Habit Formation and Change

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Habit Formation and Change
Lucas Carden1 and Wendy Wood1,2
Affiliations: 1Department of Psychology, University of Southern California, 2Marshall School
of Business, University of Southern California.

Abstract
This review highlights emerging findings, topics, and new directions in research on
habitual behavior. We cover how cognitive, attentional mechanisms contribute to habit
formation, how habit is transforming the way we think about self-control, and how focusing on
environmental as well as intrapsychic forces yields more success at habit change. Finally, we
describe studies using 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.

Habit Formation and Change
Ninety-nine hundredths or, possibly, nine hundred and ninety-nine thousandths of our activity is
purely automatic and habitual, from our rising in the morning to our lying down each night. -
William James (1899)
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 learning processes are involved (Wood & Rünger, 2016).
As people repeat behavior in a stable context, their intentions and goals to perform the behavior
gradually become less influential, whereas habits increase in influence (Ouellette & Wood, 1998;
Sheeran, Godin, Conner, & Germain, 2017). A number of recent papers have theorized how the
shift from goal-directed behavior to habit learning occurs in detail (Cushman & Morris, 2015;
Gardner, 2015; Wood & Rünger, 2016). Ironically, this shift in control of behavior does not
often occur in people’s lay theories of habit (for review, see Wood, 2017). People commonly use
volitional, goal-directed explanations for why they had performed their habits, even though
intentions and goals are poor predictors of habit (Neal, Wood, Labrecque, & Lally, 2012).
Psychology has in recent years focused on the flexible responses generated by the
nonconscious activation of goals and attitudes (Weingarten et al., 2016). 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 and semantic associations

(Amodio & Ratner, 2013). To this end, habits are a challenging construct to measure and
manipulate in the lab. As we show, new technologies provide novel insights into habits in the lab
and everyday contexts (Ram, Wang, Currim, & Currim, 2015).
In this paper, we highlight emerging findings, topics, and new directions in research on
habit. Specifically, cognitive, attentional mechanisms appear central to instrumental learning of
habits, understanding of habit has transformed research on self-control (Galla & Duckworth,
2016), behavior change interventions benefit from changing environments (Marteau, Hollands,
Fletcher, 2012; Rothman et al. 2015), and big data provide novel insight into habits on a large
scale (Larcom, Rauch, & Willems, 2017).
Cognitive Processes of Habit Formation
Attentional mechanisms are important in habit formation, given evidence that
instrumental learning guides attention to context cues (Anderson, 2016; Le Pelley, 2016). That
is, stimuli that have been rewarded in the past acquire attentional priority over non-rewarded
ones (Luque et al., 2017). This phenomenon 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 (Anderson, 2016). Through such basic perception and attention systems,
people preferentially recognize environmental features associated with past rewards.
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 (Labrecque & Wood, 2017). With extensive practice, participants were able to quickly
report the next step in the sequence when primed with the prior step. The strength of habit

associations 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 context 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 (1890) 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 (Duckworth, Gendler, & Gross, 2016). This is captured in the
conflicts between one marshmallow now vs. two marshmallows later (Michel, Shoda, Rodriguez,
1989), a farsighted planner and a myopic doer (Thaler & Shefrin, 1981), and a muscle that resists
temptations for a future self (Baumeister, Vohs, & Tice, 2007). In this struggle, habits were
treated as a target of self-control, needing to be inhibited (Quinn, Pascoe, Wood, & Neal, 2010).
More recently, research has recognized James’s claim, highlighting habit as an automatically
activated response that may be consistent with goals (Neal, Wood, & Drolet, 2013).
This shift from habits-as-impediments to habits-as-beneficial is evident in research on
trait self-control (Tangney, Baumeister, & Boone, 2004). We now know that people who score

