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BRIEF RESEARCH REPORT
published: 27 March 2020
University of Helsinki, Finland
University College London,
King’s College London,
Anouk van der Weiden
This article was submitted to
a section of the journal
Frontiers in Psychology
Received: 30 September 2019
Accepted: 09 March 2020
Published: 27 March 2020
van der Weiden A, Benjamins J,
Gillebaart M, Ybema JF and
de Ridder D (2020) How to Form
Good Habits? A Longitudinal Field
Study on the Role of Self-Control
in Habit Formation.
Front. Psychol. 11:560.
How to Form Good Habits? A
Longitudinal Field Study on the Role
of Self-Control in Habit Formation
Anouk van der Weiden1,2, Jeroen Benjamins1,3, Marleen Gillebaart1, Jan Fekke Ybema1
and Denise de Ridder1
1Department of Social Health and Organizational Psychology, Utrecht University, Utrecht, Netherlands, 2Department
of Social Economic and Organizational Psychology, Leiden University, Leiden, Netherlands, 3Department of Experimental
Psychology, Utrecht University, Utrecht, Netherlands
When striving for long-term goals (e.g., healthy eating, saving money, reducing energy
consumption, or maintaining interpersonal relationships), people often get in conﬂict with
their short-term goals (e.g., enjoying tempting snacks, purchasing must-haves, getting
warm, or watching YouTube video’s). Previous research suggests that people who are
successful in controlling their behavior in line with their long-term goals rely on effortless
strategies, such as good habits. In the present study, we aimed to track how self-control
capacity affects the development of good habits in real life over a period of 90 days.
Results indicated that habit formation increased substantially over the course of three
months, especially for participants who consistently performed the desired behavior
during this time. Contrary to our expectations, however, self-control capacity did not
seem to affect the habit formation process. Directions for future research on self-control
and other potential moderators in the formation of good habits are discussed.
Keywords: habit formation, behavior performance, trait self-control, longitudinal app study, community sample
Sometimes people ﬁnd themselves mindlessly watching TV while they had the intention to be
more physically active; eating sweets while they wanted to eat more healthily; or lashing out at
others while they wanted to be more patient or open-minded. Sounds familiar? Although people
may often be able to control themselves in order to attain long-term goals such as healthy living
or maintaining satisfactory relationships, there are also many instances in which they are unable
or unwilling to exert self-control (e.g., when temptations are strong or when tired; e.g., Muraven
and Slessareva, 2003;Baumeister et al., 2007;Hofmann et al., 2010). Also, some people are less
successful in controlling their behaviors than others (Schmeichel and Zell, 2007). In these cases,
people often revert to eﬀortless, habitual behavior (Ouellette and Wood, 1998;Webb and Sheeran,
2006;Neal et al., 2013) – often bad habits. This reliance on habits may, however, also be used to
peoples’ advantage if they manage to form good habits that are in line with their long-term goals.
Indeed, recent research suggests that people who are successful in controlling their behavior, more
eﬀortlessly rely on good habits (Adriaanse et al., 2014;Gillebaart and de Ridder, 2015). But how are
good habits formed?
Research on habit formation has shown that behavior is likely to become habitual when
it is frequently and consistently performed in the same context (e.g., Ouellette and Wood,
1998). For example, when one frequently and consistently eats vegetables for lunch, at some
point eating vegetables for lunch will become a habit. This is because the frequent co-
occurrence of context and behavior instigates an association that may guide future behavior
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van der Weiden et al. How to Form Good Habits
(e.g., Aarts and Dijksterhuis, 2000;Neal et al., 2012). Speciﬁcally,
when encountering a context (e.g., having lunch) that is
associated with a certain behavior (e.g., eating vegetables), this
context will automatically trigger this associated behavior. Hence,
once a good habit is formed, it is rather eﬀortless to perform
desired behavior. However, the process of habit formation itself
may vary in the amount of eﬀort needed – although some people
manage to form certain habits as quickly as 18 days, others need
as much as half a year (Lally et al., 2010). This raises the question
how exactly do habits form over time?
Although research on habit formation is still in its infancy,
recent studies have uncovered some of the mechanisms that
underlie the habit formation process. One of the main ﬁndings
is that the habit formation process within individuals unfolds
asymptotically (Lally et al., 2010;Fournier et al., 2017). That
is, habit strength increases steeply at ﬁrst, and then levels oﬀ.
