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Effects of Circadian Cortisol on the Development of a Health Habit

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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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Health Psychology
Effects of Circadian Cortisol on the Development of a
Health Habit
Marion Fournier, Fabienne d’Arripe-Longueville, Carole Rovere, Christopher S. Easthope, Lars
Schwabe, Jonathan El Methni, and Rémi Radel
Online First Publication, June 26, 2017. http://dx.doi.org/10.1037/hea0000510
CITATION
Fournier, M., d’Arripe-Longueville, F., Rovere, C., Easthope, C. S., Schwabe, L., El Methni, J., &
Radel, R. (2017, June 26). Effects of Circadian Cortisol on the Development of a Health Habit.
Health Psychology. Advance online publication. http://dx.doi.org/10.1037/hea0000510
BRIEF REPORT
Effects of Circadian Cortisol on the Development of a Health Habit
Marion Fournier and Fabienne d’Arripe-Longueville
Université Côte d’Azur
Carole Rovere
Université de Nice Sophia Antipolis
Christopher S. Easthope
Balgrist University Hospital
Lars Schwabe
University of Hamburg
Jonathan El Methni
Université Paris Descartes
Rémi Radel
Université Côte d’Azur
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 Automa-
ticity 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.
Keywords: health, habits, learning, stress, cortisol
Supplemental materials: http://dx.doi.org/10.1037/hea0000510.supp
Attention should be paid to behavioral habits, given their important
role in health (Wood & Neal, 2016). A behavioral habit is a process
by which a stimulus automatically generates an impulse toward ac-
tion, based on learned stimulus–response associations (Gardner,
2015). Recent studies have pointed to the critical role of stress and the
stress hormones (mainly cortisol) in the development of habit behav-
ior. For instance, it has been shown that stress induces a shift in the
control of instrumental behavior from goal-directed toward habitual
responding (e.g., Schwabe, Schächinger, de Kloet, & Oitzl, 2010).
The endogenous level of cortisol varies according to a circadian
rhythm. In humans, cortisol levels are low at midnight and increase
overnight to a peak in the morning. Following this morning peak,
cortisol levels slowly decline throughout the day (Weitzman et al.,
1971). Although the impact of cortisol on habit behavior has been
well established through stress manipulation or cortisol injection (e.g.,
Fournier, d’Arripe-Longueville, & Radel, 2017; Quirarte et al., 2009),
the influence of cortisol’s circadian rhythm on habit formation re-
mains to be elucidated.
Many studies have demonstrated the predictive capacity of
habits on health behavior (e.g., de Bruijn & Rhodes, 2011), but
less is known about the process of habit formation in real-life
settings. A previous study followed participants over 84 days
while they adopted a new, daily health-promoting behavior such
as eating fruit or exercising (Lally, van Jaarsveld, Potts, &
Marion Fournier and Fabienne d’Arripe-Longueville, Faculty of Sport
Science, Université Côte d’Azur; Carole Rovere, Institut de Pharmacologie
Moléculaire et Cellulaire, Université de Nice Sophia Antipolis; Christopher
S. Easthope, Spinal Cord Injury Center, Balgrist University Hospital; Lars
Schwabe, Department of Cognitive Psychology, Institute for Psychology,
University of Hamburg; Jonathan El Methni, Department of Statistics,
Université Paris Descartes; Rémi Radel, Faculty of Sport Science, Univer-
sité Côte d’Azur.
Correspondence concerning this article should be addressed to Marion
Fournier, Faculty of Sport Science, University Côte d’Azur, LAMHESS,
Nice, France. E-mail: fou2marion@gmail.com
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Health Psychology © 2017 American Psychological Association
2017, Vol. 999, No. 999, 000 0278-6133/17/$12.00 http://dx.doi.org/10.1037/hea0000510
1
Wardle, 2010). Although this protocol was innovative in track-
ing the formation of habits by collecting daily reports of expe-
rienced automaticity, the collected data proved challenging to
model. In the present study, a similar protocol was used to
investigate the influence of diurnal variations in cortisol con-
centrations on the dynamics of habit formation using a real-
world application. To better fit the evolution of experienced
automaticity in time, we used logistic curves (Murre, 2014).
