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How to Form Good Habits? A Longitudinal Field Study on the Role of Self-Control in Habit Formation


Abstract and Figures

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.
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fpsyg-11-00560 March 25, 2020 Time: 16:49 # 1
published: 27 March 2020
doi: 10.3389/fpsyg.2020.00560
Edited by:
Annette Horstmann,
University of Helsinki, Finland
Reviewed by:
Phillippa Lally,
University College London,
United Kingdom
Benjamin Gardner,
King’s College London,
United Kingdom
Anouk van der Weiden;
Specialty section:
This article was submitted to
Eating Behavior,
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.
doi: 10.3389/fpsyg.2020.00560
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
Goal setting
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.
App instructions
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.
App Measurements
Habit strength
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
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
Level Analysis
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)
Physical acvity
Eang fruits
Drinking water
Eang vegetables
Making contact with others
Saving money
Being paent with others
Paying aenon 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
Level Analysis
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***
ICC 0.80
Explained variance 78% 79%
*p<0.05; ***p<0.001.
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
formation process.
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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van der Weiden et al. How to Form Good Habits
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.
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:
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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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Frontiers in Psychology | 8March 2020 | Volume 11 | Article 560
... Routine learning and habit formation play an important role in behavior change [59], in particular in health behavior [58], health-related quality of life [66] and in developing and maintaining resilience [153] and stress tolerance [150]. However, habit formation may take quite a long time of systematic repetition of the desired behavior [79,142]; rapid effects can therefore not be expected. But it seems more than worthwhile to guide and help children and adolescents to develop, learn, optimize and maintain routines and habits that are relevant for healthy mental aging and thus also for personal quality of life (an individual's perception of their position in life in the respective cultural context and in relation to their goals, expectations, standards and concerns [57], and life satisfaction (an individual's self-evaluation of their life quality using their personal criteria [5,78]). ...
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Cognitive decline as part of mental ageing is typically assessed with standardized tests; below-average performance in such tests is used as an indicator for pathological cognitive aging. In addition, morphological and functional changes in the brain are used as parameters for age-related pathological decline in cognitive abilities. However, there is no simple link between the trajectories of changes in cognition and morphological or functional changes in the brain. Furthermore, below-average test performance does not necessarily mean a significant impairment in everyday activities. It therefore appears crucial to record individual everyday tasks and their cognitive (and other) requirements in functional terms. This would also allow reliable assessment of the ecological validity of existing and insufficient cognitive skills. Understanding and dealing with the phenomena and consequences of mental aging does of course not only depend on cognition. Motivation and emotions as well personal meaning of life and life satisfaction play an equally important role. This means, however, that cognition represents only one, albeit important, aspect of mental aging. Furthermore, creating and development of proper assessment tools for functional cognition is important. In this contribution we would like to discuss some aspects that we consider relevant for a holistic view of the aging mind and promote a strengthening of a multidisciplinary approach with close cooperation between all basic and applied sciences involved in aging research, a quick translation of the research results into practice, and a close cooperation between all disciplines and professions who advise and support older people.
... This study explored PA levels among working women in Singapore and their influencing factors two years into the COVID-19 pandemic, a time that might have been sufficient to shape new behaviors and habits 37 . Worldwide, PA levels have generally declined since COVID-19 due to social restrictions and safe distancing measures 38 . ...
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Physical activity (PA) levels may have changed since the COVID-19 pandemic. However, these changes are not well understood. The study aimed to describe the PA level and examine the predictive factors of a health-enhancing PA level among working women in Singapore two years into the COVID-19 pandemic. We undertook a cross-sectional descriptive correlational study. Three hundred participants were recruited and completed the online questionnaire between October and November 2021. In the PA analysis of 217 participants, only 32.7% of the participants achieved a health-enhancing PA level, while 44.7% of the total sample sat for 7 h or more daily. In the univariate analysis, occupation, nationality, monthly income, and average daily sitting hours were significantly associated with a high PA level. The current mode of work, living arrangement, and health-promoting lifestyle profile II_physical activity score remained significant in both univariate and multivariate analyses. Participants who worked from home and stayed with their families were less likely to achieve a health-enhancing PA level than those who had a regular workplace and did not stay with their families. Working women with a health-promoting physically active lifestyle were likelier to achieve a health-enhancing PA level. The long daily sitting time and suboptimal health-enhancing PA participation underscore the need for health promotion initiatives for working women.
... The required sample size for the primary outcome (automaticity over time) was estimated based on recently published recommendations [78]. The power estimation revealed that a level-2-sample size with at least N = 228 participants (i.e., number of required individuals, accounting for 30% sample attrition) and a level-1-sample size of at least n = 13 measurement points (i.e., number of repeated measurements) is required to detect medium-sized population effects of f = 0.40 [21,79] with sufficient power (≥ 0.80). ...