In addition, studies that studied habit formation on the group
level (i.e., averaging over participants) have provided insight
into the processes that facilitate such increases in habit strength.
Speciﬁcally, the frequency and consistency with which the
desired behavior is performed, the inherently rewarding nature
of the behavior, a comfortable environment (e.g., no threats or
obstacles), and easy rather than diﬃcult behaviors have been
shown to facilitate the process of habit formation (Kaushal and
Rhodes, 2015;Fournier et al., 2017).
Besides these factors, there are still many others unexplored
that may explain the variation in the time it takes people to
form a habit. One such likely candidate is self-control capacity.
That is, habit formation crucially depends on the repeated
performance of behavior that is in line with one’s long-term goal.
The initiation of such new behavior, as well as the inhibition of
acting upon short-term temptations is likely to require eﬀortful
self-control, especially in the early stages of habit formation.
Indeed a study among teenagers indicates that those who
initially had higher self-control capacity reported having stronger
meditation habits after three months of meditation sessions
(Galla and Duckworth, 2015, Study 5). Other studies revealed
that habit strength mediates the eﬀect of self-control strength
and behavior. Speciﬁcally, self-control was related to increased
habit strength, which was in turn related to increased exercise
behavior (Gillebaart and Adriaanse, 2017) and decreased snack
intake (Adriaanse et al., 2014). However, although these studies
have indicated that self-control is related to habit strength, they
do not provide insight in the role of self-control capacity in the
initial stages of habit formation.
The current study was a ﬁrst attempt to track how self-
control capacity aﬀects the development of good habits in daily
life over a relatively long period of time. We expected both
repeated goal-congruent behavior performance and self-control
capacity to facilitate the formation of good habits. Possibly,
self-control capacity may aﬀect habit formation via increased
behavior performance (as the initiation of new behavior and
inhibition of conﬂicting behavior requires self-control at ﬁrst).
To test these hypotheses, we recruited people who wanted to
form a good habit in the domain of health behavior (eating
fruit or vegetables, exercising, or drinking water), interpersonal
relationships (making more contact with others, being more
patient or open-minded, or having more attention for others),
personal ﬁnance (saving money), or environmental-friendly
behavior (recycling). Over the course of three months, we
then measured their goal-congruent behavior performance, self-
control capacity, and habit strength to examine how self-control
related to behavior performance and habit strength over time.
Participants and Design
A community sample was recruited via the population register
of the city of Utrecht in the Netherlands as well as social media
and the alumni register of Utrecht University. Anyone between
the age of 18 and 65 who possessed a smartphone was eligible
(we could provide a limited number of participants with a
smartphone for the duration of the study if they did not possess
one, N= 5). All participants indicated they wanted to form a habit
in the health, sustainability, interpersonal, or ﬁnancial domain.
The within-subjects design consisted of a pre-measurement
administered in groups of 2–13 participants at a university
location,1followed by a three-month interval of daily measures
administered through an in-house developed mobile application,
and after 90–110 days, a post-measurement (again in group
sessions at a university location). In total, 180 people participated
in the pre-measurement, of whom 90 participated in the post-
measurement. Participants took part in the daily measures over
a range of 17–110 days (M= 77.0, SD = 26.7). During this
time period, the number of bi-weekly self-control assessments
ranged from 1 to 10 (M= 6.5, SD = 2.3), which were alternated
with bi-weekly habit strength assessments, of which the number
ranged from 2 to 9 (M= 5.7, SD = 2.0). In total, 146 participants
(118 women; Mage = 31.9; SDage = 12.7; range 18–61 years) who
completed at least one follow-up assessment of habit strength
were included in the analyses. More than half of them (65.8%)
were community residents (including alumni) and the remainder
(34.2%) were bachelor students. Based on participants’ postal
code (which is indicative of education, income, and work status;
Netherlands Institute for Social Research), we inferred their
socio-economic status. About 10.3% of the participants lived
in underprivileged neighborhoods, 61.0% lived in middle class
neighborhoods, and 26.0% came from privileged neighborhoods
(postal code data was missing for 4 participants). Participants’
initial level of habit strength was moderate (M= 3.1, SD = 1.1).