The level of circadian cortisol was manipulated by having two
groups of participants perform a new health behavior at differ-
ent times of the day. Given that cortisol levels are higher in the
morning than in the evening, we hypothesized that the new
behavior would become habitual more quickly if executed in
the morning than in the evening. The cortisol level was ex-
pected to mediate this effect.
Method
Participants
On average, diurnal variations of cortisol can lead to a 20%
difference in learning and memory (May, Hasher, & Stoltzfus,
1993; Petros, Beckwith, & Anderson, 1990; Wyatt, Ritz-De
Cecco, Czeisler, & Dijk, 1999), which suggests that at least 46
participants were needed to observe such an effect with a power
of .80. Forty-eight French students (28 male; age 21.7 1.78
years, range 20 –25) participated in exchange for course
credit. Participation was limited to healthy nonsmokers with
normal body mass index (22.4 2.17 kg/m
2
, range 18.2–
27.7 kg/m
2
) not under contraceptive medication according to
the recommendations for salivary cortisol measurements
(Kirschbaum, Kudielka, Gaab, Schommer, & Hellhammer,
1999). The study was approved by the local ethics committee
for the protection of individuals (Université de Nice Sophia-
Antipolis) and conducted in accordance with the Declaration of
Helsinki (1964) ethical guidelines.
Procedure
Participants were invited to a first meeting with the experi-
menter. After providing written informed consent, they were
randomly assigned to a morning or evening group according to
a minimization algorithm, balancing for gender. Here, a chro-
notype questionnaire and a measure of intention to adopt the
behavior was completed. The intervention commenced within a
week of the first meeting. It consisted of performing a new
behavior once daily for 90 days. The behavior was a stretching
exercise that is highly recommended to maintain flexibility and
prevent low back pain (i.e., psoas iliac stretch; see the online
supplemental materials). Depending on group allocation, the
stretch was completed in bed upon waking or before sleeping.
Adherence and a visual analog scale version of the Self-Report
Behavioural Automaticity Index (SRBAI; Gardner, Abraham,
Lally, & de Bruijn, 2012) were recorded via a smartphone
reminder application on a daily basis. A salivary sample was
collected every 30 days. Details on the measures are provided in
the online supplemental materials.
Data Analysis
To evaluate the evolution of automaticity, we fitted a four-
parameter logistic function to participants’ daily responses to the
SRBAI (see the online supplemental materials). Although habit
formation has previously been modeled using a power function
(Lally et al., 2010), this method led to mixed results, providing a
moderate fit (R
2
.70) for only 48% of the participants. Because
a logistic function can outperform a power function in modeling
learning curves (Murre, 2014), this approach was adopted. We
considered that, in line with Lally et al. (2010), automaticity was
achieved at the time point at which 95% of the asymptote was
reached (x
.95
). This value served as the dependent variable.
To determine the group effect and the potential mediation effect
of cortisol on x
.95
, we employed a mediation model using the
PROCESS toolbox (Hayes, 2012). Group (morning vs. evening)
was applied as the independent variable, cortisol concentration as
a mediator, and sex and intention as covariates for the prediction
of the dependent variable. A bias-corrected bootstrapping method
with 2,000 samples was used to evaluate the effects. The total
effect of the independent variable and its indirect effect through
the mediator are presented. To control for the chronotype, we used
a moderation analysis to examine how the effect of group on x
.95
was influenced by the chronotype score. The mediation model was
also used in a further configuration with the independent variable
replaced by a Group Chronotype interaction term. The interac-
tion term adequately represented the hypothesis of a match be-
tween the moment of execution and the participants’ chronotype
score (Hasher, Goldstein, & May, 2005).
Results
Of the 48 recruited participants, 42 completed the experiment:
19 participants in the morning group and 23 in the evening group.
The intention to adopt the behavior was high (4.74 0.39), with
no difference between groups (p.24). The response rate to the
daily questionnaire was high (89.2%), with no difference between
groups (p.25). Adherence was high (94.9%), with no difference
between groups (p.25). The curve-fitting process was highly
successful. Fitting converged for each participant using the logistic
function (by comparison, only 31 participants could be fitted using
a power curve). A high adjustment quality index was obtained
(R
2
.945 .053). Figure 1 presents the mean traces of the
morning and evening groups obtained by averaging each of the
four function parameters for each condition.
