Determinants of behaviour and their efficacy as targets of behavioural change interventions

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Nature Reviews Psychology
nature reviews psychology https://doi.org/10.1038/s44159-024-00305-0
Review article
Determinants of behaviour
and their efficacy as targets of
behavioural change interventions
Dolores Albarracín 1,2,3,4,5 , Bita Fayaz-Farkhad2 & Javier A. Granados Samayoa1,3
Abstract
Unprecedented social, environmental, political and economic
challenges — such as pandemics and epidemics, environmental
degradation and community violence — require taking stock of
how to promote behaviours that benet individuals and society at
large. In this Review, we synthesize multidisciplinary meta-analyses
of the individual and social-structural determinants of behaviour
(for example, beliefs and norms, respectively) and the ecacy of
behavioural change interventions that target them. We nd that, across
domains, interventions designed to change individual determinants
can be ordered by increasing impact as those targeting knowledge,
general skills, general attitudes, beliefs, emotions, behavioural skills,
behavioural attitudes and habits. Interventions designed to change
social-structural determinants can be ordered by increasing impact
as legal and administrative sanctions; programmes that increase
institutional trustworthiness; interventions to change injunctive
norms; monitors and reminders; descriptive norm interventions;
material incentives; social support provision; and policies that increase
access to a particular behaviour. We nd similar patterns for health and
environmental behavioural change specically. Thus, policymakers
should focus on interventions that enable individuals to circumvent
obstacles to enacting desirable behaviours rather than targeting salient
but ineective determinants of behaviour such as knowledge and
beliefs.
Sections
Introduction
Behavioural determinants
Individual determinants and
interventions
Social-structural determinants
and interventions
Summary and future
directions
1Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA. 2Annenberg School of
Communication, University of Pennsylvania, Philadelphia, PA, USA. 3Annenberg Public Policy Center, University of
Pennsylvania, Philadelphia, PA, USA. 4Department of Community and Family Health, School of Nursing, University
of Pennsylvania, Philadelphia, PA, USA. 5Department of Health Care Management, Wharton School of Business,
University of Pennsylvania, Philadelphia, PA, USA. e-mail: dalba@upenn.edu
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Review article
an empirical model of behavioural change based on their efficacy to
provide a picture of general principles that can inform intervention
decisions for new or understudied behaviours.
Our Review includes all identified meta-analyses of behaviour
prediction or intervention efficacy across domains (Supplementary
Note1) based on clearly classifiable determinants, targets of change
and behavioural outcomes. However, although interventions designed
to change a particular target are assumed to change that specific tar-
get
16
, they might exert an array of effec ts. For example, an inter vention
that communicates that neighbours use less energy might influence
both descriptive norms and positive attitudes towards conserving
energy
19
. Verifying all possible mechanisms of effects is outside the
scope of this Review.
We concentrated on what targets might be most effective, which
is the first critical question when designing a programme to change
behaviour. For example, deciding whether to instil pro-vaccination
norms, combat conspiracy theories about vaccination or add vac-
cination sites is essential to the public health management of a pan
-
demic. However, implementing interventions once a target of change
is selected brings up a different set of questions that are outside the
scope of this Review. Although we briefly describe what interven-
tions often do, readers should review the primary research literature
to determine what the most successful interventions within a given
target look like. After all, reviewing intervention manuals is critical to
a faithful programme implementation20–22.
Behavioural determinants
Individual factors are at the centre of behavioural prediction and change
models such as the reasoned action approach
20–25
, the information–
motivation–behavioural-skills model
16,20,23,26–29
and social cognitive
theory25,30–32. These models collectively suggest that knowledge
(a collection of facts about an object of behaviour, typically held with
certainty even though they might be factually incorrect32), beliefs (prob-
ability judgements about an object in connection with an attribute or an
outcome
32
), general and behavioural attitudes (evaluations of objects
or behaviours, respectively, along a positive–negative dimension
33
),
emotions (visceral feelings associated with an objec t or behaviour33),
general and specific skills (cognitive skills involved in self-control30 or
domain-specific cognitive or motor skills, respectively
30
) and habits
(repeated, automated behaviours that continue even in the absence of
rewards
34
) are important determinants of behaviour and/or potential
targets for behaviour change.
For example, according to the reasoned ac tion approach, beliefs
that performing a behaviour will lead to various outcomes and the
evaluations of those outcomes influence attitudes and subsequent
intentions to execute a behaviour20. According to the information–
motivation–behavioural-skills model, information entails knowledge
about the behaviour in question, motivation comprises attitudes,
norms and intentions, and behavioural skills encompass routines
that facilitate a behaviour and associated feelings of self-efficacy or
perceived behavioural control24,27,30,35. Emotions, habits, general atti-
tudes and general skills are part of the integrative model of behavioural
prediction and change
21
and have been shown to be important for self-
regulation
29
. They are also incorporated as external variables within
the reasoned action approach29.
One problem with existing models of behavioural prediction
and change is a relative neglect of social and structural factors2,36. For
example, although the reasoned action approach posits that social
norms influence intentions, intentions are still an individual factor.
Introduction
During the past 5 years, humanity has been confronted with extraordi
-
nary social, environmental, political and economic challenges, includ-
ing pandemics and epidemics, threats to natural habitats and climate,
and community, state and police violence. The science of behaviour
change can identify efficacious interventions to change behaviours
that might be central to solving these crises. Thus, it is important to
understand the degree to which correcting misinformation, modifying
cultural beliefs, or changing norms or legal sanctions will, for example,
increase vaccination or decrease energy usage.
Previous work has provided taxonomies of the tools available to
change behaviour1–3. For example, a review and expert judgements were
used to classify behavioural change interventions, determine whether
they were based on behavioural change principles and, then, organ-
ize them into displays that enable practitioners to visualize possible
tools at their disposal
4,5
. However, despite its descriptive value, this
taxonomy is not informative about the relative inter vention efficacy
of different approaches. An intervention based on ‘behavioural change
principles’ does not guarantee success, therefore leaving the question
of efficacy unaddressed.
Other relevant work has produced estimates of specific strategies
across behaviours, but these estimates are typically obtained by com-
parison with a control group rather than other strategies
6–9
. For exam-
ple, past reviews of the efficacy of implementation intentions (forming
if–then plans to execute a behaviour) or normative appeals10–13 are not
informative about whether implementation intentions are more or less
efficacious than behavioural skills training, or whether interventions
that make group norms more apparent are more or less efficacious than
programmes that aim to increase the trustworthiness of institutions.
Existing reviews that do compare the efficacy of interventions
across different targets have been circumscribed to specific domains,
such as health
14
, climate change mitigation
15
and human immunode-
ficiency virus (HIV) prevention and care16–18. This traditional focus on
single behavioural domains might arise because research funding
is often allocated by problem (as illustrated by the disease-specific
organization of the National Institutes of Health (NIH), the main health
research funding agency in the USA) or because researchers are often
trained in siloes and assume that each issue is unique.
From a theoretical standpoint, understanding a broad spectrum of
behavioural domains is critical to a generalizable behavioural change
model. From a practical standpoint, new behavioural change challenges
will continue to surface. For example, before the COVID-19 pandemic,
no research had examined how to promote widespread masking, social
distancing or adherence to lockdown measures. Thus, reviewing targets
of behavioural change across domains is essential for well-informed
public health decisions in unprecedented situations.
