized
to
a
wait
list
control
or
to
the
9
month
intervention
group
who
par-
ticipated
in
daily
moderate
to
vigorous
physical
activities
designed
to
improve
aerobic
fitness.
Both
groups
showed
maturation
and
improvement
over
the
9
month
follow
up,
but
those
in
the
inter-
vention
group
showed
greater
improvement
in
incongruent
and
heterogeneous
tests
of
executive
function.
These
results
are
broadly
consistent
with
earlier
trials
of
using
exercise
to
enhance
executive
functioning
in
youth
(Chaddock-Heyman
et
al.,
2013;
Davis
et
al.,
2011;
Kamijo
et
al.,
2011;
Krafft
et
al.,
2014).
In
addition
to
biologically
plausible
pathways
for
exercise’s
role
in
reducing
drug
abuse
vulnerability,
one
must
also
consider
the
role
for
psychosocial
mediators
such
as
engagement
with
pro-social
peer
groups
and
exercise
as
an
alternative
coping
strategy.
Reduced
drug
abuse
vulnerability
may
be
mediated
by
improved
cognitive
functioning
which
itself
can
be
enhanced
with
physical
exercise
(Ploughman,
2008).
Physical
exercise
has
been
shown
to
enhance
cognitive
performance,
especially
school
and
reading
performance
(Etnier
et
al.,
2006;
Hillman
et
al.,
2009;
Reynolds
and
Nicolson,
2007),
which
suggests
another
potential
way
to
reduce
drug
abuse
vulnerability.
In
addition
to
enhancing
cognitive
decision-making,
physical
activity
may
serve
as
a
drug
abuse
preventive
intervention
by
improving
stress
reactivity
and
emotion
regulation.
While
acute
exercise
activates
stress-related
systems
as
measured
by
cortisol
levels,
repeated
exercise
reduces
stress
reactivity
as
measured
by

Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
7
Table
2
Clinical
studies
(2008–present)
assessing
the
effectiveness
of
physical
activity
on
drug
reward
in
humans.
Subjects
Design
Intervention
Test
Effect
Reference
Adult
female
and
male
tobacco
smokers
Randomassign
Isometric
exercise
Self-report
(palm
pilot)
↓Craving
↓Withdrawal
symptoms
Ussher
et
al.
(2009)
Adult
female
tobacco
smokers
Randomassign
Treadmill
(adjunct
to
nicotine
patch)
Smoking
status
(CO
level)
↔Abstinence
Williams
et
al.
(2010)
Teen
female
and
male
tobacco
smokers
Randomassign
of
schools
Pedometer
monitoring
(adjunct
to
NOT)
Smoking
status
(CO
level)
↓Relapse
in
males
↔Relapse
in
females
Horn
et
al.
(2011)
Adult
female
and
male
tobacco
smokers
Randomassign
Pedometer
monitoring
(adjunct
to
bupropion
and
nicotine
patch)
Smoking
status
(CO
level)
↓Relapse
Prochaska
et
al.
(2008)
Adult
female
and
male
tobacco
smokers
Randomassign
Exercise
(adjunct
to
cessation
protocol
and
nicotine
patch)
Smoking
status
(CO
level)
↔Abstinence
Abrantes
et
al.
(2014)
Adult
female
and
male
tobacco
smokers
Random
cross
over
Cycle
ergometer
Eye
track
to
smoke
cues
↓Craving
↓Cue
bias
Van
Rensburg
et
al.
(2009)
Adult
female
and
male
with
alcohol
dependence
Randomassign
Moderate
aerobic
exercise
Self-report
↓Heavy
drink
days
Brown
et
al.
(2014)
Young
adult
female
and
male
hazardous
alcohol
drinkers
Randomassign
Exercise
participation
with
contingency
management
Self-report
↔Alcohol
use
Weinstock
et
al.
(2014)
CO
=
carbon
monoxide;
NOT
=
Not-On-Tobacco
curriculum;
↓
=
decrease;
↔
=
no
change.
sympathetic
and
cardiovascular
arousal
(Huang
et
al.,
2013).
In
fourth
graders
tested
in
a
school
setting,
high-intensity
exercise
enhanced
salivary
cortisol
levels
compared
to
watching
a
movie;
however,
this
effect
was
obtained
in
female
students
only
(Budde
et
al.,
2010).
In
a
cross-sectional
study
among
adolescent
boys
and
girls,
compulsive
exercise
also
was
found
to
be
associated
with
emotion
regulation
strategies
(Goodwin
et
al.,
2012),
suggesting
that
physical
activity
may
promote
management
of
emotion.
RCT
studies
also
indicate
that
physical
activity
is
effective
in
reducing
symptoms
of
depression
and
anxiety
(Daley,
2008;
Ensari
et
al.,
2015).
Given
the
co-occurrence
of
affective
disorders,
stress-related
disorders
and
substance
use
disorders,
preventive
strategies
that
promote
physical
activity
early
in
life
may
be
especially
benefi-
cial
in
providing
a
broad
spectrum
benefit
to
overall
psychological
health.
3.
Neural
changes
mediate
the
protective
effect
of
physical
activity
As
discussed
previously,
one
may
view
physical
activity
as
a
rewarding
event,
at
least
for
some
individuals
(Azrin
et
al.,
2006;
Brene
et
al.,
2007).
Thus,
in
a
choice
situation
when
both
physical
activity
and
drugs
are
available
concomitantly,
physical
activity
may
substitute
for
drug
use.
However,
evidence
indicates
that
repeated
physical
activity
also
produces
more
enduring
neu-
robehavioral
alterations
that
outlast
the
choice
situation.
While
multiple
neural
mechanisms
are
undoubtedly
involved,
long-term
changes
in
trophic
factors
that
modulate
glial-neuronal
interac-
tions
controlling
neurochemical
transmission
and
stress
reactivity
may
be
especially
important.
In
particular,
specific
changes
in
lim-
bic
and
prefrontal
brain
regions
have
been
identified
as
mediating
the
link
between
physical
activity
and
reduced
drug
reward.
The
mPFC
is
an
integral
part
of
several
key
neurocircuits,
including
those
involved
in
drug
reward,
inhibitory
control
and
stress
reac-
tivity
(Butts
et
al.,
2011;
Goeders
and
Smith,
1983;
Koob
et
al.,
2014;
Perry
et
al.,
2011;
Segovia
et
al.,
2009;
see
Fig.
1),
Alterations
in
these
neurocircuits
may
mediate,
at
least
in
part,
the
effects
of
physical
activity
on
drug
abuse
vulnerability.
3.1.
Preclinical
evidence
Preclinical
evidence
indicates
that
repeated
physical
activity
produces
long-term
neuroadaptations
important
in
drug
abuse
vulnerability,
although
the
specific
conditions
under
which
either
sensitization
or
tolerance
occurs
remain
to
be
elucidated.
With
stimulants,
repeated
running
wheel
exercise
cross-sensitizes
to
cocaine
CPP
(Smith
et
al.,
2008a),
suggesting
that
physical
activity
and
stimulant
drugs
activate
a
common
reward
neural
circuitry.
In
contrast,
however,
rats
given
repeated
running
wheel
exercise
display
cross-tolerance
to
morphine
CPP
(Lett
et
al.,
2002).
Since
naloxone
blocks
the
rewarding
effect
of
wheel
running
(Lett
et
al.,
2001;
Vargas-Perez
et
al.,
2008),
these
latter
results
suggest
that
exercise-induced
release
of
endogenous
opioids
plays
a
role
in
the
cross-tolerance
to
morphine.
Importantly,
the
occurrence
of
sen-
sitization
or
tolerance
does
not
simply
reflect
a
difference
in
the
neural
mechanisms
underlying
drug-specific
reward
mechanisms,
as
both
sensitization
and
tolerance
have
been
reported
across
vari-
ous
drugs
(Czoty
et
al.,
2010;
Lett,
1989;
Lorrain
et
al.,
2000;
Zernig
et
al.,
2007).
Moreover,
within
the
same
study,
sensitization
or
tolerance
in
dopamine
transporter
(DAT)
function
can
occur
with
cocaine
self-administration,
depending
on
the
temporal
pattern
of
drug
intake
(Calipari
et
al.,
2013).
Thus,
for
translational
research,
it
will
be
important
to
determine
what
factors
are
important
for
producing
either
cross-tolerance
or
cross-sensitization
between
physical
activity
and
drug
reward.
In
a
recent
study
in
rats
(Morris
et
al.,
2012),
alterations
in
the
mesolimbic
reward
pathway
were
determined
using
intracra-
nial
self-stimulation
following
2
or
5
weeks
of
running
wheel
access.
Compared
to
their
pre-exercise
control
levels,
wheel
run-
ning
induced
a
leftward
shift
in
the
threshold
for
self-stimulation,
indicating
increased
sensitivity
of
the
reward
pathway.
While
this
outcome
might
imply
greater
risk
for
drug
abuse,
it
is
impor-
tant
to
point
out
that
animals
are
thought
to
regulate
their
drug
intake
around
some
optimal
reinforcement
or
satiation
threshold
(Lynch
and
Carroll,
2001;
Tsibulsky
and
Norman,
1999).
Perhaps
a
sensitized
mesocorticolimbic
reward
system
following
physical
activity
may
lower
the
reinforcement
or
satiety
threshold,
thus
leading
to
reduced
intake
compared
to
non-exercised
controls.
Alternatively,
since
drug
abuse
is
not
simply
a
problem
in
reward
sensitivity,
but
also
of
inhibitory
control
(Volkow
et
al.,
2011),
pre-
frontal
cortical
brain
regions
involved
in
inhibitory
control
may
be
strengthened
by
physical
activity,
thus
producing
a
concomitant
decrease
in
reward
sensitivity.
Consistent
with
this
latter
possibil-
ity,
running
exercise
increases
brain-derived
neurotrophic
factor
(BDNF)
in
mPFC
(Peterson
et
al.,
2014)
and
blunts
the
ability
of
3,4-
methylenedioexymethamphetamine
(MDMA)
to
evoke
dopamine
release
in
the
nucleus
accumbens
(NAc;
Chen
et
al.,
2008).