Procedure and Materials
Those who were interested in participating received an
information letter via e-mail, containing a link to register for the
study with a unique participation code. In the registration form,
participants were reminded of the terms and conditions (i.e.,
1The group administration served to allow more participants to start around the
same time, i.e., to minimize seasonal inﬂuences (e.g., new year’s resolutions). We
minimized the degree to which participants inﬂuenced each other by stressing the
importance of independent answers and reactions, as well as the importance of
being silent during the measurements. Also, one or two researchers were always
present to monitor participants and answer questions.
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voluntary nature of participation, ability to withdraw without
explanation, etc.), after which they were required to give their
consent for participating in the study. Participants could then
schedule an appointment for the pre-measurement.
Participants came to the university for a pre-measurement as part
of a larger longitudinal prospective study on trait self-control (i.e.,
to see whether self-control could be trained by daily performance
of a behavior that requires self-control – which indeed seemed
to be the case; de Ridder et al., 2019). As such, the diﬀerent
measurements (pre-, app-, and post-) also included measures that
were not of interest for the current study.2
At the start of the study, participants selected a speciﬁc
behavior they wanted to turn into a habit over the course
of the study. Choices covered health, interpersonal, ﬁnancial,
and ecological behaviors (e.g., eating fruit, being patient, saving
money, recycling). Depending on the type of behavior chosen,
participants could then choose from three to seven contexts
for behavioral practice (e.g., eating fruit when having breakfast,
being patient when talking to someone,3saving money when
in the supermarket, or recycling when tidying up). As such,
participants could choose which habit they wanted to form
based on 60 preset combinations of behaviors and contexts. See
Figure 1 for an overview of which behaviors were selected by
the participants. It was emphasized that the selected behavior
needed to be personally relevant to them, had to be a behavior
they did not regularly perform yet, and had to be feasible for
them to perform on a daily basis. After selecting a behavior
and context, participants had to specify for themselves what this
behavior entailed (e.g., when they chose exercise as their goal,
it was explained that a ten minute routine at home was more
feasible on a daily basis than an hour at the gym). As such,
participants were intrinsically motivated and there was room for
forming a new habit.
For the purpose of this study, we developed a mobile app
(which ran on iOS and android) to assess self-control capacity
and habit strength on a regular basis. At the end of the pre-
measurement, participants were instructed to install and use this
2In the pre-measurement we measured explicit habit strength (with the Self-Report
Habit Index; Verplanken and Orbell, 2003) and implicit habit strength (by means
of a Lexical Decision Task), implicit state self-control (an adapted version of the
mouse-tracker task; Freeman and Ambady, 2010) and explicit self-control capacity
(Brief Self-Control Scale; Tangney et al., 2004), general attributional style (General
Attributional Style Questionnaire; Peterson et al., 1982), goal importance, and
motivation. In the smartphone app, behavioral performance, context encounter,
and attributions of failure were measured daily, while habit formation, self-
control capacity, general self-eﬃcacy (General Self-Eﬃcacy Scale; Jerusalem and
Schwarzer, 1979), and willpower beliefs (Job et al., 2010) were measured bi-weekly.
Additionally, a mouse tracker task was alternated with a lexical decision task
every other day to measure implicit self-control and implicit habit formation,
respectively. During the post-measurement, participants completed the same
tasks and questionnaires as during the pre-measurement, except that the General
Attributional Style Questionnaire was replaced by an ego-depletion task.
3The option “when talking to someone” could be further speciﬁed into “when
talking to a friend/partner/parent/child/neighbor”.
app for daily tests and questionnaires. Participants were also
informed that they would receive a reminder every morning
via the mobile app.
Habit strength was assessed bi-weekly with the Self-Report
Habit Index (Verplanken and Orbell, 2003), which consists of
12 statements (e.g., ‘[self-chosen behavior (e.g., eating fruit)]
is something I do . . .frequently; . . .automatically; . . .without
thinking)’. For each statement, participants indicated to what
extent they felt the statement applied to them on a scale from 1
(completely disagree) to 7 (completely agree). The scale proved
reliable with a Cronbach’s alpha of.94.4
Goal-congruent behavior performance
On a daily basis, participants indicated (dichotomously) whether
or not they had performed the self-chosen behavior that day,
and whether they performed this behavior in their self-chosen
Self-control capacity was assessed bi-weekly with the Brief Self-
Control Scale (Tangney et al., 2004), which consists of 13
statements (e.g., “I am good at resisting temptation” or “People
would say I have iron self-discipline”). For each statement,
participants indicated to what extent they felt the statement
applied to them on a scale from 1 (not at all) to 5 (very much).
