Habit
formation
and
change
Lucas
Carden
1
and
Wendy
Wood
1,2
This
review
highlights
emerging
findings
and
new
directions
in
research
on
habit
formation
and
change.
We
first
identify
the
cognitive,
attentional
mechanisms
that
contribute
to
habit
formation.
Then
we
show
how
habit
is
transforming
the
way
researchers
think
about
self-control,
and
how
changing
habits
involves
environmental
pressures
as
much
as
intrapsychic
forces.
Finally,
we
describe
big
data
and
new
technologies
that
offer
novel
methods
to
study
habits
outside
the
lab
by
capturing
repeated
actions
in
the
natural
environments
in
which
they
occur.
Addresses
1
Department
of
Psychology,
University
of
Southern
California,
United
States
2
Marshall
School
of
Business,
University
of
Southern
California,
United
States
Corresponding
author:
Carden,
Lucas
([email protected])
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
This
review
comes
from
a
themed
issue
on
Habits
and
skills
Edited
by
Barbara
Knowlton
and
Jo
¨
rn
Diedrichsen
For
a
complete
overview
see
the
Issue
and
the
Editorial
Available
online
2nd
January
2018
https://doi.org/10.1016/j.cobeha.2017.12.009
2352-1546/ã
2017
Elsevier
Ltd.
All
rights
reserved.
Ninety-nine
hundredths
or,
possibly,
nine
hundred
and
ninety-nine
thousandths
of
our
activity
is
purely
auto-
matic
and
habitual,
from
our
rising
in
the
morning
to
our
lying
down
each
night.
—
William
James
[1]
William
James
never
failed
to
make
provocative
claims,
especially
regarding
the
wide-reaching
influence
of
habit
on
human
behavior.
Over
a
century
later,
research
has
moved
beyond
claims
of
the
importance
of
habit
to
identifying
the
psychological
mechanisms
that
drive
habit
formation
and
change.
Habits
form
as
people
pursue
goals
in
daily
life.
When
repeatedly
performing
a
behavior
in
a
particular
context,
people
develop
implicit
associations
in
memory
between
contexts
and
responses.
Instrumental
and
Hebbian
learn-
ing
processes
are
involved
[2

].
As
people
repeat
a
behavior
in
a
stable
context,
their
intentions
and
goals
to
perform
it
gradually
become
less
influential
guides,
whereas
habits
increase
in
influence
[3,4].
Although
theories
of
habit
differ
in
details,
all
recognize
this
shift
from
goal-directed
to
habitual
behav-
ior
through
repeated
learning
[2

,5,6].
Ironically,
peo-
ple’s
lay
theories
of
habit
do
not
recognize
this
shift
in
action
control
(for
review,
see
[7]).
People
commonly
use
volitional,
goal-directed
explanations
for
why
they
per-
form
habits,
even
when
intentions
and
goals
fail
to
guide
actual
performance
[8].
Psychology
has
in
recent
years
focused
on
the
flexible
responses
generated
by
the
nonconscious
activation
of
goals
and
attitudes
[9].
In
contrast,
habit
cuing
involves
relatively
fixed
response
patterns.
Habits
are
slow
to
develop
and
change
in
comparison
to
other
implicit
processes
such
as
Pavlovian
fear
conditioning
[10].
To
this
end,
habits
are
a
challenging
construct
to
measure
and
manipulate
in
the
lab.
Yet
new
technologies
are
providing
novel
insights
into
habits
in
the
lab
and
everyday
contexts
(e.g.
[11]).
In
this
paper,
we
highlight
emerging
findings
and
new
directions
in
research
on
habit.
First,
we
briefly
review
new
research
showing
that
cognitive,
attentional
mechanisms
appear
to
be
central
to
instrumental
learn-
ing
of
habits.
Next,
we
examine
how
our
understanding
of
how
to
change
habits
is
being
transformed
by
research
on
self-control
[12
]
and
the
way
environmental,
as
well
as
intrapsychic,
forces
contribute
to
habit
change
[13,14].
Finally,
we
highlight
recent
studies
using
inno-
vative
technology
to
study
habit
formation
and
change
outside
the
laboratory
[15
].
Cognitive
processes
of
habit
formation
Attentional
mechanisms
are
important
in
habit
formation,
given
evidence
that
instrumental
learning
guides
atten-
tion
to
context
cues
[16,17].
That
is,
stimuli
that
have
been
rewarded
in
the
past
acquire
attentional
priority
over
non-rewarded
ones
[18].
This
habit
learning
phe-
nomenon
was
demonstrated
in
experiments
in
which
participants
learned
to
associate,
for
example,
colored
circles
on
a
computer
screen
with
monetary
rewards.
When
the
task
was
then
reconfigured
so
that
the
rewarded
stimuli
were
distractors
and
participants
were
to
choose
new
targets,
the
simple
presence
of
the
distractors
impeded
performance
[16].
Through
a
habit
learning
pathway
(instrumental
learning),
basic
perception
and
attention
systems
exhibited
performance
akin
to
a
habit.
People
preferentially
recognize
environmental
features
associated
with
past
rewards.