high on such scales do not engage in much effortful inhibition (Hofmann, Baumeister, Förster, &
Vohs, 2012). In fact, they experience less motivational conflict and report less inhibition of
temptations in daily life compared with people with low self-control (Gillebaart, Schneider, &
De Ridder, 2015; Imhoff, Schmidt, & Gerstenberg, 2014). Instead, people high in self-control
have weak habits for unhealthy activities (e.g., eating junk food, Adriaanse, Kroese, Gillebaart,
& De Ridder, 2014) and strong habits for healthy activities such as sleep, exercise, and work
(Galla & Duckworth, 2015; Gillebaart & Adriaanse, 2017). 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 (Galla & Duckworth, 2015). Furthermore, experimental
research has shown that positive habits actually protect people from conflicting desires and
willpower depletion (Lin, Wood, & Monterosso, 2016).
High trait self-control may thus reflect a kind of situational strategy involving arranging
environmental cues to promote beneficial habit formation (Duckworth, Gendler, & Gross, 2016).
Such people appear to actively avoid situations offering temptations and distractions (Ent,
Baumeister, Tice, 2015). For example, they may reengineer their home or work environments:
make eating healthy snacks easier by placing fruit on their kitchen counters (Sobal & Wansink,
2007; Wansink, Hanks, & Kaipainen, 2015). In education settings, students who used more
situational strategies, such as hiding their cellphone, were more likely to reach their academic
goals (Duckworth et al, 2016).
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 struggles. They form beneficial habits that
are activated automatically by the environments in which they live.
Changing Habits
Behavior change interventions have been challenged to successfully alter lifestyle
behaviors like diet, exercise, environmental sustainability, and financial solvency (Rothman et
al., 2015). For example, the national 5-A-Day-For-Better-Health 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 (Stables et al., 2002).
Another example comes from highly controlled studies designed to change habits using
incentives. These are typically successful in achieving short-term change but fail to maintain
change over time, after the incentives are removed (for review see, Wood & Neal, 2016).
A habit perspective anticipates limited change in behavior 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 (Walker, Thomas, & Verplanken, 2015). The slow pace of habit learning
was shown with a variety of health habits, such as exercising, that develop only after many
months of repetition in stable contexts (Lally, Van Jaarsveld, Potts, & Wardle, 2010).
New directions in habit change include not only changing beliefs and perceptions but also
changing situational factors. We consider these various strategies below.
Implementation Intentions and Reminders
Popular behavior change interventions involve planning and reminders. For example,
implementation intentions help people to remember and act on intentions to change behavior.

Although earlier reviews indicated the effectiveness of implementation intentions (Adriaanse et
al., 2011), a meta-analysis of over 44 diet studies showed only small behavior change effects
during the interventions and negligible long-term effects (Turton et al., 2016). Especially for
strong antagonistic habits, like eating behavior, implementation intentions have little impact
(Webb, Sheeran, & Luszcynska, 2009). Potentially, implementation intentions could promote
habit formation when used to promote repetition in particular contexts (Wood & Neal, 2016).
Interestingly, the meta-analysis revealed more success with food-specific inhibition and attention
bias modification training, (Turton et al., 2016), both of which may target the cognitive
mechanisms underlying habit.
Reminders and symbolic rewards like trophies are common features of web and
smartphone based programs (Stawarz, Cox, & Blandford, 2015). Although reminders may be
effective in the short-term, they can impede habit formation in the long term (Stawarz, Cox, &
Blandford, 2015). Such applications can promote app dependence instead of continued repetition
of a behavior following app use (Renfree et al., 2016). In the future, we predict that such apps
will use context-aware technologies, reminding users to perform behaviors when in specific
environments (see for review, Chen, Ding, Huang, Ye, & Zhang, 2015). In this way, behavior
change apps can facilitate habit formation by connecting specific environmental cues with
desired responses.
Environmental Forces
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 (Verplanken, Walker, Davis, & Jurasek, 2008; Walker, Thomas, &
Verplanken, 2015). 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
households, half of which recently relocated, received an informational intervention to promote
25 environmental behaviors (Verplanken & Roy, 2016). The intervention was more effective for
those who had relocated with the last 3 months (see also Bamberg, 2006; Thøgersen, 2012;
Walker, Thomas, & Verplanken, 2015).
A serendipitous example of how environmental disruption 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 (Larcom, Rauch, & Willems, 2017). 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. The disruption of old cues thus enabled 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 environmental
reengineering interventions that change the structure of everyday decisions (Thaler, Sunstein, &
Balz, 2012; Rozin, Scott, Dingley, Urbanek, Jiang, & Kaltenbach, 2011). Such interventions
typically target a single conscious decision, such as opting-into a program vs. opting out.
Although altering the decision structure may promote habit formation by making it easier to
perform a desired action, habit formation requires repeated responses in a stable context.