The scale proved reliable with a Cronbach’s alpha of 0.79.
Habit Formation Over Time – Individual
First, following Lally et al. (2010) approach, we attempted to ﬁt
an asymptotic curve to individual participants’ habit strength
scores over time (by means of a Least Squares Curve Fit
algorithm in Matlab), to then see whether we could predict the
individual (rate of) change in habit strength as a function of
goal-congruent behavior performance and self-control capacity.
However, the individual patterns ﬂuctuated too much (possibly
because bi-weekly measurements were too infrequent; M= 5.73,
SD = 1.99, range = 2–9 observations per participant; see
Figure 2 for the number of observations plotted against the
number of participants6), and curve ﬁtting could only be
achieved for 4.11% of our participants (see results under point
2, Supplementary Material). As an alternative, we also tried
4We have also run the analyses with the SRBAI subscale, which led to the same
results (see point 1, Supplementary Material).
5For our main analysis, we looked at whether the behavior was performed or not,
regardless of the context it was performed in. Analyzing whether the behavior was
performed in context or not yielded similar results.
6Participants for whom an asymptotic curve could be ﬁtted did not diﬀer from
participants for whom an asymptotic curve could not be ﬁtted in their number
of data points [M = 6.33, SD = 2.25 vs. M = 6.6, SD = 1.42, respectively;
F(1,116) = 0.19, p= 0.67] or behavioral consistency [M = 0.88, SD = 0.15 vs.
M = 0.77, SD = 0.26, respectively; F(1,144) = 1.02, p= 0.31].
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Chosen Goals (N=146)
Making contact with others
Being paent with others
Paying aenon to others
In clockwise order:
FIGURE 1 | Overview of the number of participants selecting each behavior. Please note that exercise (“sporten” in Dutch) and physical activity (“bewegen” in Dutch)
refers to different types of behaviors. Whereas exercise is typically associated with certain rules and competitiveness, but most of all with high intensity (e.g., playing
football, cross ﬁt, running), physical activity refers to more casual and less intense behaviors (e.g., walking or biking, gardening, household chores).
FIGURE 2 | Number of observations for habit strength (total N= 836) plotted against the number of participants (N= 146).
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ﬁtting a less constrained power curve (y= axb), with even less
success (2.4%). We therefore decided to analyze the data on the
group level instead.
Habit Formation Over Time – Group
We examined the data in SPSS 24 with the Linear Mixed Models,
using Maximum Likelihood estimation. In the ﬁrst analysis, we
carried out a growth curve modeling for habit formation, in
which a random intercept, and ﬁxed eﬀects of a linear and a
quadratic time trend were estimated. In addition, the random
slopes of the linear and quadratic trend were tested to allow
for individual diﬀerences in the growth curve. In a second
analysis we tested whether habit formation was inﬂuenced by
self-control capacity and the performance of the behavior. In
Model 1, the random intercept was included to determine the
intraclass correlation (ICC) of habit strength as an indicator of
the variance at person level. In Model 2, lagged habit strength
(i.e., habit strength at the previous measurement) was entered to
analyze habit formation. Because we controlled for lagged habit
strength, the linear and quadratic trend were not included in
this analysis. In Model 3, self-control capacity at the previous
bi-weekly measurement of self-control and daily practice of the
chosen behavior (measured by the proportion of daily app-
measurements in which the chosen behavior was performed
during the interval between the previous and the current habit
assessment) was entered, as well as a number of control variables,
i.e., the measurement number of bi-weekly habit assessment, the
length of the interval since the previous habit assessments, and
the number of daily behavioral assessments.
Habit Formation Over Time
We ﬁrst examined whether habit strength increased over time.
Figure 3 shows a signiﬁcant increase of about 0.8 SD (a large
eﬀect size according to Cohen, 1992) in habit strength over a
period of 110 days with a stronger increase in beginning of
the study period, leveling oﬀ at the end. Both the linear trend
(t= 15.30, p<0.001) and the quadratic trend (t=−3.39,
p<0.001) were signiﬁcant. Adding the random slopes for the
linear (Wald Z= 5.37, p<0.001) and quadratic (Wald Z= 2.40,
p<0.05) improved the ﬁt of the model, showing that habit
formation diﬀered over participants.