This
asso-
ciation
may
reflect
an
ability
of
BDNF
to
prevent
the
dysfunctional

8
Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
Fig.
1.
Schematic
diagram
showing
some
of
the
components
of
the
reward-,
inhibition-
and
stress-related
brain
systems
that
may
be
altered
by
repeated
physical
activity
and
which
are
important
in
drug
abuse
vulnerability.
Evidence
indicates
that
repeated
physical
activity
enhances
sensitivity
of
both
NAc
and
mPFC
function,
as
well
as
decreasing
CORT
levels
via
recruitment
of
the
hippocampal–prefrontal–amygdala
neurocircuitry.
The
combined
effects
are
reflected
as
an
increase
in
reward
sensitivity
(left
figure),
increase
in
inhibitory
control,
executive
cognitive
function
and
emotion
regulation
(middle
figure),
and
a
reduction
in
responsivity
to
stress
stimuli
(right
figure).
neuroadaptations
induced
in
mPFC
and
related
structures
follow-
ing
drug
exposure
(McGinty
et
al.,
2010).
As
mentioned
previously,
mPFC
is
known
to
be
a
prominent
structure
involved
in
addiction
propensity
based
on
its
role
in
reward,
stress
reactivity,
decision-
making
and
inhibitory
control
(Perry
et
al.,
2011;
Van
den
Oever
et
al.,
2010).
At
the
cellular
level,
a
host
of
exercise-induced
changes
have
been
identified
in
reward-,
impulsivity-
and
stress-relevant
brain
systems.
In
NAc,
long-term
access
to
a
running
wheel
increases
mRNA
for
both
dopamine
D2
and
delta
opioid
receptors
(Greenwood
et
al.,
2011).
Striatal
dopamine
turnover,
dopamine
D2
receptor
binding
and
dynorphin
mRNA
also
are
increased
with
running
in
rats
(Gilliam
et
al.,
1984;
Hattori
et
al.,
1994;
Werme
et
al.,
2000).
Similar
to
drugs
of
abuse,
there
is
an
overexpression
of
FosB
in
NAc
and
striatum
following
physical
activity
(Greenwood
et
al.,
2011;
Werme
et
al.,
2002b),
suggesting
that
both
physical
activity
and
drugs
activate
similar
neural
systems.
Consistent
with
this,
in
the
ventral
tegmental
area,
physical
activity
increases
mRNA
for
tyrosine
hydroxylase
(Greenwood
et
al.,
2011),
indicating
the
dopamine
signaling
to
accumbal
and
prefrontal
target
regions
are
enhanced.
In
mPFC
specifically,
running
wheel
activity
decreases
levels
of
the
phosphorylated
extracellular
signal-regulated
kinase
(pERK)
and
cocaine
seeking
following
a
period
of
abstinence
(Lynch
et
al.,
2010).
Taken
together,
these
findings
suggest
that
physical
activity
may
mimic
some
of
the
cellular
effects
of
chronic
drug
exposure,
thus
blunting
the
relative
impact
of
drugs
on
these
same
cellular
systems.
Despite
some
of
the
parallel
molecular
changes
induced
by
both
physical
activity
and
drugs
of
abuse,
the
picture
is
compli-
cated
due
to
differential
actions
on
neurogenesis
and
gliogenesis.
For
example,
within
the
hippocampal–prefrontal–amygdala
stress
axis,
repeated
exercise
training
increases
bromodeoxyuridine
immunoreactivity
in
hippocampus
of
rats
and
mice
(Crews
et
al.,
2004;
Engelmann
et
al.,
2013;
van
Praag
et
al.,
1999),
indicative
of
increased
neurogenesis.
In
addition,
physical
activity
enhances
pro-
liferation
of
glial
cells
in
mPFC
(Mandyam
et
al.,
2007).
In
contrast
to
physical
activity,
however,
stimulant
drugs
cause
neuronal
and
glial
death
in
mPFC
(Kadota
and
Kadota,
2004;
Kim
and
Mandyam,
2014;
Prudencio
et
al.,
2002).
These
findings
suggest
that
phys-
ical
activity
may
protect
against
drug-induced
cell
loss,
as
well
producing
neuroadaptive
changes
in
surviving
cells
that
counteract
the
impact
of
drug
exposure.
While
the
precise
mechanisms
involved
in
protecting
cell
death
are
not
known
fully,
amphetamine-induced
elevations
in
damaging
reactive
oxygen
species
and
protein
carbonyl
levels
in
hippocam-
pus
are
prevented
by
swim
exercise
(Segat
et
al.,
2014).
More
globally,
voluntary
running
wheel
activity
up-regulates
antioxida-
tive
enzymes
in
whole
brain
capillaries,
which
protects
against
the
neurotoxic
effects
of
methamphetamine
that
occur
via
a
dis-
ruption
in
the
blood–brain
barrier
(Toborek
et
al.,
2013).
While
physical
activity
may
not
protect
against
the
initial
brain
insult
to
dopaminergic
striatal
neurons
produced
by
methamphetamine,
wheel
running
accelerates
the
course
of
neuronal
restoration
fol-
lowing
damage
(O’Dell
and
Marshall,
2014).
Physical
activity
also
restores
the
loss
of
dopamine
neurons
in
midbrain
periaqueduc-
tal
gray
(PAG)
following
methamphetamine,
as
well
as
reversing
the
enhancement
in
methamphetamine
reinstatement,
which
nor-
mally
accompanies
this
cell
loss
(Sobieraj
et
al.,
2014).
These
latter
findings
indicate
that
physical
activity
may
reduce
relapse
rates
fol-
lowing
a
period
of
abstinence.
Further,
since
the
damaging
effects
of
chronic
drug
use
leads
to
various
cognitive
deficits
(Gould,
2010;
London
et
al.,
2014),
physical
activity
also
may
be
beneficial
for
ameliorating
these
cognitive
deficits.
3.2.
Clinical
studies
While
preclinical
evidence
implicates
a
host
of
cellular
and
anatomical
changes
in
brain
function
following
physical
activity,
moving
from
preclinical
to
clinical
research
has
been
hampered
by
a
dearth
of
information
about
the
potential
neural
media-
tors
that
link
physical
activity
and
drug
use
in
humans.
Although
modern
neuroimaging
technologies
are
available
to
address
this
question,
these
technologies
have
been
used
most
often
by
researchers
in
exercise
physiology,
rehabilitation,
obesity
and
learning/memory
(Boecker
et
al.,
2008a;
Tashiro
et
al.,
2008;
Voss
et
al.,
2011).
A
more
comprehensive
understanding
of
the
drug
abuse-related
neurobiological
adaptations
resulting
from
physi-
cal
activity
will
inform
researchers
and
practitioners
about
the
development
of
improved
drug
abuse
treatment
and
prevention
interventions.

Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
9
Anatomical
work
conducted
with
adults
indicates
that
exer-
cise
increases
overall
brain
volume,
including
white
and
gray
matter
(Colcombe
et
al.,
2006).
Regional
analyses
also
indi-
cate
an
increase
in
hippocampal
volume,
with
no
change
in
caudate
nucleus
(Erickson
et
al.,
2011),
which
may
be
impor-
tant
for
the
protection
of
learning
and
memory
processes.
In
contrast,
among
young
adults
(college
students),
moderate
exer-
cise
training
produces
only
modest
effects
on
gray/white
matter
densities
compared
to
non-exercised
controls
(Gondoh
et
al.,
2009).
While
these
morphometric
changes
appear
modest,
near-
infrared
spectroscopy
studies
indicate
that
acute
mild
exercise
increases
activation
of
prefrontal
cortical
regions
(Yanagisawa
et
al.,
2010),
indicating
that
more
functional
neuroimaging
analyses
are
needed.
Some
initial
clinical
work
suggests
that
physical
activity
may
alter
neurobiological
systems
underlying
reward-,
stress-
and
emotion-based
brain
systems.
In
healthy
athletes,
positron
emis-
sion
tomography
(PET)
reveals
that
strenuous
running
decreases
opioid
receptor
availability
preferentially
in
prefrontal
and
lim-
bic
brain
regions,
including
prefrontal/orbitofrontal
and
anterior
cingulate
cortices,
as
well
as
insula
and
parahippocampal
gyrus
(Boecker
et
al.,
2008b).
However,
this
same
study
found
that
no
change
in
opiate
receptor
availability
in
the
reward-related
NAc.
Further,
healthy
adult
volunteers
exposed
to
moderate
exercise
(30
min
of
treadmill
running)
show
no
effect
on
dopamine
receptor
availability
in
caudate
(Wang
et
al.,
2000),
a
region
thought
to
be
important
for
habit
formation.
Thus,
the
effects
of
physical
activ-
ity
observed
in
prefrontal
and
limbic
regions
may
be
related
more
with
emotion-
and
stress-related
brain
systems
than
with
reward
systems
per
se.
A
further
pathway
has
been
described
recently
in
which
exer-
cise
induces
increases
in
a
muscle
secreted
protein
called
irisin
(Bostrom
et
al.,
2012;
Kelly,
2012;
Novelle
et
al.,
2013).
Irisin,
in
turn,
is
implicated
in
hippocampal
neurogenesis
(Moon
et
al.,
2013),
suggesting
that
muscle
secreted
proteins
may
play
a
pivot
role
in
the
brain
changes
associated
with
exercise-induced
improvements
in
brain
function.
Neuroimaging
studies
have
also
shown
physical
activity
to
affect
brain
function
in
alcohol
and
tobacco
using
samples.
Heavy
alco-
hol
drinkers
who
report
high
levels
of
voluntary
exercise
also
show
greater
integrity
of
white
matter
connections
involving
ante-
rior
and
dorsal
brain
regions
compared
to
non-exercising
drinkers
(Karoly
et
al.,
2013).