In this Review, we synthesize disparate bodies of research to
facilitate decisions about what behavioural change targets to choose
when designing an intervention. First, we define a parsimonious set of
individual and social-structural determinants of behaviour based on
existing theories, supplemented by an extensive review of the literature
and author verification that the final groupings were meaningful, par-
simonious and relatively homogeneous. Next, we summarize the meta-
analytic evidence for correlations between each naturally occurring
determinant (for example, knowledge) and behaviour, as well as meta-
analytic effect sizes for experimental and quasi-experimental tests
of the efficacy of behavioural interventions that target that determi-
nant (for example, interventions that provide information to increase
knowledge). We conclude by organizing intervention approaches into

Nature Reviews Psychology
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Similarly, even though social cognitive theor y emphasizes the impact of
others as models of behaviour
30
, the theory also includes self-efficacy
and personal agency, which are individual factors.
Nevertheless, several theories suggest important social-structural
determinants of behaviour that could be targets of behavioural change.
For example, there are theoretical distinctions between injunctive
norms (perceptions of the degree to which others support a person’s
behaviour20,37) and descriptive norms (subjective estimates of the
frequency of a behaviour in a particular population
38–41
)
37
, and these
two norms do not always correlate with each other (r = 0.1–0.4)42–44.
There are also theoretical distinctions between regulatory and distrib-
uted policies45, which led to our decision to separate formal legal and
administrative sanctions (legal and administrative instruments to ban
or punish a behaviour) from institutional trustworthiness (justice or
fairness within an organization or government entity, which increases
trust and reduces vigilance46–48), which can often be achieved in infor-
mal ways such as demonstrating benevolence(ref.49 and A. H. Jung
et al., unpublished data). Moreover, material incentives (provision of
financial or non‐financial rewards) can affect the motivation to perform
a behaviour and are theoretically important drivers of behaviour50,51.
The literature also suggests other social-structural factors that
might be particularly relevant for determining behaviour and driving
behaviour change. For example, a large literature suggests that social
support influences human behaviour52, and increasing the feasibility
of behaviour such as through access and defaults
2
(material or logistic
resources to facilitate the performance of a behaviour) or monitors and
reminders53,54 (physical or digital instruments that track behavioural
performance and alert users of the need to execute a behaviour) is an
important aspect of intervention design53,54.
In sum, the classification of individual and social-structural deter-
minants of behaviour we use in subsequent sections based on the
above considerations is more comprehensive and theor y-based than
classifications of nudges
1
and considerably more parsimonious and
theory-driven than classifications of behavioural change techniques
55
.
Individual determinants and interventions
Individual determinants of behaviour include knowledge, beliefs, atti-
tudes, emotions, skills and habits (Table1). In this section, we synthesize
results from meta-analyses of correlational studies that measure the
determinant along with the behaviour in question (Supplementary
Table1) and meta-analyses of randomized controlled trials, quasi-
experimental studies and laboratory research of behavioural change
interventions based on these determinants (Supplementary Table2).
Determinants are discussed in order from least to most effective when
targeted by interventions.
In comparing effect sizes across studies, readers should keep in
mind their meaning (Table2) and interpretational limitations. For
example, in a correlational study, an odds ratio of 2 between knowl-
edge and behaviour implies that for each increasing unit in the meas-
ure of knowledge, the probability of behaviour doubles. However,
correlational studies do not inform the degree to which changing
knowledge will produce a change in behaviour. Similarly, in an inter-
vention context, an odds ratio of 2 implies that the behaviour is twice
as likely following exposure to a knowledge-based intervention relative
to the control group. However, in both cases, the ultimate meaning of
the effect size depends on the baseline probability of executing the
behaviour. An odds ratio of 2 implies much greater savings in energy
if 30% of the control group saves energy than if only 3% of the control
does so.
Knowledge
Knowledge links an object or behaviour to an attribute or event with
absolute certainty and is often formally imparted through educational
efforts. For example, knowledge that a COVID-19 vaccine exists or that
human activity contributes to climate change is accepted by many
individuals and endorsed by governments. The associations bet ween
knowledge and behaviour are often studied under the umbrella of
‘literacy’, which involves a body of facts and mental models in a par-
ticular domain. For example, financial literacy (a person’s f inancial
knowledge
56,57
) correlates with desirable financial behaviours at r = 0.29
(ref. 57). However, the association between financial literacy and behav-
iour is extremely small (r = 0.09) when the behaviour is measured after
the measure of literacy was obtained instead of before (ref. 57).
There is also extensive research on the relation between literacy
and behaviour in the health and environment domains, but effects
are small (Supplementary Table1). For example, there is a negligi
-
ble association between oral health literacy and visiting the dentist
(OR = 1.25)58 and between HIV knowledge and actual condom use
(r = 0.06)44, and small associations between recycling literacy and recy-
cling (r = 0.20)59 and between climate change knowledge and climate
change-adaptation behaviours such as supporting environmentally
friendly policies or relocating in response to climate change (r = 0.14)60.
One potential explanation for the lack of a sizable correlation between
knowledge and behaviour overall (Fig.1a) is that the knowledge is
only tenuously related to the behaviours being studied. For example,
knowledge related to alcohol and its effec ts might be inconsequential
if drinking is related to normative or other beliefs20.
Interventions that target knowledge involve education (for exam-
ple, systematic instruction to individuals or groups) and other didactic
approaches intended to reduce a knowledge deficit. Meta-analyses of
behavioural effects suggest that these interventions produce negligible
effects (Fig.1b). For example, educational approaches have a negligi-
ble effect on climate change mitigation (d = 0.09)15. Similarly, a meta-
analysis of vaccination interventions showed that neither providing
information in general nor attempting to correct misinformation
increases vaccination uptake (OR = 1.04 and OR = 0.94, respectively)
(S. Liu et al., unpublished).
Importantly, some of the effect sizes derived from the correlational
evidence are larger than the largest effects obtained from intervention
studies. Thus, using correlational evidence to make inferences about
interventions might lead to the selection of ineffective programmes.
Even more critical is the fact that the efficacy of knowledge as a target
of change is negligible. From this standpoint, building a campaign or
programmes to increase knowledge is likely to leave policymakers and
constituents disappointed.
General skills
Broad behavioural and cognitive skills (for example, the ability to con-
trol attention during tasks or inhibit temptations when behaviours
require high levels of self-control) are small predictors of behaviour
(Fig.1a). For example, prosocial skills are not signif icantly correlated
with obtaining employment during adolescence (overall OR = 1.03)61
and executive functioning skills (which comprise inhibitory control
and cognitive flexibility) correlate only at r = –0.14 with disinhibited
eating62.
Many behavioural change programmes have emphasized the need
to train general skills that might help individuals tocontrol undesirable
behaviours
62
. Other interventions are based on mindfulness principles,
with the rationale that mindfulness can reduce aggression and other

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impulsive behaviours. A meta-analysis of mindfulness interventions
for children and adolescents found a small effect on reducing negative
behaviours (d = 0.21)
63
. Overall, the effect of general skills inter ventions
is negligible (Fig.1b).
General attitudes
Psychologists have long considered whether general attitudes towards
objects (for example, attitudes towards recycling) predic t behaviour
(for example, actual recycling). A narrative review from the late 1970s
found that of 54 studies of the relation between general attitudes and
behaviour, 25 showed null results and those that showed significant
results rarely exceeded an effect size of r = 0.40 (ref. 64). More recent
meta-analyses suggest that the relation between general attitudes
and behaviour is quite small (d = 0.22 (ref. 65) and r = 0.14 (ref. 66)),
whereas others suggest that the relation is much stronger (r = 0.39)67.