Fortunately, single environmental changes, such as dedicating a prominent place for fruits and
vegetables on the kitchen counter, might guide people into ripcurrents (Frey & Rogers, 2014),
potentially leading to a cascade of psychological changes that maintain new behaviors, including
identity (Wilson, 2011) and physical changes such as weight loss (Carels et al., 2014).
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. For example, a smoking cessation study combined ecological momentary
assessment of reported cravings with geo-location mapping (via smartphones) of exposure to
point-of-sale tobacco cues (Kirchner et al., 2013). Relapse rates increased with exposure to
smoking cues, even when participants were not experiencing cravings. This study suggests that
environmental cues direct attention and activate a behavioral response in mind, even when
people are not experiencing a desire to act.
Big data analyses also reveal important social consequences to seemingly mundane
habits, such as how often and where students make purchases on campus (Ram, Wang, Currim,
& Currim, 2015). Instead of survey-based methods to assess student retention, 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 (Anderson, 2016), how
people with high self-control use habits to achieve their goals (Galla & Duckworth, 2016), and
how habits are influenced by environmental disruptions (Verplanken & Roy, 2016). Additional
advances include exciting research on how social interaction habits contribute to intergroup
relations (Hackel, Doll, and Amodio, 2015) and lay beliefs about habit formation and
performance (Carden, Wood, Neal & Pascoe, 2017; for review, see Wood, 2017).
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 (Conklin et al., 2010).
Personalized smoking environments led to stronger cravings than generic environments. This
method holds strong promise for studying many kinds of habits in the lab.
Habits in general stretch researchers’ capabilities because they are interactions between
persons and environments. They reflect the past implicit learning as activated by current context
cues. On the one hand, studying habit involves understanding implicit perceptual, attentional,
and memory processes. On the other hand, habit research involves identifying the visual,
visceral, and social cues, and their combinations, that activate habits in mind. Recent research
discoveries provide a strong foundation to understand both person and situation in these ways.

References
Adriaanse, M. A., Kroese, F. M., Gillebaart, M., & De Ridder, D. T. (2014). Effortless
inhibition: Habit mediates the relation between self-control and unhealthy snack
consumption. Frontiers in Psychology, 5, Article 444. doi:10.3389/fpsyg.2014.00444
Adriaanse, M. A., Vinkers, C. D., De Ridder, D. T., Hox, J. J., & De Wit, J. B. (2011). Do
implementation intentions help to eat a healthy diet? A systematic review and meta-
analysis of the empirical evidence. Appetite, 56, 183-193.
Allcott, H., & Rogers, T. (2014). The short-run and long-run effects of behavioral interventions:
Experimental evidence from energy conservation. American Economic Review, 104,
3003-3037. http://dx.doi.org/10.1257/aer.104.10.3003
Amodio, D. M., & Ratner, K. G. (2011). A memory systems model of implicit social cognition.
Current Directions in Psychological Science, 20, 143-148.
doi:10.1177/0963721411408562
Bamberg, S. (2006). Is a residential relocation a good opportunity to change people’s travel
behavior? Results from a theory-driven intervention study. Environment and
behavior, 38, 820-840.
Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The Strength Model of Self-
Control. Current Directions in Psychological Science, 16, 351-355.
Carels, R. A., Burmeister, J. M., Koball, A. M., Oehlhof, M. W., Hinman, N., LeRoy, M., ... &
Gumble, A. (2014). A randomized trial comparing two approaches to weight loss:
differences in weight loss maintenance. Journal of Health Psychology, 19, 296-311.
http://dx.doi.org/10.1177/1359105312470156
Chen, G., Ding, X., Huang, K., Ye, X., & Zhang, C. (2015). Changing health behaviors through