Among
tobacco
dependent
smokers,
physi-
cal
activity
also
decreases
cue-induced
smoking
urge,
regardless
whether
they
are
undergoing
abstinence
or
not,
and
this
decrease
in
urge
is
correlated
with
fMRI
activations
of
orbitofrontal
cortex,
caudate
nucleus
and
hippocampal
gyrus
(Janse
Van
Rensburg
et
al.,
2009;
McClernon
et
al.,
2005).
While
these
studies
provide
prelimi-
nary
evidence
that
physical
activity
yields
functional
brain
changes
useful
for
treatment
of
addiction,
the
evidence
is
scant
for
drugs
of
abuse
other
than
alcohol
and
tobacco.
While
little
is
known
about
the
possible
preventive
effects
of
physical
activity,
evidence
indicates
that
the
exercise-induced
enhancement
of
executive
cognitive
functioning
in
youth
noted
previously
may
involve
recruitment
of
various
prefrontal
corti-
cal
regions
(Prakash
et
al.,
2015).
In
addition,
in
one
correlational
study,
aerobic
fitness
in
preadolescent
children
was
associated
with
increased
hippocampal
volume
and
memory
performance,
although
no
relation
to
NAc
volume
was
found
(Chaddock
et
al.,
2010).
A
mediational
model
indicated
that
the
correla-
tion
between
physical
fitness
and
memory
processing
in
that
study
was
mediated
by
differences
in
hippocampal
volume.
This
study
design
may
serve
as
a
useful
template
to
determine
the
role
of
physical
activity
on
abuse-related
processes
such
as
reward
sensitivity,
inhibitory
control,
stress
reactivity
and
emotion
regulation.
4.
Concluding
remarks
The
key
question
that
this
review
attempts
to
answer
is
whether
physical
activity
protects
against
drug
abuse
vulnerability.
Basic
science
studies
in
animal
models
of
drug
seeking
and
drug
use
risk
provide
generally
strong
experimental
evidence
to
conclude
that
physical
activity
reduces
the
acquisition
of
drug
using
behavior
and
facilitates
desistence.
Unfortunately,
human
studies
are
less
clear.
Studies
of
physical
activity
as
a
component
of
drug
abuse
treatment
are
suggestive,
with
the
clearest
link
for
tobacco
treatment.
Thus,
animal
models
demonstrating
that
exercise
facilitates
desistence
of
drug
use
are
partly
supported
by
the
treatment
data.
Some
of
the
difficulty
with
the
clinical
data
is
due
to
methodological
issues.
Imprecise
measures
of
physical
activity,
confounding
by
related
phenomena
such
as
“participation
in
organized
sports”,
and
rare
use
of
random
trial
experimental
designs
are
among
the
difficulties
and
weaknesses
in
the
clinical
research
to
date.
The
summary
is
that
data
are
“inconclusive”
regarding
the
salutary
impact
of
physical
exercise
on
drug
use
onset.
Yet,
intriguing
associations
of
physi-
cal
activity
with
enhanced
executive
control
in
children
suggests
a
possible
pathway
for
an
important
impact.
Based
on
the
evidence
to
date,
there
are
several
questions
that
may
be
informative
for
directing
both
preclinical
and
clinical
future
research.
While
preclinical
findings
at
the
behavioral
level
are
con-
vincing
in
demonstrating
the
effectiveness
of
physical
activity
in
reducing
drug
use
across
the
addiction
cycle,
there
is
considerable
uncertainty
about
the
neurobiological
mechanisms
that
mediate
this
protective
effect.
Although
a
number
of
cellular,
neurochemical
and
neuroanatomical
systems
have
been
identified,
particularly
in
mPFC,
results
linking
physical
activity
to
decreases
in
drug
use
are
virtually
all
correlational.
There
is
no
definitive
study
that
demon-
strates
that
any
specific
neurobiological
change
is
necessary
or
sufficient
to
induce
the
behavioral
change.
To
answer
this
ques-
tion,
it
will
be
important
to
utilize
new
strategies
that
manipulate
specific
mechanisms
in
the
presence
and
absence
of
physical
activ-
ity
in
order
to
determine
drug
use
outcomes.
The
use
of
conditional
knock-down,
knock-in
and
designer
receptors
exclusively
activated
by
designer
drugs
(DREADDS)
may
be
helpful
in
this
regard.
How-
ever,
even
these
state-of-the-art
technologies
are
limited
because
it
is
likely
that
more
than
one
pathway
mediates
the
relation
between
physical
activity
and
drug
use.
With
clinical
research,
not
only
are
there
gaps
in
our
neu-
robiological
understanding,
there
is
also
uncertainty
about
what
conditions
are
necessary
to
translate
the
strong
preclinical
evidence
into
practice.
To
date,
the
clinical
evidence
for
showing
beneficial
effects
of
physical
activity
is
not
decisive,
although
the
evidence
for
interventions
aimed
at
smoking
cessation
are
most
encourag-
ing.
Further
research
is
needed
to
determine
under
what
conditions
and
at
what
stage
of
development
physical
activity
will
be
effec-
tive
as
either
a
prevention
or
treatment
strategy.
In
this
regard,
preclinical
research
is
less
valuable,
because
animal
models
do
not
adequately
address
issues
such
as
peer
influence
and
alternative
coping
strategies
as
psychosocial
mediators.
In
addition,
more
focus
on
longitudinal
and
mediational
analyses
is
needed
to
determine
the
pathways
that
may
link
physical
activity
to
reductions
in
drug
use
in
humans.
To
address
further
some
of
the
methodological
issues
in
clinical
research,
biomarkers
of
strenuous
physical
activity
also
are
needed
to
augment
the
self-report
measures
generally
relied
upon.
Robust
signatures
of
improved
fitness
that
could
be
used
in
population
settings
would
facilitate
clinical
research
and
have
been
the
target
for
research
development
at
the
NIH
(National
Institutes
of
Health,
2015a).
In
addition,
detailed
longitudinal
studies
of
development
such
as
the
recently
announced
NIH
Adolescent
Brain
Cognitive
Development
study
may
provide
information
about
the
relation-
ship
of
physical
activity
to
brain
development
in
ways
which
could

10
Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
help
to
determine
the
mechanisms
for
impact
of
physical
exer-
cise
on
mental
processes
such
as
drug
abuse
(National
Institutes
of
Health,
2015b).
Given
the
robust
preclinical
findings,
additional
basic
and
clinical
research
using
novel
approaches
which
address
design
weaknesses
are
encouraged.
Contributors
Both
authors
contributed
to
the
literature
search
and
writing
of
this
article
and
both
authors
have
approved
the
final
article.
Disclaimer
The
findings
and
conclusions
of
this
study
are
those
of
the
authors
and
do
not
necessarily
reflect
the
views
of
the
National
Institute
on
Drug
Abuse,
the
National
Institutes
of
Health
or
the
U.S.
Department
of
Health
and
Human
Services.
References
Abrantes,
A.M.,
Bloom,
E.L.,
Strong,
D.R.,
Riebe,
D.,
Marcus,
B.H.,
Desaulneirs,
J.,
Fokas,
K.,
Brown,
R.A.,
2014.
A
preliminary
randomized
controlled
trial
of
a
behavioral
exercise
intervention
for
smoking
cessation.
Nicotine
Tob.
Res.
16,
1094–1103.
Ainslie,
P.N.,
Reilly,
T.,
Maclaren,
D.P.,
Campbell,
I.T.,
2005.
Changes
in
plasma
lipids
and
lipoproteins
following
10-days
of
prolonged
walking:
influence
of
age
and
relationship
to
physical
activity
level.
Ergonomics
48,
1352–1364.
Azrin,
N.H.,
Ehle,
C.T.,
Beaumont,
A.L.,
2006.
Physical
exercise
as
a
reinforcer
to
promote
calmness
of
an
ADHD
child.
Behav.
Mod.
30,
564–570.
Bardo,
M.T.,
Hammer
Jr.,
R.P.,
1991.
Autoradiographic
localization
of
dopamine
D1
and
D2
receptors
in
rat
nucleus
accumbens:
resistance
to
differential
rearing
conditions.
Neuroscience
45,
281–290.
Bensimhon,
D.R.,
Kraus,
W.E.,
Donahue,
M.P.,
2006.
Obesity
and
physical
activity:
a
review.
Am.
Heart
J.
151,
598–603.
Bock,
B.C.,
Marcus,
B.H.,
King,
T.K.,
Borrelli,
B.,
Roberts,
M.R.,
1999.
Exercise
effects
on
withdrawal
and
mood
among
women
attempting
smoking
cessation.
Addict.
Behav.
24,
399–410.
Boecker,
H.,
Henriksen,
G.,
Sprenger,
T.,
Miederer,
I.,
Willoch,
F.,
Valet,
M.,
Berthele,
A.,
Tolle,
T.R.,
2008a.
Positron
emission
tomography
ligand
activation
studies
in
the
sports
sciences:
measuring
neurochemistry
in
vivo.
Methods
(San
Diego,
Calif)
45,
307–318.
Boecker,
H.,
Sprenger,
T.,
Spilker,
M.E.,
Henriksen,
G.,
Koppenhoefer,
M.,
Wagner,
K.J.,
Valet,
M.,
Berthele,
A.,
Tolle,
T.R.,
2008b.
The
runner’s
high:
opioidergic
mechanisms
in
the
human
brain.
Cereb.
Cortex
(New
York,
NY:
1991)
18,
2523–2531.
Bostrom,
P.,
Wu,
J.,
Jedrychowski,
M.P.,
Korde,
A.,
Ye,
L.,
Lo,
J.C.,
Rasbach,
K.A.,
Bostrom,
E.A.,
Choi,
J.H.,
Long,
J.Z.,
Kajimura,
S.,
Zingaretti,
M.C.,
Vind,
B.F.,
Tu,
H.,
Cinti,
S.,
Hojlund,
K.,
Gygi,
S.P.,
Spiegelman,
B.M.,
2012.
A
PGC1-alpha-dependent
myokine
that
drives
brown-fat-like
development
of
white
fat
and
thermogenesis.
Nature
481,
463–468.
Brene,
S.,
Bjornebekk,
A.,
Aberg,
E.,
Mathe,
A.A.,
Olson,
L.,
Werme,
M.,
2007.