An interesting wrinkle in the study of general attitudes is the pro-
posal that researchers measure implicit attitudes in addition to the
traditional measures of attitudes used in the meta-analyses described
above. Implicit attitude measures are designed to capture relatively
automatic evaluative responses through spontaneous participants’
judgements or timed responses to a task
68–71
. In the implicit association
test, for example, implicit attitudes are measured by comparing the
time required to pair an object with the concept ‘good’ with the time
required to pair an object with the concept ‘bad’
72
. However, these
measures have produced negligible to medium associations with
behaviour as well. For example, in the area of substance use, there is a
medium association between implicit attitudes towards legal and illegal
psychoactive substances and substance use (r = 0.27)73.
Whereas the overall association between general attitudes
and specific behaviours is medium in size (Fig.1a), the effect size
Table 1 | Individual determinants of behaviour and associated measures and interventions
Determinant Deinition Example measures Example interventions
Knowledge Collection of facts about an object or behaviour,
which can include information about the
properties and consequences of a particular
object or event, such as a virus or pollution;
knowledge links an object or behaviour to an
attribute or event with absolute certainty
Measure of literacy: “Contact with a dirty
toilet is a common cause of venereal
disease or sexually transmitted disease”
(participant responds ‘true’ or ‘false’)213
Health education
Didactic instruction about climate
change in schools
General skills Cognitive or overt routines that enable individuals
to carry out various speciic behaviours; they
involve broad capacities such as controlling
attention during tasks and being able to inhibit
temptations when behaviours require high levels
of self-control
Self-report measures of self-control, which
include statements about a person’s ability
to make a plan or avoid temptations214
Behavioural change programmes
emphasizing the need to train general
skills that might help individualsto
control undesirable behaviours137
General attitudes Evaluations of objects, persons and events; for
example, prejudice is a negative judgement of
a group as the attitude object, and an attitude
towards cars is a positive or negative evaluation
of cars as the attitude object; this type of attitude
is often termed ‘attitude towards the target’23,215
Likert-scale measure of attitudes towards
environmental protections: “Humans are
severely abusing the environment”216
Implicit attitude test concerning alcohol217
Mass-media health-promotion
campaigns about a behaviour79
Interventions aimed at weakening
associations by instilling goals and
threat80
Beliefs Subjective assignments of probability that an
object or behaviour has a given attribute or
outcome32,218
Self-report measure of conspiracy beliefs:
“To what extent do you think the virus is
part of a biological warfare program?”219
Messages that explicitly introduce
expectations about a behaviour
Growth mindset interventions in
academic settings
Emotions Visceral feelings (for example, happiness or fear)
associated with a particular object, person or
event; experiencing fear of climate change or
disgust about a particular group of individuals are
examples of emotion
Likert-scale measure of emotions
towards COVID-19: “I feel fearful about
COVID-19”220
Emotional appeals that sensitize
audiences to risks and include discussion
of the threat posed by a problem or the
audience’s susceptibility to it
Behavioural skills Routines that enable people to execute a target
behaviour, often relected in higher levels of
perceived control or eficacy concerning the
behaviour30,134,218
Self-report measure of behavioural control
and conidence to perform or abstain from
a behaviour: “If I wanted to, it would be
easy for me to exercise for at least twenty
minutes, three times a week for the next
fortnight”221
Practising and receiving feedback on the
behaviour and performing homework
related to the behaviour27,129
Asking individuals to formulate
implementation intentions222,223
Behavioural
attitudes Evaluations of a behaviour as good or bad; for
example, whereas an attitude towards cars is a
general attitude, an attitude towards driving a car
for transportation is a behavioural attitude; this
type of attitude is often referred to as ‘attitude
towards the behaviour’23
Semantic differential measures of attitudes
linking recycling to adjectives such as
good or bad: “Recycling household waste
for me is something …” (participant selects
from ive-point response scale anchored
by adjectives ‘good’ and ‘bad’)224
Mode of questioning designed to
uncover and reduce attitudinal
ambivalence towards a particular
behaviour144,145
Habits Behavioural routines that have acquired features
of automaticity225, meaning that they occur
eficiently, without awareness, or continue even
without intention and after they are no longer
adaptive151,226
Measure of handwashing habit: “Washing
my hands would require effort not to do”2 27 Training to stop a behaviour when faced
with temptations157,158
Introducing environmental regularity
to promote habit formation150
Distracting oneself from behavioural
cues159

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corresponding to intervention efficacy is negligible (Fig.1b). For
example, a meta-analysis of mass-media health-promotion campaigns
revealed a negligible effect on behaviour change (r = 0.05)74. Moreover,
a meta-analysis found that although various techniques led to shifts
in implicit attitudes, these trainings had little effect on behaviour. For
example, interventions that aimed to weaken associations between
an object and a particular evaluation had a negligible influence on
behaviour (g = –0.10)75.
Clearly, people report general attitudes that correlate with their
behaviours even though attempts at changing these attitudes have a
much lower efficacy potential than the correlational evidence suggests.
It might be that people rationalize their behaviour when they report
general attitudes (consistent with research on cognitive dissonance
and self-perception
76–78
), even though those attitudes did not have a
causal role in producing behaviour. Regardless, general attitudes are
relatively inconsequential targets of change.
Beliefs
Similar to knowledge, specific beliefs about an object or behaviour
have positive relations to behavioural performance (Fig.1a). However,
there is a range of effect sizes across domains, with larger effect sizes for
environmental versus health behaviours. For example, a meta-analysis
of the determinants of recycling found medium correlations between
expectations of positive feelings if one recycles or negative feelings if
one does not recycle correlate with actual recycling (r = 0.26)59 (note
that expectations of feelings are beliefs in the probability of experienc-
ing particular emotions and not emotions themselves). By contrast, the
correlations between condom use and the perceived attractiveness
of condoms (r = 0.14) and the belief that condom use protec ts people
from HIV infection (r = 0.10) are small, and the correlation between
condom use and the belief that purchasing condoms is embarrassing
is negligible (r = –0.05)44.
Specific beliefs have also been investigated in the context of con-
spiracy theories. Intuitively, endorsing COVID-19 conspiracy theories
might seem quite consequential for the likelihood of engaging in activi-
ties such as wearing a mask or social distancing. However, the effects
are not unlike those of knowledge and other beliefs (Supplementary
Table1). In fact, a meta-analysis of crossed-lagged correlations from
17 samples estimated the impact of conspiracy beliefs on risky COVID-
19-related behaviour to be β = 0.09 (ref. 79) with a reciprocal effect from
behaviour to beliefs of similar magnitude. Thus, even these dramatic
beliefs exert negligible effects on behaviour.
Other commonly studied beliefs are cultural. These beliefs entail
judgements related to religiosity, spirituality, fashion, food consump-
tion, interpersonal relationships and the relative standing of different
social groups, including interactions among group members and with
other groups80. Cultural beliefs can act as barriers to action when the
recommended behaviour is incongruent with cultural beliefs. For
instance, cultural beliefs can constitute roadblocks to participation
in community-based health insurance when a culture views prepara-
tion for illness as a magnet for illness itself
81
. Similarly, cultural beliefs
about food consumption, which designate which foods are healthy
or unhealthy, can act as a barrier to the management of diabetes
when they conflict with recommendations provided by health-care
professionals82.