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Background: The adoption of a healthy lifestyle plays a crucial role for the health and well-being of health care professionals. Previous e- and mHealth interventions relied on deliberative psychological processes (e.g., intention, planning) to target lifestyle changes, while revealing mixed efficacy. The additional potential of non-deliberative, automatic processes (i.e., habits) for behavior change has been understudied in interventions so far. The Habit Coach mHealth intervention combines deliberative and non-deliberative processes to support health care professionals in forming healthy physical activity, nutrition and mindfulness habits in daily life. The aim of this paper is to outline the study protocol including a detailed description of the mHealth intervention, evaluation plan, and study design. The purpose of this trial is to understand healthy habit formation in health care professionals over time. Methods: A one-arm, multicenter mHealth intervention study will be conducted. Behavioral and psychosocial predictors will be collected via within-app questionnaires across a 100-day period at baseline, post, as well as at weekly assessments. To understand habit formation across time, linear mixed models will be used. Discussion: This trial aims to unravel the role of motivational and volitional determinants for healthy habit formation across multiple health behaviors in health care professionals embedded in a mHealth intervention. Trial registration: This trial is registered in the German Clinical Trials Register, DRKS-ID DRKS00027156. Date of registration 17 November 2021.
Gastronomy tourism is a critical driver of regional economic development and socio-cultural renaissance. It has been advocated as part of the tourism recovery initiatives in regards to boosting the worldwide economy in the wake of the COVID-19 pandemic. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 397 empirical articles from leading hospitality and tourism journals were systematically examined. This review brought attention to significant developments by using a combination of descriptive analysis, conceptual framework, and topic modeling. Several salient literature gaps and future research directions were amalgamated by utilizing the theory, context, characteristics, and methodology (TCCM) framework.
Purpose The purpose of this paper is to build upon reward-learning theory and examine the role of indulgent food consumption and habitual eating behaviors as a means of emotional coping. Design/methodology/approach Both qualitative and quantitative methods were enlisted to explore emotional eating and indulgent tendencies. In Phase 1 of this research, participants responded to open-ended questions regarding the drivers of emotional eating. In Phase 2, a theoretically driven model was developed from Phase 1 findings and quantitative data was collected to test it. Findings Phase 1 findings indicate that negative terms such as “stressed” and “distract” were more prevalent in the high emotional coping group as opposed to the low emotional coping group. Building from Phase 1, findings from Phase 2 demonstrate a link between emotional eating and indulgent food consumption, underscoring the impact of habitual behaviors. Specifically, emotional coping frequency fully explains the relationship between emotional eating habits and indulgent eating frequency, while intentions to eat indulgent foods partially mediates the relationship between attitude toward indulgent foods and indulgent food consumption frequency. In addition, intentions to eat indulgent foods partially mediates the relationship between emotional coping frequency and indulgent food consumption frequency. Practical implications Social marketing efforts can be enlisted to de-market fatty foods to individuals prone to engaging in emotional eating. Individuals might also be encouraged to use emotion regulation techniques to help manage negative emotions. Originality/value This research contributes to the existing marketing and consumer well-being literature by exploring the role of habit formation in the development of emotional eating and indulgent food consumption.
Physical activity applications (PA apps) offer low-cost, time-space-independent interventions that make it possible to promote public health. To increase users’ stickiness, the commercially available PA apps usually provide various services to adapt to different app usage patterns of users, thus helping them develop the habit of using apps. However, evidence was rare about whether different usage patterns are associated with the maintenance of PA app use. In this study, we introduce dual process theories and quantify users’ app usage patterns in two dimensions: time and space. We analyse the impact of fixed usage patterns on app engagement, by collecting usage data from several commercial-available PA apps in China, which includes 9,175 users. Results show that repeatedly using the PA app at the same time and space could reduce the decline of future app engagement. Moreover, a high degree of self-regulation capacity can mitigate the negative effect of non-fixed usage patterns. This study extends the understanding of health behaviour intervention from the behaviour change level to the behaviour maintenance level. In addition, it provides practical insights for PA app developers in terms of designing behaviour change technologies and for policymakers in terms of facilitating the public’s daily physical activities.
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The low character of hard work by some eighth grade students at Junior high school 10 Banjarmasin affects students by studying at school, many students do not collect the assignments given by the teacher on the specified collection time limit, do not look for alternative solutions to problems when they are faced with obstacles in completing tasks, for example by cheating. This study aims to analyze the effectiveness of the KIPAS model of counseling services using Self Management techniques in improving the hard work character of eighth grade students of Junior high school 10 Banjarmasin. The researcher conducted a quantitative study using the experimental method, namely the quasi-experimental design in the form of the non-equivalent pretest-posttest group design. This research was conducted at Junior high school Banjarmasin1. The researcher used a questionnaire with inclusion criteria and purposive sampling technique with a total of 6 students as respondents. The results of this study show that there are differences in the level of hard work characters before and after the provision of counseling services using the KIPAS model using the Self Management technique. Therefore, the KIPAS model of counseling service using the Self Management technique is effective in improving the hard working character of class VIII students at Junior high school 10 Banjarmasin.