Running
is
rewarding
and
antidepressive.
Physiol.
Behav.
92,
136–140.
Brown,
T.,
Summerbell,
C.,
2009.
Systematic
review
of
school-based
interventions
that
focus
on
changing
dietary
intake
and
physical
activity
levels
to
prevent
childhood
obesity:
an
update
to
the
obesity
guidance
produced
by
the
National
Institute
for
Health
and
Clinical
Excellence.
Obes.
Rev.
10,
110–141.
Brown,
H.E.,
Pearson,
N.,
Braithwaite,
R.E.,
Brown,
W.J.,
Biddle,
S.J.,
2013.
Physical
activity
interventions
and
depression
in
children
and
adolescents:
a
systematic
review
and
meta-analysis.
Sports
Med.
43,
195–206.
Brown,
R.A.,
Abrantes,
A.M.,
Minami,
H.,
Read,
J.P.,
Marcus,
B.H.,
Jakicic,
J.M.,
Strong,
D.R.,
Dubreuil,
M.E.,
Gordon,
A.A.,
Ramsey,
S.E.,
Kahler,
C.W.,
Stuart,
G.L.,
2014.
A
preliminary,
randomized
trial
of
aerobic
exercise
for
alcohol
dependence.
J.
Subst.
Abuse
Treat.
47,
1–9.
Buchowski,
M.S.,
Meade,
N.N.,
Charboneau,
E.,
Park,
S.,
Dietrich,
M.S.,
Cowan,
R.L.,
Martin,
P.R.,
2011.
Aerobic
exercise
training
reduces
cannabis
craving
and
use
in
non-treatment
seeking
cannabis-dependent
adults.
PLoS
ONE
6,
e17465.
Budde,
H.,
Windisch,
C.,
Kudielka,
B.M.,
Voelcker-Rehage,
C.,
2010.
Saliva
cortisol
in
school
children
after
acute
physical
exercise.
Neurosci.
Lett.
483,
16–19.
Butts,
K.A.,
Weinberg,
J.,
Young,
A.H.,
Phillips,
A.G.,
2011.
Glucocorticoid
receptors
in
the
prefrontal
cortex
regulate
stress-evoked
dopamine
efflux
and
aspects
of
executive
function.
Proc.
Natl.
Acad.
Sci.
U.S.A.
108,
18459–18464.
Calipari,
E.S.,
Ferris,
M.J.,
Zimmer,
B.A.,
Roberts,
D.C.,
Jones,
S.R.,
2013.
Temporal
pattern
of
cocaine
intake
determines
tolerance
vs
sensitization
of
cocaine
effects
at
the
dopamine
transporter.
Neuropsychopharmacology
38,
2385–2392.
Chaddock,
L.,
Erickson,
K.I.,
Prakash,
R.S.,
Kim,
J.S.,
Voss,
M.W.,
Vanpatter,
M.,
Pontifex,
M.B.,
Raine,
L.B.,
Konkel,
A.,
Hillman,
C.H.,
Cohen,
N.J.,
Kramer,
A.F.,
2010.
A
neuroimaging
investigation
of
the
association
between
aerobic
fitness,
hippocampal
volume,
and
memory
performance
in
preadolescent
children.
Brain
Res.
1358,
172–183.
Chaddock-Heyman,
L.,
Erickson,
K.I.,
Voss,
M.W.,
Knecht,
A.M.,
Pontifex,
M.B.,
Castelli,
D.M.,
Hillman,
C.H.,
Kramer,
A.F.,
2013.
The
effects
of
physical
activity
on
functional
MRI
activation
associated
with
cognitive
control
in
children:
a
randomized
controlled
intervention.
Front.
Hum.
Neurosci.
7,
72.
Chen,
H.I.,
Kuo,
Y.M.,
Liao,
C.H.,
Jen,
C.J.,
Huang,
A.M.,
Cherng,
C.G.,
Su,
S.W.,
Yu,
L.,
2008.
Long-term
compulsive
exercise
reduces
the
rewarding
efficacy
of
3,4-methylenedioxymethamphetamine.
Behav.
Brain
Res.
187,
185–189.
Colcombe,
S.J.,
Erickson,
K.I.,
Scalf,
P.E.,
Kim,
J.S.,
Prakash,
R.,
McAuley,
E.,
Elavsky,
S.,
Marquez,
D.X.,
Hu,
L.,
Kramer,
A.F.,
2006.
Aerobic
exercise
training
increases
brain
volume
in
aging
humans.
J.
Gerontol.,
A:
Biol.
Sci.
Med.
Sci.
61,
1166–1170.
Collingwood,
T.R.,
Reynolds,
R.,
Kohl,
H.W.,
Smith,
W.,
Sloan,
S.,
1991.
Physical
fitness
effects
on
substance
abuse
risk
factors
and
use
patterns.
J.
Drug
Educ.
21,
73–84.
Comer,
S.D.,
Lac,
S.T.,
Wyvell,
C.L.,
Carroll,
M.E.,
1996.
Combined
effects
of
buprenorphine
and
a
nondrug
alternative
reinforcer
on
i.v.
cocaine
self-administration
in
rats
maintained
under
FR
schedules.
Psychopharmacology
125,
355–360.
Cosgrove,
K.P.,
Hunter,
R.G.,
Carroll,
M.E.,
2002.
Wheel-running
attenuates
intravenous
cocaine
self-administration
in
rats:
sex
differences.
Pharmacol.
Biochem.
Behav.
73,
663–671.
Cotman,
C.W.,
Berchtold,
N.C.,
2007.
Physical
activity
and
the
maintenance
of
cognition:
learning
from
animal
models.
Alzheimers
Dement.
3,
S30–S37.
Craig,
S.B.,
Bandini,
L.G.,
Lichtenstein,
A.H.,
Schaefer,
E.J.,
Dietz,
W.H.,
1996.
The
impact
of
physical
activity
on
lipids,
lipoproteins,
and
blood
pressure
in
preadolescent
girls.
Pediatrics
98,
389–395.
Crews,
F.T.,
Nixon,
K.,
Wilkie,
M.E.,
2004.
Exercise
reverses
ethanol
inhibition
of
neural
stem
cell
proliferation.
Alcohol
(Fayetteville,
NY)
33,
63–71.
Cych,
P.,
Kosendiak,
A.,
Kalwa,
M.,
Kosendiak,
J.,
2013.
Physical
activity
versus
alcohol
consumption
among
teenagers
in
chosen
middle
schools
from
cities
central
and
south-western
Poland.
Adv.
Clin.
Exp.
Med.
22,
273–281.
Czoty,
P.W.,
Martelle,
J.L.,
Nader,
M.A.,
2010.
Effects
of
chronic
d-amphetamine
administration
on
the
reinforcing
strength
of
cocaine
in
rhesus
monkeys.
Psychopharmacology
209,
375–382.
Daley,
A.,
2008.
Exercise
and
depression:
a
review
of
reviews.
J.
Clin.
Psychol.
Med.
Settings
15,
140–147.
Davis,
C.L.,
Tomporowski,
P.D.,
McDowell,
J.E.,
Austin,
B.P.,
Miller,
P.H.,
Yanasak,
N.E.,
Allison,
J.D.,
Naglieri,
J.A.,
2011.
Exercise
improves
executive
function
and
achievement
and
alters
brain
activation
in
overweight
children:
a
randomized,
controlled
trial.
Health
Psychol.
30,
91–98.
Diamond,
A.,
Lee,
K.,
2011.
Interventions
shown
to
aid
executive
function
development
in
children
4
to
12
years
old.
Science
333,
959–964.
Diamond,
M.,
Rosenzweig,
M.R.,
Krech,
D.,
1965.
Relationships
between
body
weight
and
skull
development
in
rats
raised
in
enriched
and
impoverished
conditions.
J.
Exp.
Zool.
160,
29–35.
Dolezal,
B.A.,
Chudzynski,
J.,
Storer,
T.W.,
Abrazado,
M.,
Penate,
J.,
Mooney,
L.,
Dickerson,
D.,
Rawson,
R.A.,
Cooper,
C.B.,
2013.
Eight
weeks
of
exercise
training
improves
fitness
measures
in
methamphetamine-dependent
individuals
in
residential
treatment.
J.
Addict.
Med.
7,
122–128.
Dolezal,
B.A.,
Chudzynski,
J.,
Dickerson,
D.,
Mooney,
L.,
Rawson,
R.A.,
Garfinkel,
A.,
Cooper,
C.B.,
2014.
Exercise
training
improves
heart
rate
variability
after
methamphetamine
dependency.
Med.
Sci.
Sports
Exerc.
46,
1057–1066.
Eckert,
M.J.,
Abraham,
W.C.,
2013.
Effects
of
environmental
enrichment
exposure
on
synaptic
transmission
and
plasticity
in
the
hippocampus.
Curr.
Top.
Behav.
Neurosci.
15,
165–187.
Engelmann,
A.J.,
Aparicio,
M.B.,
Kim,
A.,
Sobieraj,
J.C.,
Yuan,
C.J.,
Grant,
Y.,
Mandyam,
C.D.,
2013.
Chronic
wheel
running
reduces
maladaptive
patterns
of
methamphetamine
intake:
regulation
by
attenuation
of
methamphetamine-induced
neuronal
nitric
oxide
synthase.
Brain
Struct.
Funct.
219,
657–672.
Ensari,
I.,
Greenlee,
T.A.,
Motl,
R.W.,
Petruzzello,
S.J.,
2015.
Meta-analysis
of
acute
exercise
effects
on
state
anxiety:
an
update
of
randomized
controlled
trials
over
the
past
25
years.
Depress.
Anxiety,
1–11,
http://dx.doi.org/10.1002/da.
22370
Erickson,
K.I.,
Voss,
M.W.,
Prakash,
R.S.,
Basak,
C.,
Szabo,
A.,
Chaddock,
L.,
Kim,
J.S.,
Heo,
S.,
Alves,
H.,
White,
S.M.,
Wojcicki,
T.R.,
Mailey,
E.,
Vieira,
V.J.,
Martin,
S.A.,
Pence,
B.D.,
Woods,
J.A.,
McAuley,
E.,
Kramer,
A.F.,
2011.