Quantitative reviews have estimated the relation between differ-
ent kinds of cultural beliefs and behaviour. For instance, hostile sexism
(a collection of negative beliefs about the role of women in society and
their relation to men) has a medium correlation with male-to-female
violence (z = 0.26), whereas the relation between benevolent sexism
(a collection of beliefs that women have positive qualities but need to be
protected) and male-to-female violence is negligible (z = 0.05)83. As for
religious beliefs, greater religiosity correlates with lower engagement
in criminal behaviour (r = –0.12)84 and a combination of religiosity and
spirituality correlates with less physical aggression (r = –0.12) and less
sexual aggression and domestic violence (r = –0.05), albeit weakly
85
.
More generally, greater religious involvement is associated with less
engagement in destructive behaviour (z = –0.17) and more engage-
ment in constructive behaviour (z = 0.20)86. However, some Christian
groups are philosophically opposed to what they consider unnecessary
medical intervention, resulting in disparities in vaccination coverage
across religions87.
Cultural beliefs have important implications for many behav-
iours
88–91
. For example, in the USA, Hispanic people have the lowest
rates of smoking among all racial and ethnic groups
92
, probably owing
to less acculturation (the degree to which people from minority groups
retain their native cultural language and values relative to those of the
new, dominant culture93) than other groups94. Furthermore, the preva-
lence of risky behaviours, including smoking, obesity and unhealthy
eating and drinking habits, is higher among second-generation Ameri-
cans born in the USA than first-generation immigrants to the USA
(r = 0.01–0.28). It seems that individuals living in the USA but born in
other countries (for example, Mexico and China) have closer ties to
their traditional cultures, which promote healthier lifestyle choices
95
.
This ‘immigrant paradox’ characterizes the situation of immigrants
who practised healthy dietary behaviours in their home countries but
abandon them as they acculturate to their new country of residence
90
.
When existing interventions fail to meet the needs of racial and
ethnic minority groups, culturally tailored programmes can be devel-
oped by modifying the content, language, mode of delivery or other
intervention components in existing interventions or new programmes
that consider cultural context can be developed based on the group’s
concerns
96
. However, the impac t of culturally tailored interventions on
health behaviours is seemingly negligible (g = 0.1–0.20)
97
. For exam-
ple, interventions designed to address hypermasculinity (machismo)
beliefs among Hispanic adolescents are successful at reducing the like-
lihood of engaging in HIV risk behaviour by 32% relative to participants
in the control groups98, whereas a cultural adaptation of a substance use
intervention for Latinx adolescents had a negligible effect (g = 0.06)
99
.
In fact, five out of seven of the effects of such cultural adaptations were
negligible (Supplementary Table2).
As with knowledge and general attitudes, the effect sizes for beliefs
derived from the correlational evidence (Fig.1a) are larger than the
largest effects obtained from intervention studies (Fig.1b). For exam-
ple, confidence in one’s ability to grow in a particular domain (growth
mindset) is associated with improved performance in academic
settings100–102. Accordingly, interventions have been developed to
Table 2 | Interpretation of effect sizes
d or g r or zOdds ratio or risk ratio
Negligible <0.2 <0.1 <1.44
Small 0.2–0.49 0.1–0.23 1.44–2.47
Medium 0.5–0.79 0.24–0.36 2.48–4.26
Large ≥0.8 ≥0.37 ≥4.27
d or g, standardized mean difference; r, Pearson correlation coeficient; z, standardized r
coeficient.

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change mindsets in the hope of also improving academic performance.
However, a meta-analysis of these interventions found a negligible
effect on behaviour both in experiments that successfully altered
mindset (d = 0.04) and when considering all experiments (d = 0.05)8.
Emotions
Experiencing fear of climate change or disgust about a particular group
of individuals present examples of emotion. Emotional appeals are
commonly used to sensitize audiences to the risks of an object or event
and include discussion of the threat posed by a problem or the audi-
ence’s susceptibility to it
103
. Emotions feature prominently in behav-
ioural change models (for example, the health belief model104–106).
However, the correlations between negative emotions and/or risk and
behaviour tend to be small or negligible. For example, there is a small
correlation (r = 0.12) between anxiety about COVID-19 and COVID-19
protective behaviours (although the correlation with fear is medium in
size, r = 0.24)107. Similarly, the association between perceived climate
change risk and past adaptation behaviour is only r = 0.10 (ref. 60), and
the association between perceived HIV risk and condom use is only
r = 0.06 (ref. 44). The results tend to be similar for other forms of per-
ceived threat (Supplementary Table1). For example, in the domain of
condom use, the associations between worry or concern and perceived
HIV severity are r = 0.09 and r = 0.02, respectively44.
Social emotions (emotions that serve primarily social functions
and involve reactions to how the self is perceived by others, such as
pride, gratitude, guilt, anger and envy108,109) have garnered attention
from behavioural scientists studying interpersonal behaviours. For
example, people’s tendency to experience anger while driving has
a small association with speeding behaviour (r = 0.12)110 and is more
strongly associated with a composite of high-risk driving behaviours
(r = 0.39)111. As other examples, envy has a weak negative relation with
positive workplace behaviours such as help-seeking (r = –0.21 to 0.05;
median r = –0.03) and a stronger relation with negative workplace
behaviours such as incivility (r = 0.27–0.33; median r = 0.29)112. Likewise,
guilt is associated with greater engagement in pro-environmental
behaviours (r = 0.30)113; gratitude is associated with prosocial behav-
iour (r = 0.26)
114
; the affective experience of interpersonal attraction
is correlated with a behavioural composite of amount of talking, head
Legal and administrative sanctions
Trustworthiness
Injunctive norms
Monitors and reminders
Descriptive norms
Material incentives
Social support
Access
General skills
Beliefs
Knowledge
Emotions
General attitudes
Behavioural skills
Behavioural attitudes
Habits
Material incentives
Other structural factors
Access
Injunctive norms
Descriptive norms
Trustworthiness
Knowledge
General skills
General attitudes
Beliefs
Emotions
Behavioural skills
Behavioural attitudes
Habits
0 1 2
1.81
1.89
2.1 5
2.52
2.58
3.43
3.70
1.24
1.71
2.03
2.48
3.01
3.19
6.17
1.11
1.23
1.35
1.43
1.60
1.99
2.09
2.65
0.80
1.27
1.53
1.90
2.20
2.47
2.53
4.89
3 4 5 6 7 8
0 3 6 9 12 15
0 1 2 43 5 6
0 3 6 12915
Meta-analyses of determinantsaMeta-analyses of interventions
Odds ratio Odds ratio
Odds ratio
Individual factorsSocial-structural factors
Social-structural factors Individual factors
Odds ratio
b
Mean
Maximum
Minimum
Fig. 1 | Effect size range in meta-analyses of behaviour change. a,b, Range
(minimum, red; maximum, yellow) and mean (line) of effect sizes (odds ratios)
for meta-analyses of individual (Supplementary Table1) and social-structural
(Supplementary Table3) determinants of change (panel a) and for meta-analyses
of intervention studies that targeted individual (Supplementary Table2) and
social-structural (Supplementary Table4) determinants (panel b). Only meta-
analyses that excluded extreme publication bias are included (Supplementar y
Note 1). Mean odds ratio values are presented above the mean line. Odds ratios
<1.44 are negligible, those ≥1.44 but <2.48 are small, those ≥2.48 but <4.27 are
medium and those ≥4.27 are considered large.