Effects of Goal-Congruent Behavior
Performance and Self-Control Capacity
on Habit Formation
Table 1 shows the results of a hierarchical multilevel analysis
of habit formation. As can be seen in Model 2, habit strength
is rather stable and strongly predicted by lagged habit strength
at the previous measurement of habit. Nevertheless, entering
lagged self-control capacity and goal-congruent behavior
performance in the time period during both habit strength
measurements further increased the ﬁt of the model. Self-control
capacity did not contribute to higher habit strength7. However,
7One might argue that the eﬀect of self-control capacity on habit formation is
via goal-congruent behavior performance. However, our data do not provide
support for a relationship between lagged self-control and goal-congruent behavior
performance (see de Ridder et al., 2019). Also, entering lagged self-control in the
model ﬁrst (in Model 3), and subsequently adding the other variables (in Model 4),
FIGURE 3 | Habit strength ﬁtted as a function time, with 95% conﬁdence bands.
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TABLE 1 | The multilevel regression of habit strength.
Predictors Model 1 Model 2 Model 3
Intercept 4.07*** 4.12*** 4.13***
Lagged habit strength 0.87*** 0.85***
Lagged self-control 0.01
Time of measurement −0.03*
Days between measurements 0.00
Number of app-measurements 0.00
Proportion behavior carried out 0.47***
Fit(−2 log L) 1445.05*** 1,095.91*** 1067.58***
1ﬁt 349.14*** 28.33***
df 1 5
Random intercept (person level) 1.16*** 0.00 0.00
Residual (day level) 0.30*** 0.32*** 0.31***
Explained variance 78% 79%
participants who carried out the self-chosen behavior more
consistently (higher proportion of goal-congruent behavior
performance8), showed stronger increases in habit strength.
In line with the trend in habit formation shown before, the
time of measurement (i.e., the umpteenth time) had a small
negative inﬂuence on habit strength increase. This is in line
with the lower increase in habit strength later on during
the study period.
People often struggle in the pursuit of their long-term goals.
As good habits may help people in this pursuit, we set out
to gain more insight in how good habits are formed in
daily life. We speciﬁcally focused on goal-congruent behavior
performance and self-control capacity as potential facilitators
of habit formation. We were able to test our hypotheses
in a diverse and highly committed sample. Results showed
a large increase in habit strength over the course of three
months, which was strongest for participants who consistently
performed the self-chosen goal-congruent behavior during this
time. Contrary to our expectations and previous ﬁndings
by Galla and Duckworth (2015), however, we did not ﬁnd
support for self-control capacity as a predictor of the habit
One reason why self-control capacity may not have facilitated
habit formation, could be that participants experienced little
conﬂict between their long-term goal and an immediately
does not reveal any relationship between self-control and habit strength (see point
4, Supplementary Material).
8Behavioral consistency diﬀered between the diﬀerent behaviors chosen
[F(9,136) = 3.02, p= 0.003, η2= 0.17], such that people were most consistent in
performing prosocial behaviors, whereas people were least consistent in exercising
and saving money (see point 3, Supplementary Material). Adding the chosen
behavior to the model did not improve the model ﬁt [1χ2(df = 9) = 7.10 ns],
nor did it change any of the reported eﬀects.
gratifying alternative. In contrast to well-controlled lab
experiments where participants are simultaneously confronted
with goal-congruent stimuli (e.g., broccoli) and conﬂicting
temptations (e.g., apple pie), such temptations may not always
be present when the opportunity presents itself to perform goal-
congruent behavior in real life. If so, the reason that participants
did not yet regularly perform the desired behavior before
participating in the study, may not have been because they were
unable to control their behavior in the presence of temptations.
Alternatively, in the absence of temptation, participants may
have had diﬃculty monitoring their behavior and identifying
opportunities for goal pursuit. In the current study monitoring
was facilitated by specifying a speciﬁc context for goal pursuit
and registering their behavior daily via the smartphone
application, which may have facilitated goal-congruent behavior
performance, and hence, habit formation. Indeed, monitoring
has been proven to be very eﬀective in goal progress and
attainment (see Harkin et al., 2016 for meta-analyses; Michie
et al., 2009). Future research could extend the current ﬁndings
by assessing how often people run into temptations during
long-term goal pursuit and whether its impact on the habit
formation process is modulated by self-control capacity. Also,
future research could investigate whether habit formation can
be facilitated even more by frequent monitoring at regular
intervals during the day.