Available
online
at
www.sciencedirect.com
ScienceDirect
www.sciencedirect.com
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
Evidence
that
context
cues
automatically
bring
habitual
responses
to
mind
comes
from
a
series
of
studies
in
which
participants
practiced
a
sequential
task
of
making
sushi
in
a
computer
game
(JS
Labrecque,
W
Wood,
submitted
for
publication).
With
extensive
practice,
participants
were
able
to
quickly
report
the
next
step
in
the
sequence
when
primed
with
the
prior
step.
The
strength
of
these
habit
associations
in
memory
determined
habit
persistence.
When
participants
were
especially
fast
in
the
priming
task,
indicating
strong
habits,
their
habits
persisted
even
when
they
wanted
to
alter
the
recipe
and
add
a
new
ingredient.
Habit
resistance
to
change
is
understandable
given
con-
text
cues
that
capture
attention
automatically
and
given
habitual
responses
that
are
activated
automatically
on
perception
of
the
cue.
Through
these
basic
mechanisms,
features
of
the
environment
are
interwoven
into
habit
formation
and
change.
Habits
and
effortless
self-control
William
James
[19]
claimed
that
“the
more
of
the
details
of
our
daily
life
we
can
hand
over
to
the
effortless
custody
of
automatism,
the
more
our
higher
powers
of
mind
will
be
set
free
for
their
own
proper
work.”
By
implying
that
the
main
benefit
of
forming
habits
was
to
reduce
the
need
for
inhibition
and
self-control,
James
was
prescient
about
contemporary
research
on
self-control.
Self-control
traditionally
is
a
struggle
in
which
one
part
of
ourselves
tries
to
stop
another
part
of
ourselves
from
responding
[20].
Inhibitory
control
was
captured
most
famously
in
the
conflict
children
experienced
between
one
marshmallow
now
versus
two
marshmallows
later
[21].
In
other
paradigms,
it
was
represented
as
a
farsighted
planner
pitted
against
a
myopic
doer
[22]
and
in
others
as
a
muscle
that
resisted
temptations
for
a
future
self
[23].
In
this
struggle,
habits
were
treated
as
a
target
of
self-control,
needing
to
be
inhibited
[24].
More
recently,
research
has
recognized
James’s
claim,
highlighting
habit
as
an
auto-
matically
activated
response
that
can
achieve
goals
[25].
This
shift
from
habits-as-impediments
to
habits-as-ben-
eficial
is
evident
in
research
on
trait
self-control
[26].
We
now
know
that
people
who
score
high
on
such
scales
do
not
engage
in
much
effortful
inhibition
[27].
In
fact,
they
experience
less
motivational
conflict
and
report
less
inhi-
bition
of
temptations
in
daily
life
compared
with
people
with
low
self-control
[28,29].
Instead,
people
high
in
self-
control
have
weak
habits
for
unhealthy
activities
(e.g.
eating
junk
food,
[30])
and
strong
habits
for
healthy
activities
such
as
sleep,
exercise,
and
work
[12
,31].
One
longitudinal
study
showed
that
adolescents
with
high
trait
self-control
had
formed
meditation
habits
that
better
met
their
goals
3
months
after
a
meditation
retreat
[12
].
Furthermore,
experimental
research
has
shown
that
positive
habits
actually
protect
people
from
conflicting
desires
[32].
High
trait
self-control
may
thus
reflect
a
kind
of
situa-
tional
strategy
involving
arranging
environmental
cues
to
promote
beneficial
habit
formation
[33].
People
with
high
control
appear
to
actively
avoid
situations
offering
temp-
tations
and
distractions
[34].
A
recent
longitudinal
study
directly
examined
whether
experiences
of
temptations
matter
more
or
less
than
experiences
of
effortful
self-
control
in
the
successful
attainment
of
important
personal
goals
[35
].
Over
the
course
of
a
school
semester,
college
students
who
experienced
fewer
temptations
in
daily
life
were
more
likely
to
achieve
their
goals
than
those
who
experienced
more
temptations.
Critically,
experiences
of
effortful
self-control,
though
depleting,
were
unrelated
to
goal
attainment.
The
relationship
between
temptations
and
goal
attainment
was
mediated
by
feelings
of
deple-
tion,
suggesting
that
the
mere
presence
of
temptations
lead
to
experiences
of
conflicting
and
depleting
desires,
whether
or
not
control
is
engaged.
One
implication
of
this
new
understanding
of
self-con-
trol
is
that
those
who
have
the
foresight
to
reengineer
their
home
and
work
environments
to
reduce
tempta-
tions
will
be
more
likely
to
achieve
their
goals.
Evidence
from
studies
investigating
students’
studying
strategies
indeed
suggests
that
many
have
this
foresight:
students
who
used
more
situational
strategies,
such
as
hiding
their
cellphone,
were
more
likely
to
reach
their
academic
goals
and
not
struggle
with
temptations
[36].
Merely
manipulating
the
proximity
of
desired
and
undesired
foods
(e.g.
in
or
out
of
arms’
reach)
directly
influences
healt hy
eating
behavior
[37].
Effortless
self-control
might
seem
an
oxymoron
given
the
traditional
characterization
of
self-control
as
inhibition.