Exercise
training
increases
size
of
hippocampus
and
improves
memory.
Proc.
Natl.
Acad.
Sci.
U.S.A.
108,
3017–3022.
Etnier,
J.L.,
Nowell,
P.M.,
Landers,
D.M.,
Sibley,
B.A.,
2006.
A
meta-regression
to
examine
the
relationship
between
aerobic
fitness
and
cognitive
performance.
Brain
Res.
Rev.
52,
119–130.
Fontes-Ribeiro,
C.A.,
Marques,
E.,
Pereira,
F.C.,
Silva,
A.P.,
Macedo,
T.R.,
2011.
exercise
prevent
addiction?
Curr.
Neuropharmacol.
9
(May),
45–48.
Gallego,
X.,
Cox,
R.J.,
Funk,
E.,
Foster,
R.A.,
Ehringer,
M.A.,
2015.
Voluntary
exercise
decreases
ethanol
preference
and
consumption
in
C57BL/6
adolescent
mice:
sex
differences
and
hippocampal
BDNF
expression.
Physiol.
Behav.
138,
28–36.
Gelfo,
F.,
De
Bartolo,
P.,
Giovine,
A.,
Petrosini,
L.,
Leggio,
M.G.,
2009.
Layer
and
regional
effects
of
environmental
enrichment
on
the
pyramidal
neuron
morphology
of
the
rat.
Neurobiol.
Learn.
Mem.
91,
353–365.
Geuzaine,
A.,
Tirelli,
E.,
2014.
Wheel-running
mitigates
psychomotor
sensitization
initiation
but
not
post-sensitization
conditioned
activity
and
conditioned
place
preference
induced
by
cocaine
in
mice.
Behav.
Brain
Res.
262,
57–67.
Giesen,
E.S.,
Deimel,
H.,
Bloch,
W.,
2015.
Clinical
exercise
interventions
in
alcohol
use
disorders:
a
systematic
review.
J.
Subst.
Abuse
Treat.
52,
1–9.

Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
11
Gilliam,
P.E.,
Spirduso,
W.W.,
Martin,
T.P.,
Walters,
T.J.,
Wilcox,
R.E.,
Farrar,
R.P.,
1984.
The
effects
of
exercise
training
on
[3H]-spiperone
binding
in
rat
striatum.
Pharmacol.
Biochem.
Behav.
20,
863–867.
Goeders,
N.E.,
Smith,
J.E.,
1983.
Cortical
dopaminergic
involvement
in
cocaine
reinforcement.
Science
221,
773–775.
Gondoh,
Y.,
Sensui,
H.,
Kinomura,
S.,
Fukuda,
H.,
Fujimoto,
T.,
Masud,
M.,
Nagamatsu,
T.,
Tamaki,
H.,
Takekura,
H.,
2009.
Effects
of
aerobic
exercise
training
on
brain
structure
and
psychological
well-being
in
young
adults.
J.
Sports
Med.
Phys.
Fit.
49,
129–135.
Goodwin,
H.,
Haycraft,
E.,
Meyer,
C.,
2012.
The
relationship
between
compulsive
exercise
and
emotion
regulation
in
adolescents.
Br.
J.
Health
Psychol.
17,
699–710.
Gould,
T.J.,
2010.
Addiction
and
cognition.
Addict.
Sci.
Clin.
Pract.
5,
4–14.
Greenwood,
B.N.,
Foley,
T.E.,
Le,
T.V.,
Strong,
P.V.,
Loughridge,
A.B.,
Day,
H.E.,
Fleshner,
M.,
2011.
Long-term
voluntary
wheel
running
is
rewarding
and
produces
plasticity
in
the
mesolimbic
reward
pathway.
Behav.
Brain
Res.
217,
354–362.
Griffin,
K.W.,
Botvin,
G.J.,
2010.
Evidence-based
interventions
for
preventing
substance
use
disorders
in
adolescents.
Child
Adolesc.
Psychiatr.
Clin.
N.
Am.
19,
505–526.
Haasova,
M.,
Warren,
F.C.,
Ussher,
M.,
Janse
Van
Rensburg,
K.,
Faulkner,
G.,
Cropley,
M.,
Byron-Daniel,
J.,
Everson-Hock,
E.S.,
Oh,
H.,
Taylor,
A.H.,
2013.
The
acute
effects
of
physical
activity
on
cigarette
cravings:
systematic
review
and
meta-analysis
with
individual
participant
data.
Addiction
(Abingdon,
England)
108,
26–37.
Hansen,
W.B.,
2010.
Current
and
future
directions
in
elementary
school
drug
prevention.
J.
Drug
Educ.
40,
91–98.
Hattori,
S.,
Naoi,
M.,
Nishino,
H.,
1994.
Striatal
dopamine
turnover
during
treadmill
running
in
the
rat:
relation
to
the
speed
of
running.
Brain
Res.
Bull.
35,
41–49.
Heishman,
S.J.,
Schuh,
K.J.,
Schuster,
C.R.,
Henningfield,
J.E.,
Goldberg,
S.R.,
2000.
Reinforcing
and
subjective
effects
of
morphine
in
human
opioid
abusers:
effect
of
dose
and
alternative
reinforcer.
Psychopharmacology
148,
272–280.
Henchoz,
Y.,
Dupuis,
M.,
Deline,
S.,
Studer,
J.,
Baggio,
S.,
N’Goran,
A.A.,
Daeppen,
J.B.,
Gmel,
G.,
2014.
Associations
of
physical
activity
and
sport
and
exercise
with
at-risk
substance
use
in
young
men:
a
longitudinal
study.
Prev.
Med.
64,
27–31.
Hillman,
C.H.,
Pontifex,
M.B.,
Raine,
L.B.,
Castelli,
D.M.,
Hall,
E.E.,
Kramer,
A.F.,
2009.
The
effect
of
acute
treadmill
walking
on
cognitive
control
and
academic
achievement
in
preadolescent
children.
Neuroscience
159,
1044–1054.
Hillman,
C.H.,
Pontifex,
M.B.,
Castelli,
D.M.,
Khan,
N.A.,
Raine,
L.B.,
Scudder,
M.R.,
Drollette,
E.S.,
Moore,
R.D.,
Wu,
C.T.,
Kamijo,
K.,
2014.
Effects
of
the
FITKids
randomized
controlled
trial
on
executive
control
and
brain
function.
Pediatrics
134,
e1063–e1071.
Holley,
J.,
Crone,
D.,
Tyson,
P.,
Lovell,
G.,
2011.
The
effects
of
physical
activity
on
psychological
well-being
for
those
with
schizophrenia:
a
systematic
review.
Br.
J.
Child
Psychol.
50,
84–105.
Horn,
K.,
Dino,
G.,
Branstetter,
S.A.,
Zhang,
J.,
Noerachmanto,
N.,
Jarrett,
T.,
Taylor,
M.,
2011.
Effects
of
physical
activity
on
teen
smoking
cessation.
Pediatrics
128,
e801–e811.
Hosseini,
M.,
Alaei,
H.A.,
Naderi,
A.,
Sharifi,
M.R.,
Zahed,
R.,
2009.
Treadmill
exercise
reduces
self-administration
of
morphine
in
male
rats.
Pathophysiology
16,
3–7.
Huang,
C.J.,
Webb,
H.E.,
Zourdos,
M.C.,
Acevedo,
E.O.,
2013.
Cardiovascular
reactivity,
stress,
and
physical
activity.
Front.
Physiol.
4,
314.
Hudziak,
J.J.,
Albaugh,
M.D.,
Ducharme,
S.,
Karama,
S.,
Spottswood,
M.,
Crehan,
E.,
Evans,
A.C.,
Botteron,
K.N.,
2014.
Cortical
thickness
maturation
and
duration
of
music
training:
health-promoting
activities
shape
brain
development.
J.
Am.
Acad.
Child
Adolesc.
Psychiatry
53,
1153–1161
(e1152).
Iannotti,
R.J.,
Kogan,
M.D.,
Janssen,
I.,
Boyce,
W.F.,
2009.
Patterns
of
adolescent
physical
activity,
screen-based
media
use,
and
positive
and
negative
health
indicators
in
the
U.S.
and
Canada.
J.
Adolesc.
Health
44,
493–499.
Irons,
J.G.,
Babson,
K.A.,
Bergeria,
C.L.,
Bonn-Miller,
M.O.,
2014.
Physical
activity
and
cannabis
cessation.
Am.
J.
Addict.
23,
485–492.
Janse
Van
Rensburg,
K.,
Taylor,
A.,
Hodgson,
T.,
Benattayallah,
A.,
2009.
Acute
exercise
modulates
cigarette
cravings
and
brain
activation
in
response
to
smoking-related
images:
an
fMRI
study.
Psychopharmacology
203,
589–598.
Johnson,
M.W.,
Bickel,
W.K.,
Kirshenbaum,
A.P.,
2004.
Substitutes
for
tobacco
smoking:
a
behavioral
economic
analysis
of
nicotine
gum,
denicotinized
cigarettes,
and
nicotine-containing
cigarettes.
Drug
Alcohol
Depend.
74,
253–264.
Kadota,
T.,
Kadota,
K.,
2004.
Neurotoxic
morphological
changes
induced
in
the
medial
prefrontal
cortex
of
rats
behaviorally
sensitized
to
methamphetamine.
Arch.
Histol.
Cytol.
67,
241–251.
Kamijo,
K.,
Pontifex,
M.B.,
O’Leary,
K.C.,
Scudder,
M.R.,
Wu,
C.T.,
Castelli,
D.M.,
Hillman,
C.H.,
2011.
The
effects
of
an
afterschool
physical
activity
program
on
working
memory
in
preadolescent
children.
Dev.
Sci.
14,
1046–1058.
Kanarek,
R.B.,
Marks-Kaufman,
R.,
D’Anci,
K.E.,
Przypek,
J.,
1995.