social and physical context awareness. In Computing, Networking and Communications
(ICNC), International Conference 2015 (pp. 663-667). IEEE.
doi:10.1109/ICCNC.2015.7069424
Conklin, C. A., Perkins, K. A., Robin, N., McClernon, F. J., & Salkeld, R. P. (2010). Bringing
the real world into the laboratory: personal smoking and nonsmoking environments.
Drug
and Alcohol Dependence, 111(1), 58-63.
Cushman, F., & Morris, A. (2015). Habitual control of goal selection in humans. Proceedings of
the National Academy of Sciences, 112, 13817-13822.
Duckworth, A. L., White, R. E., Matteucci, A. J., Shearer, A., & Gross, J. J. (2016). A stitch in
time: Strategic self-control in high school and college students. Journal of Educational
Psychology, 108, 329
Duckworth, A. L., Gendler, T. S., & Gross, J. J. (2016). Situational strategies for self-control.
Perspectives on Psychological Science, 11, 35-55.
Ent, M. R., Baumeister, R. F., & Tice, D. M. (2015). Trait self-control and the avoidance of
temptation. Personality and Individual Differences, 74, 12-15.
Frey, E., & Rogers, T. (2014). Persistence: How treatment effects persist after interventions stop.
Policy Insights from the Behavioral and Brain Sciences, 1, 172-179.
*Galla, B. M., & Duckworth, A. L. (2015). More than resisting temptation: Beneficial habits
mediate the relationship between self-control and positive life outcomes. Journal of
Personality and Social Psychology, 109, 508-525. doi: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.
Gardner, B. (2015). A review and analysis of the use of “habit” in understanding, predicting and
influencing health-related behaviour. Health Psychology Review, 9, 277-295. doi:10.108
0/17437199.2013.876238
Gillebaart, M., & Adriaanse, M. A. (2017). Self-control predicts exercise behavior by force of
habit, a conceptual replication of Adriaanse et al. (2014). Frontiers in Psychology, 8.
Gillebaart, M., Schneider, I. K., & De Ridder, D. T. (2015). Effects of trait self-control on
response conflict about healthy and unhealthy food. Journal of Personality, 84, 789-798.
doi:10.1111/jopy.12219
Hackel, L. M., Doll, B. B., & Amodio, D. M. (2015). Instrumental learning of traits versus
rewards: Dissociable neural correlates and effects on choice. Nature Neuroscience, 18,
1233-1235. doi:10.1038/nn.4080
Hofmann, W., Baumeister, R. F., Förster, G., & Vohs, K. (2012). Everyday temptations: An
experience sampling study of desire, conflict, and self-control. Journal of Personality
and Social Psychology, 102, 1318-1335. doi:10.1037/a0026545
Imhoff, R., Schmidt, A. F., & Gerstenberg, F. (2014). Exploring the interplay of trait self-control
and ego depletion: Empirical evidence for ironic effects. European Journal of
Personality, 28, 413-424. doi:10.1002/per.1899
Kirchner, T. R., Cantrell, J., Anesetti-Rothermel, A., Ganz, O., Vallone, D. M., & Abrams, D. B.
(2013). Geospatial exposure to point-of-sale tobacco: real-time craving and
smoking-cessation outcomes. American Journal of Preventive Medicine, 45, 379-385.
James, W. (1890). The principles of psychology (Vol. II). New York, NY: Henry Holt.