However,
we
now
know
that
self-control
involves
a
wide
range
of
responses
beyond
willpower.
To
be
successful,
people
high
in
self-control
appear
to
play
offense,
not
defense,
by
anticipating
and
avoiding
self-control
strug-
gles.
This
strategy,
however,
does
not
obviate
the
need
for
often
inhibiting
responses
early
in
the
habit
formation
process.
Thus,
perhaps
inhibitory
self-control
is
needed
in
the
early
stages
of
habit
formation
and,
as
time
pro-
gresses,
effortless
self-control,
or
habit,
takes
over
control
of
behavior.
It
is
likely
that
people
with
high
self-control
may
have
started
playing
offense
earlier
in
life
and
can
thus
reap
the
rewards
of
effortless
self-control
later
in
life.
To
what
extent
this
offensive
strategy
is
conscious
or
unconscious
is
a
promising
area
of
research
to
be
explored.
Changing
habits
Behavior
change
interventions
have
been
challenged
to
successfully
alter
lifestyle
behaviors
like
diet,
exercise,
environmental
sustainability,
and
financial
solvency
[14].
For
example,
the
national
5-A-Day-For-Better-Health
118
Habits
and
skills
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
www.sciencedirect.com
fruits
and
vegetables
campaign
presented
people
with
information
about
the
pros
and
cons
of
health
behaviors,
attempting
to
motivate
them
to
change.
The
campaign
successfully
increased
people’s
knowledge
about
what
they
should
do
to
be
healthy,
but
had
limited
effect
on
eating
habits
[38,39].
Another
example
comes
from
highly
controlled
studies
designed
to
change
habits
using
incen-
tives.
These
are
typically
successful
in
achieving
short-
term
change
but
fail
to
maintain
change
over
time,
after
the
incentives
are
removed
(for
reviews
see,
[40,41]).
A
habit
perspective
anticipates
limited
change
in
behav-
ior
when
performance
contexts
remain
stable.
Because
habits
are
stored
in
procedural
memory
relatively
separate
from
goals
and
intentions,
encountering
the
same
context
activates
habitual
responses,
even
when
newly
adopted
intentions
are
strong
[42].
The
slow
pace
of
habit
learning
was
shown
with
a
variety
of
health
habits,
such
as
exercis-
ing,
that
develop
with
weeks
or
months
of
repetition
in
stable
contexts
[43].
New
directions
in
habit
change
include
not
only
changing
beliefs
and
perceptions
but
also
using
cognitive
strategies
(e.g.,
reminders)
in
concert
with
environmental
change
strategies.
Implementation
intentions,
reminders,
and
rewards
Popular
behavior
change
interventions
involve
planning
and
reminders.
For
example,
implementation
intentions
help
people
to
remember
to
act
on
intentions
to
change
behavior.
Although
earlier
reviews
indicated
the
effec-
tiveness
of
implementation
intentions
[44],
a
meta-anal-
ysis
of
over
44
diet
studies
showed
only
small
behavior
change
effects
during
the
interventions
and
negligible
long-term
effects
[45].
Especially
for
strong
antagonistic
habits,
like
unhealthy
eating
and
smoking,
implementa-
tion
intentions
have
little
impact
[45,46].
Potentially,
implementation
intentions
could
promote
habit
forma-
tion
when
encouraging
repetition
in
contexts
that
support
the
action,
like
walking
in
a
pedestrian-friendly
environ-
ment
[41].
Interestingly,
Turton
et
al.’s
meta-analysis
revealed
more
success
with
food-specific
inhibition
and
attention
bias
modification
training,
both
of
which
may
target
the
cognitive
mechanisms
underlying
habit
[45].
Reminders
and
symbolic
rewards
like
trophies
are
com-
mon
features
of
web
and
smartphone
based
programs
[47].
Although
reminders
may
be
effective
in
the
short-
term,
they
can
impede
habit
formation
in
the
long
term
[47].
Reminders
can
cause
people
to
deliberate
about
repeating
a
behavior,
and
deliberation
sometimes
pre-
cludes
habit
formation
(Labrecque
and
Wood,
unpub).
Deliberation
keeps
learning
processes
focused
on
goals,
whereas
habit
formation
arises
when
goals
recede
in
salience
and
influence
[48].
Web
and
smartphone
applications
can
also
promote
app
dependence
instead
of
continued
repetition
of
a
behavior
following
app
use
[49].
Because
many
apps
rely
on
extrinsic
rewards,
the
apps
may
crowd
out
intrinsic
moti-
vation
to
repeat
the
behavior.
Habit
change
apps
that
are
embedded
within
a
multipurpose
technology
(e.g.
smart
watch)
can
be
effective
since
they
may
prompt
and
remind
the
user
to
perform
a
habit
(e.g.
increasing
walk-
ing
steps)
even
when
the
user
is
performing
other
actions
with
the
technology
(e.g.
text
messaging).
Although
few
existing
apps
focus
on
repetition
of
a
behavior
in
a
particular
context,
context-aware
technolo-
gies
are
on
the
horizon
(see
for
review,
[50]).