Exercise
attenuates
oral
intake
of
amphetamine
in
rats.
Pharmacol.
Biochem.
Behav.
51,
725–729.
Karoly,
H.C.,
Stevens,
C.J.,
Thayer,
R.E.,
Magnan,
R.E.,
Bryan,
A.D.,
Hutchison,
K.E.,
2013.
Aerobic
exercise
moderates
the
effect
of
heavy
alcohol
consumption
on
white
matter
damage.
Alcohol.
Clin.
Exp.
Res.
37,
1508–1515.
Kelly,
D.P.,
2012.
Medicine.
Irisin,
light
my
fire.
Science
336,
42–43.
Kendzor,
D.E.,
Dubbert,
P.M.,
Olivier,
J.,
Businelle,
M.S.,
Grothe,
K.B.,
2008.
The
influence
of
physical
activity
on
alcohol
consumption
among
heavy
drinkers
participating
in
an
alcohol
treatment
intervention.
Addict.
Behav.
33,
1337–1343.
Kim,
A.,
Mandyam,
C.D.,
2014.
Methamphetamine
affects
cell
proliferation
in
the
medial
prefrontal
cortex:
a
new
niche
for
toxicity.
Pharmacol.
Biochem.
Behav.
126,
90–96.
Kobilo,
T.,
Liu,
Q.R.,
Gandhi,
K.,
Mughal,
M.,
Shaham,
Y.,
van
Praag,
H.,
2011.
Running
is
the
neurogenic
and
neurotrophic
stimulus
in
environmental
enrichment.
Learn.
Mem.
18,
605–609.
Koob,
G.F.,
Kenneth
Lloyd,
G.,
Mason,
B.J.,
2009.
Development
of
pharmacotherapies
for
drug
addiction:
a
Rosetta
stone
approach.
Nat.
Rev.
Drug
Discov.
8,
500–515.
Koob,
G.F.,
Buck,
C.L.,
Cohen,
A.,
Edwards,
S.,
Park,
P.E.,
Schlosburg,
J.E.,
Schmeichel,
B.,
Vendruscolo,
L.F.,
Wade,
C.L.,
Whitfield
Jr.,
T.W.,
George,
O.,
2014.
Addiction
as
a
stress
surfeit
disorder.
Neuropharmacology
76
(Pt
B),
370–382.
Krafft,
C.E.,
Schwarz,
N.F.,
Chi,
L.,
Weinberger,
A.L.,
Schaeffer,
D.J.,
Pierce,
J.E.,
Rodrigue,
A.L.,
Yanasak,
N.E.,
Miller,
P.H.,
Tomporowski,
P.D.,
Davis,
C.L.,
McDowell,
J.E.,
2014.
An
8-month
randomized
controlled
exercise
trial
alters
brain
activation
during
cognitive
tasks
in
overweight
children.
Obesity
22,
232–242.
Lacy,
R.T.,
Strickland,
J.C.,
Brophy,
M.K.,
Witte,
M.A.,
Smith,
M.A.,
2014.
Exercise
decreases
speedball
self-administration.
Life
Sci.
114,
86–92.
Lenoir,
M.,
Ahmed,
S.H.,
2008.
Supply
of
a
nondrug
substitute
reduces
escalated
heroin
consumption.
Neuropsychopharmacology
33,
2272–2282.
Lett,
B.T.,
Grant,
V.L.,
Koh,
M.T.,
2001.
Naloxone
attenuates
the
conditioned
place
preference
induced
by
wheel
running
in
rats.
Physiol.
Behav.
72,
355–358.
Lett,
B.T.,
Grant,
V.L.,
Koh,
M.T.,
Flynn,
G.,
2002.
Prior
experience
with
wheel
running
produces
cross-tolerance
to
the
rewarding
effect
of
morphine.
Pharmacol.
Biochem.
Behav.
72,
101–105.
Lett,
B.T.,
1989.
Repeated
exposures
intensify
rather
than
diminish
the
rewarding
effects
of
amphetamine,
morphine,
and
cocaine.
Psychopharmacology
98,
357–362.
Linke,
S.E.,
Ussher,
M.,
2015.
Exercise-based
treatments
for
substance
use
disorders:
evidence,
theory,
and
practicality.
Am.
J.
Drug
Alcohol
Abuse
41,
7–15.
Linke,
S.E.,
Ciccolo,
J.T.,
Ussher,
M.,
Marcus,
B.H.,
2013.
Exercise-based
smoking
cessation
interventions
among
women.
Women’s
Health
(London,
England)
9,
69–84.
London,
E.D.,
Kohno,
M.,
Morales,
A.M.,
Ballard,
M.E.,
2014.
Chronic
methamphetamine
abuse
and
corticostriatal
deficits
revealed
by
neuroimaging.
Brain
Res.
104,
734–741.
Lorrain,
D.S.,
Arnold,
G.M.,
Vezina,
P.,
2000.
Previous
exposure
to
amphetamine
increases
incentive
to
obtain
the
drug:
long-lasting
effects
revealed
by
the
progressive
ratio
schedule.
Beahav.
Brain
Res.
107,
9–19.
Lynch,
W.J.,
Carroll,
M.E.,
2001.
Regulation
of
drug
intake.
Exp.
Clin.
Psychopharmacol.
9,
131–143.
Lynch,
W.J.,
Piehl,
K.B.,
Acosta,
G.,
Peterson,
A.B.,
Hemby,
S.E.,
2010.
Aerobic
exercise
attenuates
reinstatement
of
cocaine-seeking
behavior
and
associated
neuroadaptations
in
the
prefrontal
cortex.
Biol.
Psychiatry
68,
774–777.
Lynch,
W.J.,
Peterson,
A.B.,
Sanchez,
V.,
Abel,
J.,
Smith,
M.A.,
2013.
Exercise
as
a
novel
treatment
for
drug
addiction:
a
neurobiological
and
stage-dependent
hypothesis.
Neurosci.
Biobehav.
Rev.
37,
1622–1644.
Mandyam,
C.D.,
Wee,
S.,
Eisch,
A.J.,
Richardson,
H.N.,
Koob,
G.F.,
2007.
Methamphetamine
self-administration
and
voluntary
exercise
have
opposing
effects
on
medial
prefrontal
cortex
gliogenesis.
J.
Neursci.
27,
11442–11450.
Marcus,
B.H.,
Albrecht,
A.E.,
Niaura,
R.S.,
Taylor,
E.R.,
Simkin,
L.R.,
Feder,
S.I.,
Abrams,
D.B.,
Thompson,
P.D.,
1995.
Exercise
enhances
the
maintenance
of
smoking
cessation
in
women.
Addict.
Behav.
20,
87–92.
Marcus,
B.H.,
Lewis,
B.A.,
Hogan,
J.,
King,
T.K.,
Albrecht,
A.E.,
Bock,
B.,
Parisi,
A.F.,
Niaura,
R.,
Abrams,
D.B.,
2005.
The
efficacy
of
moderate-intensity
exercise
as
an
aid
for
smoking
cessation
in
women:
a
randomized
controlled
trial.
Nicotine
Tob.
Res.
7,
871–880.
McClernon,
F.J.,
Hiott,
F.B.,
Huettel,
S.A.,
Rose,
J.E.,
2005.
Abstinence-induced
changes
in
self-report
craving
correlate
with
event-related
FMRI
responses
to
smoking
cues.
Neuropsychopharmacology
30,
1940–1947.
McGinty,
J.F.,
Whitfield
Jr.,
T.W.,
Berglind,
W.J.,
2010.
Brain-derived
neurotrophic
factor
and
cocaine
addiction.
Brain
Res.
1314,
183–193.
Miller,
M.L.,
Vaillancourt,
B.D.,
Wright
Jr.,
M.J.,
Aarde,
S.M.,
Vandewater,
S.A.,
Creehan,
K.M.,
Taffe,
M.A.,
2012.
Reciprocal
inhibitory
effects
of
intravenous
d-methamphetamine
self-administration
and
wheel
activity
in
rats.
Drug
Alcohol
Depend.
121,
90–96.
Moffitt,
T.E.,
Arseneault,
L.,
Belsky,
D.,
Dickson,
N.,
Hancox,
R.J.,
Harrington,
H.,
Houts,
R.,
Poulton,
R.,
Roberts,
B.W.,
Ross,
S.,
Sears,
M.R.,
Thomson,
W.M.,
Caspi,
A.,
2011.
A
gradient
of
childhood
self-control
predicts
health,
wealth,
and
public
safety.
Proc.
Natl.
Acad.
Sci.
U.S.A.
108,
2693–2698.
Montoya,
I.D.,
Vocci,
F.,
2008.
Novel
medications
to
treat
addictive
disorders.
Curr.
Psychiatry
Rep.
10,
392–398.
Moon,
H.S.,
Dincer,
F.,
Mantzoros,
C.S.,
2013.
Pharmacological
concentrations
of
irisin
increase
cell
proliferation
without
influencing
markers
of
neurite
outgrowth
and
synaptogenesis
in
mouse
H19-7
hippocampal
cell
lines.
Metabolism
62,
1131–1136.
Morris,
M.J.,
Na,
E.S.,
Johnson,
A.K.,
2012.
Voluntary
running-wheel
exercise
decreases
the
threshold
for
rewarding
intracranial
self-stimulation.
Behav.
Neursci.
126,
582–587.
Mustroph,
M.L.,
Stobaugh,
D.J.,
Miller,
D.S.,
DeYoung,
E.K.,
Rhodes,
J.S.,
2011.
Wheel
running
can
accelerate
or
delay
extinction
of
conditioned
place
preference
for
cocaine
in
male
C57BL/6J
mice,
depending
on
timing
of
wheel
access.
Eur.
J.
Neurosci.
34,
1161–1169.