Another reason why self-control may not have aﬀected habit
formation, is because our instructions to participants may
have created an association between the speciﬁc, self-chosen
behavior and a speciﬁc context. Research has shown that if
people form speciﬁc “if. . ., then. . .” plans (also referred to
as implementation intentions), in which a speciﬁc behavior is
linked to a speciﬁc context (e.g., if I open the fridge, then I
will grab the cherry tomatoes), this will automatically trigger
the speciﬁc behavior upon encounter of the speciﬁc context
(Gollwitzer, 1999;Webb and Sheeran, 2007). As such, habit
strength – or rather, behavioral automaticity – should increase
instantly and self-control is no longer required. Although
we did not ask our participants to form implementation
intentions, our request to select a speciﬁc context in which
to perform the speciﬁc self-chosen behavior may have resulted
in cue-behavior associations that facilitate eﬀortless behavior
performance. However, our data as well as the data of Lally
and colleagues (Lally et al., 2010; in which implementation
intentions were actually formed) do not seem to support this
line of reasoning. Even if cue-behavior associations were formed,
they did not result in instant increases in habit strength, as
habit formation unfolded gradually over the course of several
months, leaving room for self-control capacity to inﬂuence
the habit formation process. It would be interesting, though,
to further investigate the role of self-control capacity in the
presence versus absence of cue-behavior associations in an
experimental ﬁeld study.
Yet another reason for not ﬁnding an eﬀect of self-control
on the habit formation process may be that we focused on trait
rather than state self-control. Although trait self-control did
increase over time (see de Ridder et al., 2019; and hence, may
have beneﬁted the habit formation process), trait self-control is
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a relatively stable factor. Future research should assess within-
individual ﬂuctuations of state self-control in the habit formation
process – preferably also ﬁtting habit formation on the individual
level. Our ﬁndings suggest that more data points are required
for such analyses.
In line with previous research (Lally et al., 2010;Fournier
et al., 2017), the current (aggregated) data provided support for
the asymptotic contribution of repeated goal-congruent behavior
performance to the formation of habit. Unfortunately, we were
unable to show this trend on the individual level, due to the bi-
weekly assessment of habit strength. Hence, future studies would
beneﬁt from more frequent assessments. These studies may also
want to test further moderators of habit formation, e.g., what type
of contextual cues may be the best triggers for behavior, the role
of motivation, and how the formation of good habits aﬀect the
bad habits they aim to substitute (see also Gardner and Lally,
Beside the strengths of our study (a diverse and highly
committed sample), it is important to note that the self-report
measurement of habit strength may have been subject to biases.
Although the SRHI is commonly used and well validated
(Verplanken and Orbell, 2003;Gardner et al., 2011), it would
be even more compelling if the current ﬁndings could be
corroborated by more implicit measures of habit strength, such
as a lexical decision task (Meyer et al., 1972). In the current
study, we have attempted to measure habit strength by means of a
lexical decision task in the mobile app. However, the mobile app
measurements were not sensitive enough to detect any eﬀects (see
point 5, Supplementary Material). Future research may instead
opt for online computer measurements.
To conclude, our study was the ﬁrst to track the role of self-
control capacity in the habit formation process in a longitudinal
ﬁeld experiment. Although we did not ﬁnd evidence for self-
control as a facilitator of habit formation, the current ﬁndings do
oﬀer new directions for future research on self-control and other
potential moderators in the formation of good habits.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
The studies involving human participants were reviewed and
approved by The Faculty Ethics Review Board – Faculty of
Social and Behavioral Sciences at Utrecht University. The
patients/participants provided their written informed consent to
participate in this study.
AW, JB, MG, and DR developed the theory and study design.
AW carried out the experiment and data preparations, and took
the lead in writing of the manuscript. AW and JB performed the
individual-level analyses. JY performed the group-level analyses.
All authors provided critical feedback and helped to shape the
analyses and manuscript.
We would like to thank Django den Boer and Roy van Koten for
developing the Habit Tracker app, and Demi Blom for recruiting
participants and keeping them involved.
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
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Conﬂict of Interest: The authors declare that the research was conducted in the
absence of any commercial or ﬁnancial relationships that could be construed as a
potential conﬂict of interest.
Copyright © 2020 van der Weiden, Benjamins, Gillebaart, Ybema and de Ridder.
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