These
apps
and
technologies
would
remind
users
to
perform
beha-
viors
when
in
specific
environments,
perhaps
being
trig-
gered
by
sensors
in
those
environments
(e.g.
entering
the
kitchen
triggers
reminder
to
drink
water).
In
this
way,
behavior
change
apps
can
facilitate
habit
formation
by
connecting
specific
environmental
cues
with
desired
responses.
Changing
environments,
changing
habits
When
environments
change,
the
cues
activating
habits
may
change
also,
with
the
result
of
disrupting
habit
performance.
Without
familiar
habit
cues,
people
are
forced
to
make
decisions
about
how
to
act.
According
to
the
habit
discontinuity
effect,
behavior
change
interventions
are
more
effective
during
life
course
changes
that
disrupt
habit
cues,
such
as
moving
house,
having
a
child,
and
changing
jobs
[42,51].
The
absence
of
old
cues
provides
a
window
of
opportunity
to
make
decisions
and
implement
new
goals
and
intentions.
In
illustration,
a
recent
field
experiment
with
over
800
house-
holds,
half
of
which
recently
relocated,
received
an
infor-
mational
intervention
to
promote
25
environmental
beha-
viors
[52
].
The
intervention
was
more
effective
for
those
who
had
relocated
with
the
last
3
months
(see
also
[42,53,54]).
A
serendipitous
example
of
how
environmental
disrup-
tion
changes
societal
habits
occurred
with
a
two
day
partial
London
Tube
workers
strike
in
February,
2014.
From
200
million
points
of
card
swipe
data,
researchers
tracked
commuters’
transportation
habits
before
and
after
the
strike
[15
].
The
disruption
led
5%
of
commuters
to
adopt
new,
more
optimal
travel-route
habits,
and
these
occurred
especially
in
areas
where
the
tube
map
was
inaccurately
drawn
or
commuters
could
not
estimate
train
speed.
The
disruption
of
old
cues
thus
enabled
some
commuters
to
discover
and
form
more
optimal
traveling
habits.
Although
habits
can
be
disrupted
by
changes
in
macro
environments
or
during
life
transitions,
habit
performance
can
also
be
altered
through
choice
architecture
or
Habit
formation
and
change
Carden
and
Wood
119
www.sciencedirect.com
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
environmental
reengineering
interventions
that
change
the
structure
of
everyday
decisions
[55,56].
Given
that
habit
formation
requires
repeated
responses
in
a
stable
context,
altering
the
decision
structure
may
sometimes
promote
habit
formation
by
making
it
easier
to
perform
a
desired
action.
Fortunately,
conscious
decisions
to
alter
the
envi-
ronment,
such
as
dedicating
a
prominent
place
for
fruits
and
vegetables
on
the
kitchen
counter,
might
also
guide
people
into
rip
currents
[57],
potentially
leading
to
a
cascade
of
psychological
changes
that
maintain
new
beha-
viors,
including
identity
[58]
and
physical
changes
such
as
weight
loss
[59].
Rip
currents
are
channels
of
water
that
run
perpendicular
to
the
beach
that
can
carry
someone
effortlessly
far
into
the
ocean.
Analogously,
environmen-
tal
changes,
such
as
manipulating
the
saliency
of
a
kitchen
fruit
bowl,
can
cause
people
to
repeatedly
eat
fruits,
leading
to
weight
loss
and
new
identity
attributions
(e.g.
I
am
a
healthy
eater).
Using
big
data
and
smart
phones
to
study
habits
in
everyday
life
In
the
past
decade,
big
data
and
smartphone
technologies
offer
revolutionary
new
ways
to
study
habits
in
daily
life.
These
open
up
fine-grained
analysis
of
the
context
cues
that
trigger
everyday
habits.
One
example
is
a
smoking
cessation
study
that
combined
ecological
momentary
assessment
of
reported
cravings
with
geo-location
map-
ping
(via
smartphones)
of
exposure
to
point-of-sale
tobacco
cues
[60].
Relapse
rates
increased
with
exposure
to
smoking
cues,
even
when
participants
were
not
experiencing
cravings.
This
study
suggests
that
environ-
mental
cues
direct
attention
and
activate
a
habitual
response
in
mind,
even
when
people
are
not
experiencing
a
desire
to
act.
Big
data
analyses
also
reveal
important
social
conse-
quences
to
seemingly
mundane
habits,
such
as
how
often
and
where
students
make
purchases
on
campus
[11].
Instead
of
survey-based
methods
to
assess
student
reten-
tion,
researchers
modeled
students’
social
networks
from
the
frequency
and
location
of
ID
card
transactions
(e.g.
campus
restaurant,
printer
services).
Students
were
less
likely
to
drop
out
in
their
freshman
year
if
they
showed
more
regularity
in
their
transactions,
suggesting
greater
social
integration
on
campus.
An
implication
is
that
at-risk
students
can
be
identified
from
such
indicators
of
habitual
social
integration,
and
retention
interventions
can
be
designed
accordingly.
Conclusion
and
future
directions
Habit
research
has
blossomed
over
the
past
few
years.
We
are
making
progress
on
how
basic
cognitive
mechanisms
like
attention
relate
to
habit
formation
[16],
how
people
with
high
self-control
use
habits
to
achieve
their
goals
[12
],
and
how
habit
performance
is
influenced
by
envi-
ronmental
disruptions
[52
].