Nature Reviews Psychology
Review article
nodding and sitting distance (r = 0.20)
115
; and emotional prejudice is
more strongly associated with discriminatory behaviour (r
median
= 0.35)
than stereotypes and other beliefs
116
. Finally, even though social emo-
tions are not consistently associated with purchasing behaviour
(rgratitude = 0.50; rpride = 0.07; rguilt = –0.01; ranger = –0.19), they have medium
to large correlations with sharing behaviour (r
gratitude
= 0.74; r
pride
= 0.32;
r
guilt
= 0.54; r
anger
= –0.38)
117
. However, these strong associations with
sharing behaviour might partly be a function of the lower cost of this
behaviour (operationalized as complaining and word of mouth in the
source meta-analysis) compared with purchasing behaviour.
Generally, inducing emotions influences behaviour (g = 0.31)
118
.
Although negative emotions have been found to have no overall effect
on food consumption (g = 0.02), positive emotions increase food intake
(g = 0.24)
119
. Likewise, communicating to induce fear tends to have
small effects (Supplementary Table2). For example, communicating
the level of genetic cardiometabolic risk to patients has no effect on
dietary changes or weight loss
120
. Moreover, despite occasional claims
of backfire effects
121
, a comprehensive meta-analysis of fear-appeal
experiments found that the effects of risk information and fear were
positive but negligible in size (d = 0.20 (ref. 122) and d = 0.14 (ref. 103),
respectively). Furthermore, inductions of both anticipatory emo-
tions (for example, fear and worry; d = 0.21) and anticipated emotions
(for example, regret, guilt and shame; d = 0.30) produce positive but
small changes in the enactment of behaviour
123
. All in all, the effects of
emotions are small.
Many interventions have targeted social emotions to bring about
behavioural change
124
. In particular, gratitude interventions are popu-
lar in the positive psychology literature
125
. However, the overall effects
of gratitude interventions are small. For example, meta-analyses have
found negligible effects of gratitude interventions on exercise (d = 0.10)
and prosocial behaviour (d = 0 and d = 0.12)114, and a stronger but still
small effect on behaviours that express gratitude (for example, writing
a thank-you note; d = 0.40)125.
As with the other individual determinants reviewed thus far, the
effect sizes for emotions are stronger in correlational than intervention
studies. Although the available evidence suggests medium correlations
between emotions and behaviour (Fig.1a), concluding that they might
be a desirable avenue for intervention could lead to underwhelming
results as the efficacy of emotion-based interventions is small (Fig.1b).
Behavioural skills
Specific behavioural skills show a medium-sized correlation with
actual behaviour (Fig.1a). For example, mothers who have the skills
to discuss birth control methods with their daughters are 5.69 times
more likely to have their daughters vaccinated against human papil-
loma virus (HPV) than mothers who lack such communication skills
126
.
In addition, specific behavioural skills are often reflected in people’s
sense of the controllability of a particular behaviour (perceived
behavioural control)
22,127
. For example, according to meta-analyses,
perceived behavioural control has a strong association with actual recy-
cling (r = 0.39)
59
, and confidence that one can refuse alcohol (refusal
self-efficacy) has medium associations with the frequency of drinking
(r = –0.35), the quantity of alcohol consumed (r = –0.29) and binge
drinking (r = –0.32)128.
Behavioural skills interventions involve receiving arguments
about the execution of a set of skills, as well as observing a role model
execute a behaviour, practising and receiving feedback on the behav-
iour, and performing homework related to that behaviour
27,129
. For
example, verbal arguments might be used to encourage individuals
to secure resources for and overcome obstacles to wearing a condom
during sex130 and more practical behavioural skill training interven-
tions involve role-playing the application of condoms16. Teenag-
ers might practise refusing invitations to smoke cigarettes or drink
alcohol131–133, and adults might be taught to avoid drinking before or
during sex or to monitor their emotional states to avoid risky sexual
situations27,134.
Meta-analyses of these types of interventions have shown that
training behavioural skills provides benefits for behavioural change.
For example, communication skills training effectively increases
both safer-sex discussions with partners (d = 0.35) and condom use
(d = 0.39)135. Organizational training across various such as inter-
personal communication also produces sizable improvements in
work behaviour (d = 0.62), particularly for programmed instruction
(d = 0.94), which is given in small, specific steps that require a correct
response before the learner moves to the next step
136
. Although the
overall effect size for inter vention efficacy is small (Fig.1b), behavioural
skills are among the more promising targets to achieve behavioural
change and have more sizable effects than general skills (d = 0.62
(ref. 136) versus d = 0.30 (ref. 137)).
Behavioural attitudes
Studies of attitudinal determinants involve analyses of associations
with behavioural attitudes as well as indirect measures of behavioural
attitudes (beliefs about behavioural outcomes weighted by evaluations
of those outcomes20,138). A general meta-analysis of newly formed atti-
tudes estimated that the link between attitudes towards behaviours
and actual behaviour is large (r = 0.58)
139
. These findings are supported
by meta-analyses in other domains. For instance, there is a medium
correlation between attitudes towards sun-protection behaviour
and actual sun-protection behaviour (r = 0.31)
140
, and large correla-
tions between attitudes towards car use and actual car use (r = 0.41)
141
,
attitudes towards consuming organic vegetables and organic veg-
etable consumption (r = 0.44)
142
, and attitudes towards condom use
and actual condom use (r = 0.38)138. Similarly, indirect attitude meas-
ures show a medium correlation with condom use (r = 0.31)
138
. Thus,
behavioural attitudes are generally better predictors of behaviour than
general attitudes, knowledge and specific beliefs (Fig.1a).
Interventions targeting behavioural attitudes include media mes-
sages or in-person discussions of the benefits of changing a behav-
iour130,143, as well as motivational interviewing designed to reduce
attitudinal ambivalence towards a particular behaviour144,145. However,
interventions to change attitudes towards behaviours are generally
comprehensive and include other strategies such as targeting norms
and perceived behavioural control20,127. Consequently, many interven-
tion studies provide little information on the specific impact of targeting
behavioural attitudes. Laboratory experiments designed to impact
behavioural attitudes as a way of influencing behaviour have found large
effects on behaviours (d = 1.10 and d = 0.79)146, but the effects of actual
interventions are typically small (Supplementary Table2). Overall, the
effects of behavioural attitude interventions are small (Fig.1b).
As with the other individual determinants, the differences in effect
sizes between correlational and intervention studies are considerable.
Importantly, the correlational studies that find the strongest associa-
tions measured behaviour in the laboratory
139
and involve behaviours
that exist only in those contexts (for example, voting in support of a
fictitious policy as part of an experiment
139,147
). Consequently, these
experiments are poor representatives of the complex decisions people
make when attitudes coexist with other factors.

Nature Reviews Psychology
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Habits
Past behaviour is an important precursor of future behaviour. For exam-
ple, past condom use has a medium correlation with current condom
use (r = 0.36)
44
, and past recycling behaviour has large correlations
with future recycling (r = 0.41)
59
and seeing oneself as a person who
recycles (r = 0.48)59.
Habits have been equated with past behaviour in many analyses
148
.
However, contemporar y theories define habits as repeated behaviours
that exhibit automaticity, occur without awareness and are difficult to
stop even when they no longer provide benefits to the individual1 49–154.
A meta-analysis of associations between health-provider habits (for
example, handwashing) measured with habit scales that tap into auto-
maticity showed a medium association with the execution of those
behaviours (r = 0.33)
155
, and another meta-analysis found a large asso
-
ciation between car habits and car use (r = 0.50)156. In sum, habits have
large associations with behaviours (Fig.1a).