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Background Habits are obtained as a consequence of cue-contingent behavioural repetition. Context cues stimulate strong habits without an individual contemplating that action has been initiated. Because of its health-enhancing effects, making physical activity a part of one’s life is essential. This study examined the associations of physical activity (PA) behaviours with PA habits and the role of autonomous motivation in developing PA habits. Methods This study used a cross-sectional design. A structured questionnaire was implemented through emails to 226 university students, where PA levels, habits and autonomous motivation were self-reported. Results Binary logistic regression identified age groups, gender and participants who were trying to lose weight as the significant predictors in meeting physical activity guidelines. Path analysis showed that moderate-intensity physical activity (β = 0.045, CI = 0.069–0.248) and strength training exercises (β = 0.133, CI = 0.148–0.674) were significantly associated with PA habits (p < 0.01). Autonomous motivation was directly associated with PA habits (β = 0.062, CI = [0.295–0.541], p < 0.01) and was also significantly related to moderate-intensity physical activity (β = 0.243, CI = [0.078–0.266], p < 0.01) and strength training exercises (β = 0.202, CI = [0.033–0.594], p < 0.05). Conclusions The emphasis on experiment-based logic and interest in habit formation in the research community is extensive. As the college years offer an excellent opportunity to establish healthy behavioural interventions, encouraging students in regular PA and exhibiting an autonomous motivation towards PA may be necessary.
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In this paper, I provide an overview of the Christian moral wisdom with respect to virtue formation and character cultivation. I focus in particular on some warnings issued by the great teachers on these topics with respect to the motivations one ought to have in the Christian life. I then discuss some findings of contemporary psychology on habit formation which seem to be at odds with the warnings in Christian moral wisdom. I argue that while there is surface discord between the contemporary psychology of habit formation and Christian moral wisdom, there is in fact a deep concord between them.
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Objective: Given the impact of individuals' habits on health, it is important to study how behaviors can become habitual. Cortisol has been well documented to have a role in habit formation. This study aimed to elucidate the influence of the circadian rhythm of cortisol on habit formation in a real-life setting. Method: Forty-eight students were followed for 90 days during which they attempted to adopt a health behavior (psoas iliac stretch). They were randomly assigned to perform the stretch either upon waking in the morning, when cortisol concentrations are high, or before evening bedtime, when cortisol levels approach the nadir. A smartphone application was used to assess the Self-Report Behavioural Automaticity Index every day and to provide reminders for salivary measurements every 30 days. The speed of the health habit formation process was calculated by modeling the learning curves. Results: Extrapolation of the curves indicated that the morning group achieved automaticity at an earlier time point (105.95 days) than did the evening group (154.01 days). In addition, the cortisol level during the performance of the health behavior was identified as a significant mediator of the time point when the health behavior became habitual. Conclusion: The present findings suggest that the time course of the development of healthy habits depends on the time of the day and that the effect is mediated through diurnal variation in cortisol levels. Future studies are now needed to determine to what extent cortisol rhythmicity can help individuals to adopt new health behaviors. (PsycINFO Database Record
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A recent study suggests that habits play a mediating role in the association between trait self-control and eating behavior, supporting a notion of effortless processes in trait self-control (Adriaanse et al., 2014). We conceptually replicated this research in the area of exercise behavior, hypothesizing that these associations would generalize to other self-control related behaviors. Sufficient exercise is essential for several health and well-being outcomes, and therefore many people intend to exercise. However, the majority of the population does not actually exercise to a sufficient or intended extent, due to competing temptations and short-term goals. This conflict makes exercise a typical example of a self-control dilemma. A within-subjects survey study was conducted to test associations between trait self-control, habit strength, and exercise behavior. Participants were recruited at a local gym. Results demonstrated that trait self-control predicted exercise behavior. Mediation analysis revealed that the association between self-control and exercise was mediated by stronger exercise habits, replicating findings by Adriaanse et al. (2014). These results highlight the relevance of self-control in the domain of exercise. In addition, they add to a growing body of knowledge on the underlying mechanisms of trait self-control on behavior that point to habit—rather than effortful impulse inhibition—as a potential key to self-control success.