Additional
advances
include
exciting
research
on
how
social
interaction
habits
contribute
to
intergroup
relations
[61].
We
are
also
start-
ing
to
track
lay
beliefs
about
habit
formation
and
perfor-
mance
([62];
for
review,
see
[7]).
Future
habit
research
can
mine
new
technologies
to
measure
the
context
cues
that
drive
habits.
In
a
unique
study
in
addiction
research,
smokers
took
pictures
of
their
favorite
smoking
environments
and
brought
them
into
the
lab
for
cue-reactivity
tests
[63].
Personalized
smoking
environments
led
to
stronger
cravings
than
generic
envir-
onments.
This
method
of
image
analysis
holds
strong
promise
for
studying
habits
in
controlled
settings.
Habits
in
general
stretch
researchers’
capabilities
because
they
are,
at
core,
interactions
between
persons
and
envir-
onments.
They
reflect
past
implicit
learning
activated
by
current
context
cues.
On
the
one
hand,
studying
habit
involves
understanding
intrapsychic
processes
such
as
implicit
perceptual,
attentional,
and
memory
processes.
On
the
other
hand,
habit
research
involves
understanding
how
the
world
beyond
our
heads
activates
habitual
responses.
Recent
research
discoveries
provide
a
strong
foundation
to
understand
how
habits
are
driven
by
both
the
person
and
environment
in
these
ways.
Conflict
of
interest
statement
Nothing
declared.
References
and
recommended
reading
Papers
of
particular
interest,
published
within
the
period
of
review,
have
been
highlighted
as:
of
special
interest

of
outstanding
interest
1.
James
W:
Talks
to
Teachers
on
Psychology
and
to
Students
on
Some
of
Life’s
Ideals.
New
York,
NY:
Holt;
1899.
2.

Wood
W,
Ru
¨
nger
D:
Psychology
of
habit.
Psychology
2016,
67:289-314
http://dx.doi.org/10.1146/annurev-psych-122414-
033417.
This
paper
provides
a
broad
review
of
the
cognitive,
motivational,
and
neurobiological
bases
of
habit.
A
model
is
presented
of
the
many
ways
in
which
habit
interfaces
with
goal-directed
processes.
3.
Ouellette
JA,
Wood
W:
Habit
and
intention
in
everyday
life:
the
multiple
processes
by
which
past
behavior
predicts
future
behavior.
Psychol
Bull
1998,
124:54-74
http://dx.doi.org/10.1037/
0033-2909.124.1.54.
4.
Sheeran
P,
Godin
G,
Conner
M,
Germain
M:
Paradoxical
effects
of
experience:
past
behavior
both
strengthens
and
weakens
the
intention-behavior
relationship.
J
Assoc
Consum
Res
2017.
5.
Cushman
F,
Morris
A:
Habitual
control
of
goal
selection
in
humans.
Proc
Natl
Acad
Sci
U
S
A
2015,
112:13817-13822.
6.
Gardner
B:
A
review
and
analysis
of
the
use
of
“habit”
in
understanding,
predicting
and
influencing
health-related
behaviour.
Health
Psychol
Rev
2015,
9:277-295
http://dx.doi.org/
10.1080/17437199.2013.876238.
7.
Wood
W:
Habit
in
personality
and
social
psychology.
Personal
Soc
Psychol
Rev
2017:1-15
http://dx.doi.org/10.1177/
1088868317720362.
8.
Neal
DT,
Wood
W,
Labrecque
JS,
Lally
P:
How
do
habits
guide
behavior?
Perceived
and
actual
triggers
of
habits
in
daily
life.
J
Exp
Soc
Psychol
2012,
48:492-498
http://dx.doi.org/10.1016/j.
jesp.2011.10.011.
120
Habits
and
skills
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
www.sciencedirect.com
9.
Weingarten
E,
Chen
Q,
McAdams
M,
Yi
J,
Hepler
J,
Albarracı
´
n
D:
From
primed
concepts
to
action:
a
meta-
analysis
of
the
behavioral
effects
of
incidentally
presented
words.
Psychol
Bull
2016,
142:472-497
http://dx.doi.org/10.1037/bul0000030.
10.
Amodio
DM,
Ratner
KG:
A
memory
systems
model
of
implicit
social
cognition.
Curr
Dir
Psychol
Sci
2011,
20:143-148
http://dx.
doi.org/10.1177/0963721411408562.
11.
Ram
S,
Wang
Y,
Currim
F,
Currim
S:
Using
big
data
for
predicting
freshmen
retention.
2015
International
Conference
on
Information
Systems:
Exploring
the
Information
Frontier,
ICIS
2015.
Association
for
Information
Systems;
2015.
12.
Galla
BM,
Duckworth
AL:
More
than
resisting
temptation:
beneficial
habits
mediate
the
relationship
between
self-
control
and
positive
life
outcomes.
J
Personal
Soc
Psychol
2015,
109:508-525
http://dx.doi.org/10.1037/pspp0000026 .
This
paper
attempts
to
reconcile
the
findings
that
people
with
high
self-
control
attain
more
positive
outcomes
but
do
not
do
so
by
using
effortful
self-control.