James, W. (1899). Talks to teachers on psychology and to students on some of life’s ideals. New
York, NY: Holt.
Labrecque, J. S., & Wood, W. (2015). What measures of habit strength to use? Comment on
Gardner (2015). Health Psychology Review, 9, 303-310.
doi:10.1080/17437199.2014.992030
Labrecque, J. S., & Wood, W. (2017). Thinking of forming a habit? Manuscript under review.
Labrecque, J. S., Wood, W., Neal, D. T., & Harrington, N. (2017). Habit slips: When consumers
unintentionally resist new products. Journal of the Academy of Marketing Science, 45,
119-133. doi: 10.1007/s11747-016-0482-9
Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed:
Modelling habit formation in the real world. European Journal of Social Psychology, 40,
998–1009. doi:10.1002/ejsp.674
*Larcom, S., Rauch, F., & Willems, T. (2017). The Benefits of Forced Experimentation: Striking
Evidence from the London Underground Network. The Quarterly Journal of Economics.
doi: 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.
Lin, P. Y., Wood, W., & Monterosso, J. (2016). Healthy eating habits protect against
temptations. Appetite, 103, 432-440. http://dx.doi.org/10.1016/j.appet.2015.11.011
Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N., & Wills, A. J. (2016). Attention and
associative learning in humans: An integrative review. Psychological Bulletin, 142,

1111-1140. doi: 10.1037/bul0000064
Luque, D., Beesley, T., Morris, R. W., Jack, B. N., Griffiths, O., Whitford, T. J., & Le Pelley, M.
E. (2017). Goal-directed and habit-like modulations of stimulus processing during
reinforcement learning. Journal of Neuroscience, 37(11), 3009-3017.
Marteau, T. M., Hollands, G. J., & Fletcher, P. C. (2012). Changing human behavior to prevent
disease: The importance of targeting automatic processes. Science, 337(6101),
1492-1495. http://dx.doi.org/10.1126/science.1226918
Mischel, W., Shoda, Y., & Rodriguez, M. L. (1989). Delay of gratification in children. Science,
244, 933–938. doi:10.1126/science.2658056
Neal, D. T., Wood, W., & Drolet, A. (2013). How do people adhere to goals when willpower is
low? The profits (and pitfalls) of strong habits. Journal of Personality and Social
Psychology, 104, 959-975. http://dx.doi.org/10.1037/a0032626
Neal, D. T., Wood, W., Labrecque, J. S., & Lally, P. (2012). How do habits guide behavior?
Perceived and actual triggers of habits in daily life. Journal of Experimental Social
Psychology, 48, 492-498. http://dx.doi.org/10.1016/j.jesp.2011.10.011
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: the multiple processes
by which past behavior predicts future behavior. Psychological Bulletin, 124, 54-74.
http://dx.doi.org/10.1037/0033-2909.124.1.54
Quinn, J. M., Pascoe, A., Wood, W., & Neal, D. T. (2010). Can’t control yourself? Monitor those
bad habits. Personality and Social Psychology Bulletin, 36, 499-511.
doi:10.1177/0146167209360665
Ram, S., Wang, Y., Currim, F., & Currim, S. (2015). Using big data for predicting freshmen
retention. In 2015 International Conference on Information Systems: Exploring the