Habit-promoting interventions involve157,158 training to stop
behaviour in the face of temptations157,158, introducing environmental
regularity to promote habit formation150 and distracting people from
behavioural cues159. For example, laboratory cognitive training to
inhibit approach to food cues, promote distraction, reappraise food
cravings and use other cognitive control techniques has a small effect
on food intake (g = 0.27), with reappraisal (g = 0.45), attentional bias
modification (g = 0.44) and distraction (g = –0.31) having the strong-
est effects
159
. Similarly, a meta-analysis found that stop signal training
(d = –0.39) and attentional bias modification (d = –0.51) showed small
and medium effects on eating behaviour, respectively160.
Habit reversal training has also been used to reduce tics158. In this
treatment, patients are trained to identify occurrences of the tic and
the events that trigger it and implement a competing, incompatible
response. For example, if stress or hunger increases tics, activation
of antagonist muscles when a tic is expected can eliminate the tic
158
(d = 0.94)157. This treatment changes motor associations with external
stimuli and therefore reduces behaviours that are executed despite
undesirable consequences.
Interventions to curb habits are impressive because they are
fighting against chronic, automated tendencies that are difficult to
eliminate. As with many of the individual factors we considered, the
effects obtained from correlational studies are markedly stronger
than the corresponding effects from intervention studies. Neverthe-
less, interventions to train habits are clearly promising and, among all
individual targets, demonstrate the strongest impact on behavioural
change (Fig.1b). However, they face the challenge of needing to elicit
behaviour before that behaviour can become automated.
Social-structural determinants and interventions
Social-structural determinants of behaviour include legal and admin-
istrative sanctions, trustworthiness, injunctive norms, monitors and
reminders, descriptive norms, material incentives, social support
and access (Table3). Although these determinants reflect social and
environmental conditions, the measures of determinants often rely on
self-report. For example, descriptive norms tap into how much others
perform a behaviour, but measures in correlational studies reflect a
respondent’s perception of what others do. In this section, we synthe-
size results from meta-analyses of correlational studies that measure
the determinant along with the behaviour in question (Supplementary
Table3) and meta-analyses of randomized controlled trials, quasi-
experimental studies and laboratory research of behavioural change
interventions based on these determinants (Supplementary Table4).
Determinants are discussed in order from least to most effective when
targeted by interventions. As noted above, readers should keep in mind
their meaning (Table2) and interpretational limitations when compar-
ing effect sizes across studies.
Legal and administrative sanctions
We identified no meta-analyses of correlations between behaviour
and legal and administrative sanctions. In terms of interventions, poli-
cies that attempt to ban negative behaviour and link it to sanctions
(for example, restricting one’s ability to work or travel if one chooses
not to get vaccinated)161 have been criticized for their potential for
psychological reactance (a negative emotional response caused by
threats to or actual losses of freedom)
162,163
. Specifically, people gen-
erally believe that they possess a certain level of freedom and wish to
have control over their actions. When they encounter events restricting
their perceived freedom, they might become motivated to restore it
by acting against the threatening events. Accordingly, although deter-
rence theory has remained a cornerstone of criminal justice policy,
deterrence-based initiatives have only small to medium effects on
behaviour (r = 0.22–0.33)164 and mandates can sometimes work, as
shown by the success of COVID-19 vaccination mandates in many
places165–167. Collectively, however, legal and administrative sanctions
have a negligible effect on behaviour (Fig.1b).
Trustworthiness
Interpersonal trust is a combination of attitudes, affective reactions and
beliefs about others (for example, health-care providers or politicians)
that reduces interpersonal vigilance and increases vulnerability
46–48
.
For instance, the trustworthiness of an individual delivering a message
has been found to influence its persuasiveness
168–174
. Trust has been
frequently studied in the context of cooperation games, where trust in
one’s game partner strongly predicts altruistic behaviour (r = 0.58)175 .
Trust has also been examined in organizational research, where intra-
team trust is associated with better team performance (r = 0.30)
176
, and
trust in leaders is associated with better task performance (r = 0.26)
and better organizational citizenship behaviour (r = 0.30)177.
Behavioural scientists are also interested in institutional forms of
trust, such as trust in scientists and government institutions. One meta-
analysis found that climate-friendly behaviours correlated with trust
in governmental institutions (r = 0.17), trust in environmental groups
(r = 0.38), trust in industry (r = 0.14) and trust in scientists (r = 0.33)
178
.
However, these associations tend to be stronger for public behaviours
(for example, support of public environmental policies) than for private
behaviours (for example, obtaining health insurance)178.
Notably, specific factors can change the strength and direction of
these associations. For example, there were small correlations between
trust in government institutions and compliance with COVID-19 behav-
ioural guidelines (r = 0.11) and COVID-19 vaccination (r = 0.10)179. How-
ever, trust in former President Donald Trump correlated negatively with
all COVID-19 prevention behaviours
179
. Overall, there is a medium-sized
association between all forms of trustworthiness and behaviour (Fig.1a).
Interventions to increase institutional trustworthiness focus
on increasing the perceived fairness and goodwill of authorities or
organizations, in addition to programmes to increase distributed and
procedural justice. Interventions aimed at improving the perceived
trustworthiness of health-care authorities lead to negligible increases
in behavioural outcomes (g = 0.13)180. Inter ventions to increase distrib-
uted justice at work have produced negligible effects on work perfor-
mance (OR = 1.20)
181
, whereas interventions to increase procedural

Nature Reviews Psychology
Review article
justice have small positive effects on work behaviour (OR = 1.49)
182
.
Overall, however, interventions that aim to increase institutional
trustworthiness have a negligible effect (Fig.1b).
Injunctive norms
Several health behaviour theories (for example, the theories of rea-
soned action and planned behaviour
20,23
, as well as the theory of norma-
tive focus37,183) converge on the hypothesis that social norms influence
behaviour. Injunctive norms (perceptions of the degree to which others
support one’s behaviour
20,37
) have small associations with behaviours
such as blood donation (r = 0.17)
42
, recycling (r = 0.21)
59
and adolescent
sexual behaviour (r = 0.22)
13
. Overall, however, the correlation between
injunctive norms and behaviour is medium in size (Fig.1a).
Over the past five decades, social normative interventions (for
example, messages that communicate that others approve of specific
behaviours) have been used to change environmental behaviours
124
,
child-rearing practices12, health184 and other risky behaviours by making
people feel that others approve of the course of action recommended
in the intervention. A synthesis of these interventions across numerous
domains revealed a small effect on behaviour (d = 0.34)185. The impact of
injunctive norm interventions has also been synthesized in the domain
of environmental behaviour, revealing a negligible effect (d = 0.10)
186
.
Notably, these interventions can have effects because people are una-
ware of the true injunctive norms
187
. For example, if most students drink
heavily because they assume their peers approve of drinking, reporting
disapproving injunctive norms can curb drinking
188
. Overall, however,
interventions that target injunctive norms have small effects (Fig.1b).
Monitors and reminders
We identified no meta-analyses of correlations between behav-
iour and monitors and reminders. In terms of interventions, moni-
tors and reminder interventions can, potentially, delegate monitoring
and reminder functions to the environment and consequently decrease
self-control failures
189
. Manual reminders (for example, tracking sheets
and paper planners) can promote various health screenings, includ-
ing for breast, cervical and colorectal cancer (OR = 1.63, OR = 1.10 and
OR = 1.85, respectively)
190
. However, they fail to influence preventive
care more generally (OR = 0.99)190 and have negligible effects on vac-
cination (OR = 0.95)(S. Liu et al., unpublished). Often, the use of both
manual and computer-generated reminders is most effective (colo-
rectal cancer screening OR = 2.57; all preventative care OR = 2.23)
190
.