Rather,
the
researchers
posit
and
find
evidence
for
beneficial
habits
mediating
the
relationship
between
self-control
and
goal
attainment.
13.
Marteau
TM,
Hollands
GJ,
Fletcher
PC:
Changing
human
behavior
to
prevent
disease:
the
importance
of
targeting
automatic
processes.
Science
2012,
337:1492-1495
http://dx.
doi.org/10.1126/science.1226918.
14.
Rothman
AJ,
Gollwitzer
PM,
Grant
AM,
Neal
DT,
Sheeran
P,
Wood
W:
Hale
and
hearty
policies:
how
psychological
science
can
create
and
maintain
healthy
habits.
Perspect
Psychol
Sci
2015,
10:701-705.
15.
Larcom
S,
Rauch
F,
Willems
T:
The
benefits
of
forced
experimentation:
striking
evidence
from
the
London
underground
network.
Q
J
Econ
2017
http://dx.doi.org/10.1093/
qje/qjx020.
This
paper
described
an
analysis
of
London
commuters’
transportation
habits
before
and
after
a
workers’
strike.
Many
commuters
exhibited
suboptimal
traveling
routes
before
the
strike
and
then
developed
stable,
more
optimal
routes
after
the
strike.
Habit
and
search
cost
explanations
are
discussed.
16.
Anderson
BA:
The
attention
habit:
how
reward
learning
shapes
attentional
selection.
Ann
N
Y
Acad
Sci
2016,
1369:24-39.
17.
Le
Pelley
ME,
Mitchell
CJ,
Beesley
T,
George
DN,
Wills
AJ:
Attention
and
associative
learning
in
humans:
an
integrative
review.
Psychol
Bull
2016,
142:1111-1140
http://dx.doi.org/
10.1037/bul0000064.
18.
Luque
D,
Beesley
T,
Morris
RW,
Jack
BN,
Griffiths
O,
Whitford
TJ,
Le
Pelley
ME:
Goal-directed
and
habit-like
modulations
of
stimulus
processing
during
reinforcement
learning.
J
Neurosci
2017,
37:3009-3017.
19.
James
W:
The
Principles
of
Psychology
.
New
York,
NY:
Henry
Holt;
1890.
20.
Muraven
M,
Baumeister
RF:
Self-regulation
and
depletion
of
limited
resources:
does
self-control
resemble
a
muscle?
Psychol
Bull
2000,
126:247-259.
21.
Mischel
W,
Shoda
Y,
Rodriguez
ML:
Delay
of
gratification
in
children.
Science
1989,
244:933-938
http://dx.doi.org/10.1126/
science.2658056.
22.
Thaler
RH,
Shefrin
HM:
An
economic
theory
of
self-control.
J
Polit
Econ
1981,
89:392-406.
23.
Baumeister
RF,
Vohs
KD,
Tice
DM:
The
strength
model
of
self-
control.
Curr
Dir
Psychol
Sci
2007,
16:351-355.
24.
Quinn
JM,
Pascoe
A,
Wood
W,
Neal
DT:
Can’t
control
yourself?
Monitor
those
bad
habits.
Personal
Soc
Psychol
Bull
2010,
36:499-511
http://dx.doi.org/10.1177/0146167209360665.
25.
Neal
DT,
Wood
W,
Drolet
A:
How
do
people
adhere
to
goals
when
willpower
is
low?
The
profits
(and
pitfalls)
of
strong
habits.
J
Personal
Soc
Psychol
2013,
104:959-975
http://dx.doi.
org/10.1037/a0032626.
26.
Tangney
JP,
Baumeister
RF,
Boone
AL:
High
self-control
predicts
good
adjustment,
less
pathology,
better
grades,
and
interpersonal
success.
J
Personal
2004,
72:271-324
http://dx.
doi.org/10.1111/j.0022-3506.2004.00263.x.
27.
Hofmann
W,
Baumeister
RF,
Fo
¨
rster
G,
Vohs
K:
Everyday
temptations:
an
experience
sampling
study
of
desire,
conflict,
and
self-control.
J
Personal
Soc
Psychol
2012,
102:1318-1335
http://dx.doi.org/10.1037/a0026545.
28.
Gillebaart
M,
Schneider
IK,
De
Ridder
DT:
Effects
of
trait
self-
control
on
response
conflict
about
healthy
and
unhealthy
food.
J
Personal
2015,
84:789-798
http://dx.doi.org/10.1111/
jopy.12219.
29.
Imhoff
R,
Schmidt
AF,
Gerstenberg
F:
Exploring
the
interplay
of
trait
self-control
and
ego
depletion:
empirical
evidence
for
ironic
effects.
Eur
J
Personal
2014,
28:413-424
http://dx.doi.org/
10.1002/per.1899.
30.
Adriaanse
MA,
Kroese
FM,
Gillebaart
M,
De
Ridder
DT:
Effortless
inhibition:
habit
mediates
the
relation
between
self-control
and
unhealthy
snack
consumption.
Front
Psychol
2014,
5
http://
dx.doi.org/10.3389/fpsyg.2014.00444
Article
444.