12
Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
National
Institutes
of
Health,
NIH
Common
Fund
launches
physical
activity
research
program,
2015a,
http://grants.nih.gov/grants/guide/rfa-files/RFA-DA-15-014.html
National
Institutes
of
Health,
Adolescent
brain
cognitive
development
(ABCD)-Coordinating
Center
(U24),
2015b,
http://grants.nih.gov/grants/guide/rfa-files/RFA-DA-15-014.html
Novelle,
M.G.,
Contreras,
C.,
Romero-Pico,
A.,
Lopez,
M.,
Dieguez,
C.,
2013.
Irisin,
two
years
later.
Int.
J.
Endocrinol.
2013,
746281.
O’Dell,
S.,
Marshall,
J.J.F.,
2014.
Running
wheel
exercise
before
a
binge
regimen
of
methamphetamine
does
not
protect
against
striatal
dopaminergic
damage.
Synapse
68,
419–425.
Ozburn,
A.R.,
Harris,
R.A.,
Blednov,
Y.A.,
2008.
Wheel
running,
voluntary
ethanol
consumption,
and
hedonic
substitution.
Alcohol
42,
417–424.
Palmgreen,
P.,
Donohew,
L.,
2010.
Impact
of
SENTAR
on
prevention
campaign
policy
and
practice.
Health
Commun.
25,
609–610.
Parcel,
G.S.,
Simons-Morton,
B.,
O’Hara,
N.M.,
Baranowski,
T.,
Wilson,
B.,
1989.
School
promotion
of
healthful
diet
and
physical
activity:
impact
on
learning
outcomes
and
self-reported
behavior.
Health
Educ.
Q.
16,
181–199.
Pentz,
M.A.,
Riggs,
N.R.,
2013.
Longitudinal
relationships
of
executive
cognitive
function
and
parent
influence
to
child
substance
use
and
physical
activity.
Prev.
Sci.
14,
229–237.
Perry,
J.L.,
Joseph,
J.E.,
Jiang,
Y.,
Zimmerman,
R.S.,
Kelly,
T.H.,
Darna,
M.,
Huettl,
P.,
Dwoskin,
L.P.,
Bardo,
M.T.,
2011.
Prefrontal
cortex
and
drug
abuse
vulnerability:
translation
to
prevention
and
treatment
interventions.
Brain
Res.
Rev.
65,
124–149.
Peterson,
A.B.,
Abel,
J.M.,
Lynch,
W.J.,
2014.
Dose-dependent
effects
of
wheel
running
on
cocaine-seeking
and
prefrontal
cortex
Bdnf
exon
IV
expression
in
rats.
Psychopharmacology
231,
1305–1314.
Ploughman,
M.,
2008.
Exercise
is
brain
food:
the
effects
of
physical
activity
on
cognitive
function.
Dev.
Neurorehabil.
11,
236–240.
Prakash,
R.S.,
Voss,
M.W.,
Erickson,
K.I.,
Kramer,
A.F.,
2015.
Physical
activity
and
cognitive
vitality.
Annu.
Rev.
Psychol.
66,
769–797.
Prochaska,
J.J.,
Hall,
S.M.,
Humfleet,
G.,
Munoz,
R.F.,
Reus,
V.,
Gorecki,
J.,
Hu,
D.,
2008.
Physical
activity
as
a
strategy
for
maintaining
tobacco
abstinence:
a
randomized
trial.
Prev.
Med.
47,
215–220.
Prudencio,
C.,
Abrantes,
B.,
Lopes,
I.,
Tavares,
M.A.,
2002.
Structural
and
functional
cellular
alterations
underlying
the
toxicity
of
methamphetamine
in
rat
retina
and
prefrontal
cortex.
Ann.
N.Y.
Acad.
Sci.
965,
522–528.
Qin,
L.,
Knol,
M.J.,
Corpeleijn,
E.,
Stolk,
R.P.,
2010.
Does
physical
activity
modify
the
risk
of
obesity
for
type
2
diabetes:
a
review
of
epidemiological
data.
Eur.
J.
Epidemiol.
25,
5–12.
Rasmussen,
E.B.,
Hillman,
C.,
2011.
Naloxone
and
rimonabant
reduce
the
reinforcing
properties
of
exercise
in
rats.
Exp.
Clin.
Psychopharmacol.
19,
389–400.
Reynolds,
D.,
Nicolson,
R.I.,
2007.
Follow-up
of
an
exercise-based
treatment
for
children
with
reading
difficulties.
Dyslexia
13,
78–96.
Rive,
M.M.,
van
Rooijen,
G.,
Veltman,
D.J.,
Phillips,
M.L.,
Schene,
A.H.,
Ruhe,
H.G.,
2013.
Neural
correlates
of
dysfunctional
emotion
regulation
in
major
depressive
disorder.
A
systematic
review
of
neuroimaging
studies.
Neurosci.
Biobehav.
Rev.
37,
2529–2553.
Roberts,
V.,
Maddison,
R.,
Simpson,
C.,
Bullen,
C.,
Prapavessis,
H.,
2012.
The
acute
effects
of
exercise
on
cigarette
cravings,
withdrawal
symptoms,
affect,
and
smoking
behaviour:
systematic
review
update
and
meta-analysis.
Psychopharmacology
222,
1–15.
Rozeske,
R.R.,
Greenwood,
B.N.,
Fleshner,
M.,
Watkins,
L.R.,
Maier,
S.F.,
2011.
Voluntary
wheel
running
produces
resistance
to
inescapable
stress-induced
potentiation
of
morphine
conditioned
place
preference.
Behav.
Brain
Res.
219,
378–381.
Sanchez,
V.,
Moore,
C.F.,
Brunzell,
D.H.,
Lynch,
W.J.,
2013.
Effect
of
wheel-running
during
abstinence
on
subsequent
nicotine-seeking
in
rats.
Psychopharmacology
227,
403–411.
Segat,
H.J.,
Kronbauer,
M.,
Roversi,
K.,
Schuster,
A.J.,
Vey,
L.T.,
Roversi,
K.,
Pase,
C.S.,
Antoniazzi,
C.T.,
Burger,
M.E.,
2014.
Exercise
modifies
amphetamine
relapse:
behavioral
and
oxidative
markers
in
rats.
Behav.
Brain
Res.
262,
94–100.
Segovia,
G.,
del
Arco,
A.,
Mora,
F.,
2009.
Environmental
enrichment,
prefrontal
cortex,
stress,
and
aging
of
the
brain.
J.
Neural
Transm.
116,
1007–1016.
Shen,
X.,
Kosten,
T.R.,
2011.
Immunotherapy
for
drug
abuse.
CNS
Neurol.
Disord.
Drug
Targets
10,
876–879.
Silva,
M.T.,
Heyman,
G.M.,
2001.
Chronic
morphine
consumption
decreases
wheel
running
and
wheel
running-reinforced
behavior
in
rats.
Pharmacol.
Biochem.
Behav.
69,
51–57.
Sinyor,
D.,
Brown,
T.,
Rostant,
L.,
Seraganian,
P.,
1982.
The
role
of
a
physical
fitness
program
in
the
treatment
of
alcoholism.
J.
Stud.
Alcohol.
43,
380–386.
Smith,
M.A.,
Lynch,
W.J.,
2011.
Exercise
as
a
potential
treatment
for
drug
abuse:
evidence
from
preclinical
studies.
Front.
Psychiatry
2,
82.
Smith,
M.A.,
Pitts,
E.G.,
2012.
Wheel
running
decreases
the
positive
reinforcing
effects
of
heroin.
Pharmacol.
Rep.
64,
960–964.
Smith,
M.A.,
Witte,
M.A.,
2012.
The
effects
of
exercise
on
cocaine
self-administration,
food-maintained
responding,
and
locomotor
activity
in
female
rats:
importance
of
the
temporal
relationship
between
physical
activity
and
initial
drug
exposure.
Exp.
Clin.
Psychopharmacol.
20,
437–446.
Smith,
M.A.,
Gergans,
S.R.,
Iordanou,
J.C.,
Lyle,
M.A.,
2008a.
Chronic
exercise
increases
sensitivity
to
the
conditioned
rewarding
effects
of
cocaine.
Pharmacol.
Rep.
60,
561–565.
Smith,
M.A.,
Schmidt,
K.T.,
Iordanou,
J.C.,
Mustroph,
M.L.,
2008b.
Aerobic
exercise
decreases
the
positive-reinforcing
effects
of
cocaine.
Drug
Alcohol
Depend.
98,
129–135.
Smith,
M.A.,
Walker,
K.L.,
Cole,
K.T.,
Lang,
K.C.,
2011.
The
effects
of
aerobic
exercise
on
cocaine
self-administration
in
male
and
female
rats.
Psychopharmacology
218,
357–369.
Smith,
M.A.,
Pennock,
M.M.,
Walker,
K.L.,
Lang,
K.C.,
2012.
Access
to
a
running
wheel
decreases
cocaine-primed
and
cue-induced
reinstatement
in
male
and
female
rats.
Drug
Alcohol
Depend.
121,
54–61.
Sobieraj,
J.C.,
Kim,
A.,
Fannon,
M.J.,
Mandyam,
C.D.,
2014.
Chronic
wheel
running-induced
reduction
of
extinction
and
reinstatement
of
methamphetamine
seeking
in
methamphetamine
dependent
rats
is
associated
with
reduced
number
of
periaqueductal
gray
dopamine
neurons.
Brain
Struct.
Funct.
(Epub
ahead
of
print).
Stitzer,
M.L.,
McCaul,
M.E.,
Bigelow,
G.E.,
Liebson,
I.A.,
1983.
Oral
methadone
self-administration:
effects
of
dose
and
alternative
reinforcers.
Clin.
Pharmacol.
Ther.
34,
29–35.
Strohle,
A.,
2009.
Physical
activity,
exercise,
depression
and
anxiety
disorders.
J.
Neural
Transm.
116,
777–784.
Tashiro,
M.,
Itoh,
M.,
Fujimoto,
T.,
Masud,
M.M.,
Watanuki,
S.,
Yanai,
K.,
2008.
Application
of
positron
emission
tomography
to
neuroimaging
in
sports
sciences.
Methods
45,
300–306.
Terry-McElrath,
Y.M.,
O’Malley,
P.M.,
Johnston,
L.D.,
2011.