Overall monitors and reminders have a small effect (Fig.1b). Thus, they
might be a useful intervention strategy, particularly in combination
with interventions for other targets.
Descriptive norms
Descriptive norms contribute to the social processes that shape a wide
range of behaviours. Although descriptive norms do not correlate with
blood donation behaviour (r = 0.03)42, they do correlate with recy-
cling (r = 0.33)59, adolescent sexual behaviour (r = 0.40)13, consumer
Table 3 | Social-structural determinants of behaviour and associated measures and interventions
Determinant Deinition Sample measures Sample interventions
Legal and
administrative
sanctions
Legal and administrative instruments to
prescribe, ban or sanction a behaviour State and county records of laws
coded through a policy review228 Banning smoking in public establishments229
Mandating vaccination230
Mandating sick pay231
Taxing pollution232
Trustworthiness Justice or fairness within an
organization or government entity,
which leads constituents to follow
recommendations49
Self-report measure of procedural
justice: “How fair were the
procedures used to handle
the problem?”233
Providing channels for Latinx voters to voice their
concerns
Community-oriented policing that fosters
non-enforcement interactions234
Injunctive norms Perceptions of the degree to which
others support a person’s behaviour20,37 Self-report measure of injunctive
norms: “People who are important
to me think I should use condoms”21
Messages that communicate that others approve
of condom use235
Posting signs stating that taking the stairs is a good way
to get some exercise236
Monitors and
reminders Physical or digital instrument to
track behavioural performance and
remind users of the need to execute
a behaviour
Self-reported use of pill boxes,
diaries and planners237 Clinical reminder system for promoting preventive care238
Digital watches and phone apps that promote physical
activity
Descriptive norms Frequency of a behaviour in a particular
population38–41 Self-reported perceptions of what
others do: “Most residents would
vaccinate their child against
COVID-19”228
Comparative feedback such as a chart tracking one’s
energy consumption in relation to one’s neighbours239
Using role models to promote a target behaviour30,31
Posting signs stating that most people used the stairs236
Material incentives Providing inancial or non‐inancial
rewards in exchange for a behaviour Introduction of state lottery for
vaccinated residents as a reward
for vaccinations240
Paying people US$24 to receive the COVID-19 vaccine241
Social support Informational, instrumental or
inancial help to facilitate a particular
behaviour201
Self-reported lists of individuals
who can perform instrumental,
informational and emotional
support functions242
Leveraging family or adhoc groups to assist individuals
to meet their physical activity goals
Groups of Latina mothers led by ‘promotoras’ who support
and accompany each other during health-promoting
activities243
Access Material or logistic resources
to facilitate the performance
of a behaviour
Census demographics and
self-report of health insurance244
Self-reported health insurance245
Reducing co-payments for medication246
Providing health insurance247
Providing basic income248

Nature Reviews Psychology
Review article
behaviour (r = 0.31)
9
and smoking initiation (OR = 1.88–2.53)
43
. Overall,
there is a medium-sized association between descriptive norms and
behaviour (Fig.1a).
Most normative interventions try to persuade recipients that
others already behave in the recommended ways. In fact, simply com-
municating descriptive social norms changes behaviour in various
settings, especially when the desired behaviour is highly prevalent191.
For example, college students tend to overestimate the amount of
alcohol consumed by their peers187 and normative interventions that
revise this misperception reduce drinking
182
. Indeed, meta-analyses of
approaches to modify descriptive norms have shown small effects for
alcohol use (d = 0.29)192 and condom use (d = 0.36)11.
Other normative interventions include providing comparative
feedback such as a chart tracking one’s energy consumption in rela-
tion to one’s neighbours
186
. Although people often dislike comparative
feedback193,194, exercise apps that provide comparative feedback are
highly effective (d = 0.96)195.
Having role models to look up to and learn from
30
is a particularly
influential normative intervention (d = 0.51)
186
. This finding is consist-
ent with evidence that interventions delivered by facilitators who
resemble recipients demographically are more successful at increasing
condom use than interventions delivered by demographically dissimi-
lar facilitators
173
. Overall, interventions that aim to change descriptive
norms have small effects on behaviour (Fig.1b).
Material incentives
Correlational evidence about the effects of material incentives suggests
that offering incentives for biochemically validated samples produces
medium increases in smoking cessation (risk ratio = 2.58)
196
. Other
effects, however, are negligible. For example, receiving state subsidies
is minimally correlated with environmentally friendly application of
pesticides (g = 0.12)197.
Many policies designed to promote human behaviour adopt
behaviourist principles
198
by pairing positive behaviour with incen-
tives (for example, providing financial incentives for choosing to get
vaccinated). However, the overall efficacy of incentives is small (Fig.1b).
For example, financial incentives were offered by many countries to
encourage COVID-19 vaccination but, according to a meta-analysis, the
effects were negligible (OR = 1.44)(S. Liu et al., unpublished). Financial
incentives have also been used to decrease energy consumption, where
the effects are small (d = 0.36)
199
, and to curb substance use, where the
effects are medium (d = 0.70)200.
Social support
Social support (the provision of informational, instrumental or finan-
cial help to facilitate a particular behaviour
201
) has been examined in
relation to stress and health, as well as particularly difficult behaviours
that benefit from external advice and assistance, such as weight loss,
medication adherence and resource conservation. Social support dif-
fers from norms in that, as studied in relation to behaviour, the support
concerns a particular behavioural goal. Whereas social norms might
concern others’ approval for maintaining a healthy diet, social support
implies that others are willing to provide advice or other forms of help
around a particular dietary goal.
There are variable associations between social support and behav-
iour. For example, adherence to medical treatments is 1.74 times higher
among patients with cohesive families and 1.53 times lower among
patients with high-conflict families
152
. Moreover, exercise is facilitated
by support from family and important others (d = 0.36 and d = 0.44) as
well as exercise-class leaders and classmates (d = 0.31 and d = 0.32)153.
In addition, there are large associations between emotional, material
and informational support and the quality of childcare behaviours
executed by mothers (r = 0.31, r = 0.27 and r = 0.31, respectively)154.
The effect of social support interventions is medium (Fig.1b).
These interventions often take the form of support groups that facili-
tate a behaviour such as the dietary or physical activity modifications
required to lose weight. Such social support interventions are associ-
ated with small positive effects on adherence to antiretroviral medi-
cation (OR = 1.66)
202
and a reduction in suicide (OR = 0.48)
203
. Social
support interventions based on public commitments to a behaviour
204
(which can increase a person’s motivation to execute a behaviour
but also social support for it) are associated with small (d = 0.26)15 to
medium (g = 0.58)186 increases in conservation behaviour.
Access
According to social cognitive theory24, environmental attributes
can constrain behaviour and thereby act as critical determinants of
behaviour. For example, increases in the price of pesticides decrease
environmentally friendly pesticide application (d = –0.36)197. Likewise,
demographic variables related to a person’s position within the social
hierarchy have a range of associations with behaviour. For example,
healthy behaviours during pregnancy correlate with income (r = 0.26)205
and having a recycling bin and owning a home both correlate with
recycling (r = 0.16 and r = 0.24, respectively)
59
. Overall, the association
between access and behaviour is small (Fig.1a).
Some access interventions are designed to impact the system at
large. Interventions to decrease inequality are attrac tive, given large
disparities in behaviours that benefit individuals and society at large.