31.
Gillebaart
M,
Adriaanse
MA:
Self-control
predicts
exercise
behavior
by
force
of
habit,
a
conceptual
replication
of
Adriaanse
et
al.
(2014).
Front
Psychol
2017,
8.
32.
Lin
PY,
Wood
W,
Monterosso
J:
Healthy
eating
habits
protect
against
temptations.
Appetite
2016,
103:432-440
http://dx.doi.
org/10.1016/j.appet.2015.11.011.
33.
Duckworth
AL,
Gendler
TS,
Gross
JJ:
Situational
strategies
for
self-control.
Perspect
Psychol
Sci
2016,
11:35-55.
34.
Ent
MR,
Baumeister
RF,
Tice
DM:
Trait
self-control
and
the
avoidance
of
temptation.
Personal
Individ
Differ
2015,
74:12-15.
35.
Milyavskaya
M,
Inzlicht
M:
What’s
so
great
about
self-control?
Examining
the
importance
of
effortful
self-control
and
temptation
in
predicting
real-life
depletion
and
goal
attainment.
Soc
Psychol
Personal
Sci
2017
http://dx.doi.org/
10.1177/1948550616679237.
This
paper
described
a
longitudinal
study
that
combined
prospective
data
collection
and
experience
sampling
methods
to
examine
whether
goal
attainment
is
a
function
of
effortful
self-control
or
avoiding
temptations.
Results
indicated
that
goal
attainment
was
unrelated
to
effortful
self-
control;
rather,
success
was
related
to
decreased
exposure
to
temptations.
36.
Duckworth
AL,
White
RE,
Matteucci
AJ,
Shearer
A,
Gross
JJ:
A
stitch
in
time:
strategic
self-control
in
high
school
and
college
students.
J
Educ
Psychol
2016,
108:329.
37.
Privitera
GJ,
Zuraikat
FM:
Proximity
of
foods
in
a
competitive
food
environment
influences
consumption
of
a
low
calorie
and
a
high
calorie
food.
Appetite
2014,
76:175-179.
38.
Stables
GJ,
Subar
AF,
Patterson
BH,
Dodd
K,
Heimendinger
J,
Van
Duyn
MAS,
Nebeling
L:
Changes
in
vegetable
and
fruit
consumption
and
awareness
among
US
adults:
results
of
the
1991
and
1997
5
A
Day
for
Better
Health
Program
surveys.
J
Am
Diet
Assoc
2002,
102:809-817
http://dx.doi.org/10.1016/S0002-
8223(02)90181-1.
39.
Casagrande
SS,
Wang
Y,
Anderson
C,
Gary
TL:
Have
Americans
increased
their
fruit
and
vegetable
intake?
The
trends
between
1988
and
2002.
Am
J
Prev
Med
2007,
32:257-263.
40.
Mantzari
E,
Vogt
F,
Shemilt
I,
Wei
Y,
Higgins
JP,
Marteau
TM:
Personal
financial
incentives
for
changing
habitual
health-
related
behaviors:
a
systematic
review
and
meta-analysis.
Prev
Med
2015,
75:75-85.
41.
Wood
W,
Neal
DT:
Healthy
through
habit:
interventions
for
initiating
and
maintaining
health
behavior
change.
Behav
Sci
Policy
2016,
2:71-83.
42.
Walker
I,
Thomas
GO,
Verplanken
B:
Old
habits
die
hard:
travel
habit
formation
and
decay
during
an
office
relocation.
Environ
Behav
2015,
47:1089-1106.
43.
Lally
P,
van
Jaarsveld
CHM,
Potts
HWW,
Wardle
J:
How
are
habits
formed:
modelling
habit
formation
in
the
real
world.
Eur
J
Soc
Psychol
2010,
40:998-1009
http://dx.doi.org/10.1002/
ejsp.674.
Habit
formation
and
change
Carden
and
Wood
121
www.sciencedirect.com
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
44.
Adriaanse
MA,
Vinkers
CD,
De
Ridder
DT,
Hox
JJ,
De
Wit
JB:
Do
implementation
intentions
help
to
eat
a
healthy
diet?
A
systematic
review
and
meta-analysis
of
the
empirical
evidence.
Appetite
2011,
56:183-193.
45.
Turton
R,
Bruidegom
K,
Cardi
V,
Hirsch
CR,
Treasure
J:
Novel
methods
to
help
develop
healthier
eating
habits
for
eating
and
weight
disorders:
a
systematic
review
and
meta-analysis.
Neurosci
Biobehav
Rev
2016,
61:132-155
http://dx.doi.org/
10.1016/j.neubiorev.2015.12.008.
46.
Webb
TL,
Sheeran
P,
Luszczynska
A:
Planning
to
break
unwanted
habits:
habit
strength
moderates
implementation
intention
effects
on
behaviour
change.
Br
J
Soc
Psychol
2009,
48:507-523.
47.
Stawarz
K,
Cox
AL,
Blandford
A:
Beyond
self-tracking
and
reminders:
designing
smartphone
apps
that
support
habit
formation.
In
Proceedings
of
the
33rd
Annual
ACM
Conference
on
Human
Factors
in
Computing
Systems.