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Control theory and other frameworks for understanding self-regulation suggest that monitoring goal progress is a crucial process that intervenes between setting and attaining a goal, and helps to ensure that goals are translated into action. However, the impact of progress monitoring interventions on rates of behavioral performance and goal attainment has yet to be quantified. A systematic literature search identified 138 studies (N �= 19,951) that randomly allocated participants to an intervention designed to promote monitoring of goal progress versus a control condition. All studies reported the effects of the treatment on (a) the frequency of progress monitoring and (b) subsequent goal attainment. A random effects model revealed that, on average, interventions were successful at increasing the frequency of monitoring goal progress (d� �= 1.98, 95% CI [1.71, 2.24]) and promoted goal attainment (d� �= 0.40, 95% CI [0.32, 0.48]). Furthermore, changes in the frequency of progress monitoring mediated the effect of the interventions on goal attainment. Moderation tests revealed that progress monitoring had larger effects on goal attainment when the outcomes were reported or made public, and when the information was physically recorded. Taken together, the findings suggest that monitoring goal progress is an effective self-regulation strategy, and that interventions that increase the frequency of progress monitoring are likely to promote behavior change.
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Reasoned action approaches have primarily been applied to understand exercise behaviour for the past three decades, yet emerging findings in unconscious and Dual Process research show that behavior may also be predicted by automatic processes such as habit. The purpose of this study was to: (1) investigate the behavioral requirements for exercise habit formation, (2) how Dual Process approach predicts behaviour, and (3) what predicts habit by testing a model (Lally and Gardner in Health Psychol Rev 7:S137-S158, 2013). Participants (n = 111) were new gym members who completed surveys across 12 weeks. It was found that exercising for at least four bouts per week for 6 weeks was the minimum requirement to establish an exercise habit. Dual Process analysis using Linear Mixed Models (LMM) revealed habit and intention to be parallel predictors of exercise behavior in the trajectory analysis. Finally, the habit antecedent model in LLM showed that consistency (β = .21), low behavioral complexity (β = .19), environment (β = .17) and affective judgments (β = .13) all significantly (p < .05) predicted changes in habit formation over time. Trainers should keep exercises fun and simple for new clients and focus on consistency which could lead to habit formation in nearly 6 weeks.
Habitual actions are elicited automatically in associated settings, bypassing conscious motivation. This has prompted interest in habit formation as a mechanism for sustaining behaviour change when conscious motivation erodes. Promoting habit depends on understanding how habit develops. This chapter reviews theory and evidence around the habit formation process. First, we describe the few, recent studies that have explicitly sought to study habit development for meaningful activities in humans. Next, we outline a framework for understanding the habit formation process, and narratively review evidence regarding the factors that may directly facilitate or impede habit development, generating hypotheses for future studies. We offer practical suggestions for optimal modelling of habit formation and its determinants.
Self-control is of invaluable importance for well-being. While previous research has focused on self-control failure, we introduce a new perspective on self-control, including the notion of effortless self-control, and a focus on self-control success rather than failure. We propose that effortless strategies of dealing with response conflict (i.e., competing behavioral tendencies) are what distinguishes successful self-controllers from less successful ones. While people with high trait self-control may recognize the potential for response conflict in self-control dilemmas, they do not seem to subjectively experience this conflict as much as people with low self-control. Two strategies may underlie this difference: avoidance of response conflict through adaptive, habitual behaviors, and the efficient downregulating of response conflict. These strategies as well as the role of response conflict are elaborated upon and discussed in the light of existing literature on self-control.
Why does self-control predict such a wide array of positive life outcomes? Conventional wisdom holds that self-control is used to effortfully inhibit maladaptive impulses, yet this view conflicts with emerging evidence that self-control is associated with less inhibition in daily life. We propose that one of the reasons individuals with better self-control use less effortful inhibition, yet make better progress on their goals is that they rely on beneficial habits. Across 6 studies (total N = 2,274), we found support for this hypothesis. In Study 1, habits for eating healthy snacks, exercising, and getting consistent sleep mediated the effect of self-control on both increased automaticity and lower reported effortful inhibition in enacting those behaviors. In Studies 2 and 3, study habits mediated the effect of self-control on reduced motivational interference during a work-leisure conflict and on greater ability to study even under difficult circumstances. In Study 4, homework habits mediated the effect of self-control on classroom engagement and homework completion. Study 5 was a prospective longitudinal study of teenage youth who participated in a 5-day meditation retreat. Better self-control before the retreat predicted stronger meditation habits 3 months after the retreat, and habits mediated the effect of self-control on successfully accomplishing meditation practice goals. Finally, in Study 6, study habits mediated the effect of self-control on homework completion and 2 objectively measured long-term academic outcomes: grade point average and first-year college persistence. Collectively, these results suggest that beneficial habits-perhaps more so than effortful inhibition-are an important factor linking self-control with positive life outcomes. (PsycINFO Database Record (c) 2015 APA, all rights reserved).