Accordingly, researchers have tested structural and community inter
-
ventions, such as microfinancing, which involves small loans to develop
a business as a source of income. However, randomized controlled trials
testing the impact of microloans showed a negligible effect on women’s
control over household expenses (d = –0.01)206. In the area of health,
broader structural and community interventions have small effects as
well (risk ratio = 1.20 and risk ratio = 0.90 for condom use and number
of partners, respectively)207.
Other policy instruments increase access by changing the environ-
ment to offer more specific opportunities for behavioural change. For
example, interventions that ensure access to vaccines by providing
transportation or sites close to potential users double vaccination cov-
erage (S. Liu et al., unpublished). Other policies design situations that
channel behaviour, such as making the desired behaviour the default
on organ-donation forms (d = 0.68)208,209. Yet others decrease access
by taxing alcohol to reduce use (OR = 5.92)
210
. Overall interventions
that aim to increase access have large effects on behaviour (Fig.1b).
Summary and future directions
Our Review suggests that across domains, knowledge, general skills,
general attitudes, beliefs, legal and administrative sanctions, and trust-
worthiness have negligible effects as targets of intervention; emotions,
behavioural skills, behavioural attitudes, injunctive norms, monitors
and reminders, descriptive norms and material incentives have small
effects; habits and social support have medium effects; and access
has large effects (Fig.2a). Of course, some behaviours, populations
and contexts might be unique. Thus, no review or meta-analysis can
predict the result of an intervention across all contexts. Nevertheless,
our Review suggests that certain variables, although highly salient,
might not change behaviour and should not be the primary focus of a

Nature Reviews Psychology
Review article
behavioural intervention. Moreover, the discrepancies in effect sizes
between correlational studies and intervention studies suggest that
correlational studies are often ill-suited as a basis for deciding what
determinants to address in interventions.
A key aim of our Review was to offer a synthesis across all behav-
iours. To determine the extent to which these conclusions are gen-
eralizable, we examined determinants for health behaviour (Fig.2b)
and environmental behaviour (Fig.2c) specifically. These domains
a
c
b
Knowledge
General attitudes
Behavioural attitudes
Behavioural skills
Beliefs
Emotions
General skills
Habits
Knowledge
General attitudes
Behavioural skills
Beliefs
Emotions
General skills
Habits
Knowledge
Behavioural attitudes
Behavioural skills
Beliefs
sanctions
All
behaviours
Health
behaviours
Environmental
behaviours
Legal and administrative
sanctions
Legal and administrative
Descriptive norms
Injunctive norms
Social support
Access
Material incentives
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Fig. 2 | Models of behavioural change intervention efficacy. a–c, Conclusions
of our synthesis of meta-analyses of behaviour change interventions for all
behaviours (panel a), health behaviours (panel b) and environmental behaviours
(panel c). In all panels, individual targets of change are presented on the left
and social-structural targets of change are presented on the right. Vertically,
targets of change are organized from least to most effective based on the
average effect sizes for each behavioural target (Fig.1b;Supplementary Figs.1
and2), and grouped based on whether effects are negligible, small, medium or
large (Table1). Only meta-analyses that excluded extreme publication bias are
included (Supplementary Note1).

Nature Reviews Psychology
Review article
were chosen because they have been assessed in most meta-analyses
(Supplementary Note2). The distribution of individual determinants for
health behaviour is the same as that for all behaviours. The distribution
is similar for environmental behaviour, except that the data are less com-
plete. The efficacy data for social-structural factors related to health
and environmental behaviours are sparser but still revealing. In both
cases, interventions that target descriptive norms, material incentives,
social support and access are promising, whereas interventions that
emphasize institutional trustworthiness in the health domain and legal
and administrative sanctions or injunctive norms in the environmental
domain might be insufficient to move populations to change.
Thus, the next pandemic and current climate change crisis will
require not knowledge but, rather, active approaches that enable
individuals to circumvent obstacles and gain support, and that ensure
access to resources in ways that promote positive behaviour in all
groups. For example, the US campaign for COVID-19 vaccination tar-
geted vaccine confidence (general attitudes). However, our Review
suggests that it would have been more appropriate to increase access
to vaccination, in addition to training behavioural skills, strengthening
norms, leveraging social support and using material incentives. Our
Review also suggests that behavioural skills training should be effective
to induce behaviours to curb climate change. However, in that case,
actual beliefs also have a small effect, suggesting that the dominant
intervention emphasis of increasing perceptions of climate change
and its outcomes, albeit insufficient, is not misguided.
The next step for intervention researchers is to link these con-
clusions to specific intervention contents and policies. For exam-
ple, randomized controlled trials should test different methods to
change descriptive norms or specific implementations of interventions
designed to increase access to a behaviour. Impor tantly, researchers
and policymakers need to stop repeating programmes that are typically
unsuccessful. For example, although some boilerplate information
about a behaviour should routinely be introduced to an audience not
familiar with a behaviour or the goal of a behaviour, launching large
efforts to test the efficacy of interventions to increase institutional
trust or corrections for misinformation seems futile if the motivation
is behavioural change. Finally, more trials that test different interven-
tion targets are needed so that future research reviews can draw on
more data that better control for populations and contexts. Such
controls are not possible when different experiments test different
targets of change.
Researchers should also study naive theories about behavioural
change among policymakers and their constituents. If policymakers
believe that knowledge is fundamental to behavioural change, they
will continue to implement well-intended but unsuccessful interven-
tions. Likewise, if policymakers consider all targets of change as equally
attractive possibilities without considering their relative efficacy, their
choices are also likely to be misguided. Understanding these naive
conceptualizations and how they translate into behavioural change
initiatives is critical to ensuring that evidence-based findings similar
to those provided here shape the practise of behavioural change.
Any literature review has limitations. First, our review did not
specifically consider that different channels might be used to impart
knowledge or modify beliefs or injunctive norms. For instance, indi-
vidualized knowledge might be imparted to a person who visits with
a dietician, delivered to schools or broadcast on mass media. In these
situations, even when the beliefs exist within the minds of individu-
als, interventions might operate at the individual, school or commu-
nity level. Similarly, policies to increase access to services might be
implemented at the level of an organization, county, state, nation or
group of nations that enter international agreements. Which level or
combination of levels produces the most effective interventions is an
important question for future research.
Second, the choice to synthesize meta-analyses might have biased
our conclusions because some areas have been meta-analysed more
than others. However, meta-analysis remains the only method that
allows for comparisons across research that uses different metrics
211,212
.
A first-order meta-analysis of this large intervention literature might be
an aspirational goal for the field that might be feasible with newer forms
of automation. Until then, our review of meta-analyses is informative
and actionable. Behavioural change is likely to remain one of the most
important solutions to humanity’s challenges, and we must be armed
with more and better guidelines to promote it.
Published online: xx xx xxxx
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Acknowledgements
The authors thank M. Leung for assistance in checking eect sizes and references. The
research was funded by National Institutes of Health (NIH) grants R01MH132415, R01 AI147487,
DP1 DA048570, R01 MH114847 and NSF 2031972 to D.A., and by the Annenberg Foundation
Endowment to the Division of Communication Science at the Annenberg Public Policy Center.
Author contributions
All authors researched data for the article and extracted eect sizes. All authors contributed
substantially to discussion of the content. D.A. wrote the main sections of the article and all
authors contributed to the irst draft and all revisions. All authors reviewed and/or edited the
manuscript before submission.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material available at
https://doi.org/10.1038/s44159-024-00305-0.
Peer review information Nature Reviews Psychology thanks Taciano Milfont, Richard Petty and
the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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