ACM;
2015:2653-2662
http://dx.doi.org/10.1145/2702123.2702230 .
48.
Gillan
CM,
Otto
AR,
Phelps
EA,
Daw
ND:
Model-based
learning
protects
against
forming
habits.
Cognit
Affect
Behav
Neurosci
2015,
15:523-536.
49.
Renfree
I,
Harrison
D,
Marshall
P,
Stawarz
K,
Cox
A:
Don’t
kick
the
habit:
the
role
of
dependency
in
habit
formation
apps.
In
Proceedings
of
the
2016
CHI
Conference
Extended
Abstracts
on
Human
Factors
in
Computing
Systems.
ACM;
2016:2932-2939
http://dx.doi.org/10.1145/2851581.2892495 .
50.
Chen
G,
Ding
X,
Huang
K,
Ye
X,
Zhang
C:
Changing
health
behaviors
through
social
and
physical
context
awareness.
Computing,
Networking
and
Communications
(ICNC),
International
Conference
2015.
IEEE;
2015:663-667
http://dx.doi.org/10.1109/
ICCNC.2015.7069424.
51.
Verplanken
B,
Walker
I,
Davis
A,
Jurasek
M:
Context
change
and
travel
mode
choice:
combining
the
habit
discontinuity
and
self-activation
hypotheses.
J
Environ
Psychol
2008,
28:121-127
http://dx.doi.org/10.1016/j.jenvp.2007.10.005.
52.
Verplanken
B,
Roy
D:
Empowering
interventions
to
promote
sustainable
lifestyles:
testing
the
habit
discontinuity
hypothesis
in
a
field
experiment.
J
Environ
Psychol
2016,
45:127-134.
This
paper
describes
a
field
experiment
which
tested
the
habit
disconti-
nuity
effect
–
habit
change
interventions
are
more
effective
during
life
course
changes
(e.g.
moving
house).
800
households
were
randomly
assigned
to
receive
a
sustainable
behaviors
intervention.
The
intervention
was
more
effective
for
those
who
recently
relocated.
53.
Bamberg
S:
Is
a
residential
relocation
a
good
opportunity
to
change
people’s
travel
behavior?
Results
from
a
theory-driven
intervention
study.
Environ
Behav
2006,
38:820-840.
54.
Thøgersen
J:
The
importance
of
timing
for
breaking
commuters’
car
driving
habits.
Collegium
2012,
12:130-140.
55.
Thaler
RH,
Sunstein
CR,
Balz
JP:
Choice
architecture.
In
The
Behavioral
Foundations
of
Public
Policy.
Edited
by
Shafir
E.
Princeton,
NJ:
Princeton
University
Press;
2012:428-439
http://dx.
doi.org/10.2139/ssrn.2536504.
56.
Rozin
P,
Scott
S,
Dingley
M,
Urbanek
JK,
Jiang
H,
Kaltenbach
M:
Nudge
to
nobesity
I:
Minor
changes
in
accessibility
decrease
food
intake.
Judgm
Decis
Mak
2011,
6:323-332.
57.
Frey
E,
Rogers
T:
Persistence:
how
treatment
effects
persist
after
interventions
stop.
Policy
Insights
Behav
Brain
Sci
2014,
1:172-179.
58.
Wilson
TD:
Redirect:
The
Surprising
New
Science
of
Psychological
Change.
UK:
Penguin;
2011.
59.
Carels
RA,
Burmeister
JM,
Koball
AM,
Oehlhof
MW,
Hinman
N,
LeRoy
M,
Bannon
E,
Ashrafioun
L,
Storfer-Isser
A,
Darby
LA,
Gumble
A:
A
randomized
trial
comparing
two
approaches
to
weight
loss:
differences
in
weight
loss
maintenance.
J
Health
Psychol
2014,
19:296-311
http://dx.doi.org/10.1177/
1359105312470156.
60.
Kirchner
TR,
Cantrell
J,
Anesetti-Rothermel
A,
Ganz
O,
Vallone
DM,
Abrams
DB:
Geospatial
exposure
to
point-of-sale
tobacco:
real-time
craving
and
smoking-cessation
outcomes.
Am
J
Prev
Med
2013,
45:379-385.
61.
Hackel
LM,
Doll
BB,
Amodio
DM:
Instrumental
learning
of
traits
versus
rewards:
dissociable
neural
correlates
and
effects
on
choice.
Nat
Neurosci
2015,
18:1233-1235
http://dx.doi.org/
10.1038/nn.4080.
62.
Carden
L,
Wood
W,
Neal
DT,
Pascoe
A:
Incentives
activate
a
control
mind-set:
good
for
deliberate
behaviors,
bad
for
habit
performance.
J
Assoc
Consum
Res
2017,
2.
63.
Conklin
CA,
Perkins
KA,
Robin
N,
McClernon
FJ,
Salkeld
RP:
Bringing
the
real
world
into
the
laboratory:
personal
smoking
and
nonsmoking
environments.
Drug
Alcohol
Depend
2010,
111:58-63.
122
Habits
and
skills
Current
Opinion
in
Behavioral
Sciences
2018,
20:117–122
www.sciencedirect.com