Exercise
and
substance
use
among
American
youth,
1991–2009.
Am.
J.
Prev.
Med.
40,
530–540.
Thanos,
P.K.,
Tucci,
A.,
Stamos,
J.,
Robison,
L.,
Wang,
G.J.,
Anderson,
B.J.,
Volkow,
N.D.,
2010.
Chronic
forced
exercise
during
adolescence
decreases
cocaine
conditioned
place
preference
in
Lewis
rats.
Behav.
Brain
Res.
215,
77–82.
Thanos,
P.K.,
Stamos,
J.,
Robison,
L.S.,
Heyman,
G.,
Tucci,
A.,
Wang,
G.J.,
Robinson,
J.K.,
Anderson,
B.J.,
Volkow,
N.D.,
2013.
Daily
treadmill
exercise
attenuates
cocaine
cue-induced
reinstatement
and
cocaine
induced
locomotor
response
but
increases
cocaine-primed
reinstatement.
Behav.
Brain
Res.
239,
8–14.
Toborek,
M.,
Seelbach,
M.J.,
Rashid,
C.S.,
Andras,
I.E.,
Chen,
L.,
Park,
M.,
Esser,
K.A.,
2013.
Voluntary
exercise
protects
against
methamphetamine-induced
oxidative
stress
in
brain
microvasculature
and
disruption
of
the
blood-brain
barrier.
Mol.
Neurodegener.
8,
22.
Trost,
A.,
Hauber,
W.,
2014.
Dopamine
D1/D2
receptors
do
not
mediate
the
expression
of
conditioned
place
preference
induced
by
the
aftereffect
of
wheel
running.
BMC
Neurosci.
15,
124.
Tsibulsky,
V.L.,
Norman,
A.B.,
1999.
Satiety
threshold:
a
quantitative
model
of
maintained
cocaine
self-administration.
Brain
Res.
839,
85–93.
Ussher,
M.,
Cropley,
M.,
Playle,
S.,
Mohidin,
R.,
West,
R.,
2009.
Effect
of
isometric
exercise
and
body
scanning
on
cigarette
cravings
and
withdrawal
symptoms.
Addiction
(Abingdon,
England)
104,
1251–1257.
Ussher,
M.H.,
Taylor,
A.,
Faulkner,
G.,
2012.
Exercise
interventions
for
smoking
cessation.
Cochrane
Database
Syst.
Rev.
(1),
http://dx.doi.org/10.1002/
14651858.CD002295.pub4,
CD002295.
Ussher,
M.H.,
Taylor,
A.H.,
Faulkner,
G.E.,
2014.
Exercise
interventions
for
smoking
cessation.
Cochrane
Database
Syst.
Rev.
(8),
http://dx.doi.org/10.1002/
14651858.CD002295.pub5,
CD002295.
Van
den
Oever,
M.C.,
Spijker,
S.,
Smit,
A.B.,
De
Vries,
T.J.,
2010.
Prefrontal
cortex
plasticity
mechanisms
in
drug
seeking
and
relapse.
Neurosci.
Biobehav.
Rev.
35,
276–284.
van
Praag,
H.,
Kempermann,
G.,
Gage,
F.H.,
1999.
Running
increases
cell
proliferation
and
neurogenesis
in
the
adult
mouse
dentate
gyrus.
Nat.
Neurosci.
2,
266–270.
Van
Rensburg,
K.J.,
Taylor,
A.,
Hodgson,
T.,
2009.
The
effects
of
acute
exercise
on
attentional
bias
towards
smoking-related
stimuli
during
temporary
abstinence
from
smoking.
Addiction
(Abingdon,
England)
104,
1910–1917.
Vargas-Perez,
H.,
Sellings,
L.H.,
Paredes,
R.G.,
Prado-Alcala,
R.A.,
Diaz,
J.L.,
2008.
Reinforcement
of
wheel
running
in
BALB/c
mice:
role
of
motor
activity
and
endogenous
opioids.
J.
Mot.
Behav.
40,
587–593.
Viola,
G.G.,
Rodrigues,
L.,
Americo,
J.C.,
Hansel,
G.,
Vargas,
R.S.,
Biasibetti,
R.,
Swarowsky,
A.,
Goncalves,
C.A.,
Xavier,
L.L.,
Achaval,
M.,
Souza,
D.O.,
Amaral,
O.B.,
2009.
Morphological
changes
in
hippocampal
astrocytes
induced
by
environmental
enrichment
in
mice.
Brain
Res.
1274,
47–54.
Volkow,
N.D.,
Wang,
G.J.,
Fowler,
J.S.,
Tomasi,
D.,
Telang,
F.,
2011.
Addiction:
beyond
dopamine
reward
circuitry.
Proc.
Natl.
Acad.
Sci.
U.S.A.
108,
15037–15042.
Voss,
M.W.,
Nagamatsu,
L.S.,
Liu-Ambrose,
T.,
Kramer,
A.F.,
2011.
Exercise,
brain,
and
cognition
across
the
life
span.
J.
Appl.
Physiol.
111,
1505–1513.
Walker,
R.,
Morris,
D.W.,
Greer,
T.L.,
Trivedi,
M.H.,
2014.
Research
staff
training
in
a
multisite
randomized
clinical
trial:
methods
and
recommendations
from
the
stimulant
reduction
intervention
using
dosed
exercise
(STRIDE)
trial.
Addict.
Res.
Theory
22,
407–415.
Wang,
G.J.,
Volkow,
N.D.,
Fowler,
J.S.,
Franceschi,
D.,
Logan,
J.,
Pappas,
N.R.,
Wong,
C.T.,
Netusil,
N.,
2000.
PET
studies
of
the
effects
of
aerobic
exercise
on
human
striatal
dopamine
release.
J.
Nucl.
Med.
41,
1352–1356.
Weinstock,
J.,
Barry,
D.,
Petry,
N.M.,
2008.
Exercise-related
activities
are
associated
with
positive
outcome
in
contingency
management
treatment
for
substance
use
disorders.
Addict.
Behav.
33,
1072–1075.
Weinstock,
J.,
Capizzi,
J.,
Weber,
S.M.,
Pescatello,
L.S.,
Petry,
N.M.,
2014.
Exercise
as
an
intervention
for
sedentary
hazardous
drinking
college
students:
a
pilot
study.
Ment.
Health
Phys.
Act.
7,
55–62.
Weiss,
L.,
Gass,
J.,
Egan,
J.E.,
Ompad,
D.C.,
Trezza,
C.,
Vlahov,
D.,
2014.
Understanding
prolonged
cessation
from
heroin
use:
findings
from
a
community-based
sample.
J.
Psychoactive
Drugs
46,
123–132.

Michael.T.
Bardo,
Wilson.M.
Compton
/
Drug
and
Alcohol
Dependence
153
(2015)
3–13
13
Werme,
M.,
Thoren,
P.,
Olson,
L.,
Brene,
S.,
2000.
Running
and
cocaine
both
upregulate
dynorphin
mRNA
in
medial
caudate
putamen.
Eur.
J.
Neurosci.
12,
2967–2974.
Werme,
M.,
Lindholm,
S.,
Thoren,
P.,
Franck,
J.,
Brene,
S.,
2002a.
Running
increases
ethanol
preference.
Behav.
Brain
Res.
133,
301–308.
Werme,
M.,
Messer,
C.,
Olson,
L.,
Gilden,
L.,
Thoren,
P.,
Nestler,
E.J.,
Brene,
S.,
2002b.
Delta
FosB
regulates
wheel
running.
J.
Neurosci.
22,
8133–8138.
Williams,
D.M.,
Whiteley,
J.A.,
Dunsiger,
S.,
Jennings,
E.G.,
Albrecht,
A.E.,
Ussher,
M.H.,
Ciccolo,
J.T.,
Parisi,
A.F.,
Marcus,
B.H.,
2010.
Moderate
intensity
exercise
as
an
adjunct
to
standard
smoking
cessation
treatment
for
women:
a
pilot
study.
Psychol.
Addict.
Behav.
24,
349–354.
Wilson,
D.B.,
Smith,
B.N.,
Speizer,
I.S.,
Bean,
M.K.,
Mitchell,
K.S.,
Uguy,
L.S.,
Fries,
E.A.,
2005.
Differences
in
food
intake
and
exercise
by
smoking
status
in
adolescents.
Prev.
Med.
40,
872–879.
Yanagisawa,
H.,
Dan,
I.,
Tsuzuki,
D.,
Kato,
M.,
Okamoto,
M.,
Kyutoku,
Y.,
Soya,
H.,
2010.
Acute
moderate
exercise
elicits
increased
dorsolateral
prefrontal
activation
and
improves
cognitive
performance
with
Stroop
test.
NeuroImage
50,
1702–1710.
Zernig,
G.,
Ahmed,
S.H.,
Cardinal,
R.N.,
Morgan,
D.,
Acquas,
E.,
Foltin,
R.W.,
Vezina,
P.,
Negus,
S.S.,
Crespo,
J.A.,
Stockl,
P.,
Grubinger,
P.,
Madlung,
E.,
Haring,
C.,
Kurz,
M.,
Saria,
A.,
2007.
Explaining
the
escalation
of
drug
use
in
substance
dependence:
models
and
appropriate
animal
laboratory
tests.
Pharmacology
80,
65–119.
Zlebnik,
N.E.,
Anker,
J.J.,
Gliddon,
L.A.,
Carroll,
M.E.,
2010.
Reduction
of
extinction
and
reinstatement
of
cocaine
seeking
by
wheel
running
in
female
rats.
Psychopharmacology
209,
113–125.
Zlebnik,
N.E.,
Anker,
J.J.,
Carroll,
M.E.,
2012.
Exercise
to
reduce
the
escalation
of
cocaine
self-administration
in
adolescent
and
adult
rats.
Psychopharmacology
224,
387–400.
Zschucke,
E.,
Heinz,
A.,
Strohle,
A.,
2012.
Exercise
and
physical
activity
in
the
therapy
of
substance
use
disorders.
Sci.
World
J.
2012,
901741.