Information Frontier, ICIS 2015. Association for Information Systems.
Renfree, I., Harrison, D., Marshall, P., Stawarz, K., & Cox, A. (2016, May). 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 (pp. 2932-
2939). ACM. http://dx.doi.org/10.1145/2851581.2892495
Rothman, A. J., Gollwitzer, P. M., Grant, A. M., Neal, D. T., Sheeran, P., & Wood, W. (2015).
Hale and hearty policies: How psychological science can create and maintain healthy
habits. Perspectives on Psychological Science, 10, 701-705.
Rozin, P., Scott, S., Dingley, M., Urbanek, J. K., Jiang, H., & Kaltenbach, M. (2011). Nudge to
nobesity I: Minor changes in accessibility decrease food intake. Judgment and Decision
Making, 6, 323–332.
Sheeran, P., Godin, G., Conner, M., & Germain, M. (2017). Paradoxical Effects of Experience:
Past Behavior Both Strengthens and Weakens the Intention-Behavior Relationship.
Journal of the Association for Consumer Research
Sobal, J., & Wansink, B. (2007). Kitchenscapes, tablescapes, platescapes, and foodscapes:
Influences of microscale built environments on food intake. Environment and Behavior,
39(1), 124-142.
Stables, G. J., Subar, A. F., Patterson, B. H., Dodd, K., Heimendinger, J., Van Duyn, M. A. S., &
Nebeling, L. (2002). 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.
Journal of the American Dietetic Association, 102, 809-817.
http://dx.doi.org/10.1016/S0002-8223(02)90181-1
Stawarz, K., Cox, A. L., & Blandford, A. (2015, April). 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 (pp. 2653-2662).
ACM. http://dx.doi.org/10.1145/2702123.2702230
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self‐control predicts good
adjustment, less pathology, better grades, and interpersonal success. Journal of
Personality, 72, 271-324. http://dx.doi.org/10.1111/j.0022-3506.2004.00263.x
Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. Journal of Political
Economy, 89, 392–406.
Thaler, R. H., Sunstein, C. R., & Balz, J. P. (2012). Choice architecture. In E. Shafir (Ed.), The
behavioral foundations of public policy (pp. 428–439). Princeton, NJ: Princeton
University Press. http://dx.doi.org/10.2139/ssrn.2536504
Thøgersen J. 2012. The importance of timing for breaking commuters’ car driving habits.
Collegium 12:130–40
Turton, R., Bruidegom, K., Cardi, V., Hirsch, C. R., & Treasure, J. (2016). Novel methods to
help develop healthier eating habits for eating and weight disorders: A systematic review
and meta-analysis. Neuroscience and Biobehavioral Reviews, 61, 132–155.
http://dx.doi.org/10.1016/j .neubiorev.2015.12.008
*Verplanken, B., & Roy, D. (2016). Empowering interventions to promote sustainable lifestyles:
Testing the habit discontinuity hypothesis in a field experiment. Journal of
Environmental Psychology, 45, 127-134.
This paper describes a field experiment which tested the habit discontinuity 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.
Verplanken, B., Walker, I., Davis, A., & Jurasek, M. (2008). Context change and travel mode
choice: Combining the habit discontinuity and self-activation hypotheses. Journal of
Environmental Psychology, 28, 121-127. doi:10.1016/j.jenvp.2007.10.005
Wansink, B., Hanks, A. S., & Kaipainen, K. (2016). Slim by design: Kitchen counter correlates
of obesity. Health Education & Behavior, 43, 552-558.
https://doi.org/10.1177/1090198115610571
Walker, I., Thomas, G. O., & Verplanken, B. (2015). Old habits die hard: Travel habit
formation and decay during an office relocation. Environment and Behavior, 47, 1089
1106.
Webb, T. L., Sheeran, P., & Luszczynska, A. (2009). Planning to break unwanted habits: Habit
strength moderates implementation intention effects on behaviour change. British
Journal
of Social Psychology, 48(3), 507-523.
Weingarten, E., Chen, Q., McAdams, M., Yi, J., Hepler, J., & Albarracín, D. (2016). From
primed concepts to action: A meta- analysis of the behavioral effects of incidentally
presented words. Psychological Bulletin, 142, 472-497. doi:10.1037/bul0000030
Wilson, T. D. (2011). Redirect: The surprising new science of psychological change. Penguin
UK.
**Wood, W., & Rünger, D. (2016). Psychology of habit. Psychology, 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.
Wood, W. (2017). Habit in personality and social psychology. Personality and Social
Psychology Review, 1-15. https://doi.org/10.1177/1088868317720362
Wood, W., & Neal, D. T. (2016). Healthy through habit: Interventions for initiating and
maintaining health behavior change. Behavioral Science & Policy, 2, 71-83.