Automaticity (x
.95
) was slightly positively skewed but did not
significantly deviate from normality (p.10). The results of the
first mediation model populated with group as the independent
variable are presented in Table 1. After controlling for covariates
(sex and intention), we observed a significant total effect of time
of day until habit development (effect ⫽⫺26.74, SE 10.25,
95% confidence interval [CI: 47.52, 5,96]), indicating that the
behavior became habitual more quickly when it was performed in
the morning (M105.95 46.72 days) as opposed to the evening
(M154.01 71.05 days). Group (morning vs. evening) had a
significant effect on the cortisol level, indicating that cortisol
levels differed between groups (morning: 2.16 .87 ng/ml; eve-
ning: 1.10 .86 ng/ml). When included in the model predicting
the time to form a habit (x
.95
), cortisol was a significant predictor
and the group effect was no longer significant, suggesting a me-
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2FOURNIER ET AL.
diating role. Accordingly, the group effect had no significant direct
effect (effect ⫽⫺14.21, SE 11.21, 95% CI [36.93, 8.50]), but
a significant indirect effect on the dependent variable through the
effect of the cortisol (effect ⫽⫺11.89, SE 6.25, 95% CI
[30.39, 2.98]). Figure 2 illustrates the nature of the relation
between cortisol levels and x
.95
.
The second model, in which the independent variable group was
replaced by the Group Chronotype interaction, led to a similar
pattern (see Table 2). The Group Chronotype interaction had a
significant total effect on x
.95
(effect ⫽⫺.57, SE .22, 95% CI
[1.02, .12]). The total effect was decomposed into a nonsig-
nificant direct effect (effect ⫽⫺.29, SE .25, 95% CI [.79,
2.13]) and a significant indirect effect through cortisol (ef-
fect ⫽⫺.27, SE .15, 95% CI [.65, .04]).
Discussion
Because it has been shown that cortisol level has a strong
connection to habit memory formation and that cortisol concen-
trations vary markedly over the day, this study aimed to test the
influence of the circadian rhythm of cortisol on the development of
a healthy habit. The presented findings indicate that the stretching
behavior that participants intended to adopt became habitual more
quickly when it was performed in the morning than in the evening.
In addition, it seems that cortisol played a mediating role, because
there was a significant indirect effect of the time of day on
automatization speed through the level of cortisol displayed at the
moment of behavior execution. Cortisol concentrations in the
morning were higher than in the evening samples, and the cortisol
level was negatively associated with the time taken to make the
behavior habitual (see Figure 2). To our knowledge, this is the first
time that habit formation has been studied through the prism of
chronobiology. Our findings are consistent with those in other
studies that have manipulated glucocorticoides GC levels with
either pharmacological injection or stress induction, because they
have all indicated that cortisol level has an impact on habit for-
mation (e.g., Quirarte et al., 2009; Schwabe & Wolf, 2009).
Our results show that the indirect effect of time of day on habit
formation through the circulating cortisol level persisted, and was
even slightly stronger, when we took into account the match
between chronotype and the time of behavior execution. This
match effect contributes to the large body of literature on the
effects of chronobiology on behavior (e.g., Hasher et al., 2005)
showing that synchrony between individual preferences and the
time of the day has an important effect on performance and
particularly on tasks relying on attentional demands such as learn-
ing. It is thus not surprising that this individual trait further refines
Figure 1. Fitted logistic function representating the evolution of be-
havioral automaticity (Self-Reported Behavioural Automaticity Index,
SRBAI) in time in the morning and evening conditions. Shaded areas
represent standard error of the mean. Data from after the measurement
period (90 days) is extrapolated and indicated using light gray. The
moment when participants reach 95% of the maximal asymptote (dotted
line, x
.95
) represents the time taken to form the habit. Traces are
normalized for representation purposes.
Table 1
Regression Models Used to Determine the Mediating Role of Cortisol in the Effect of the
Condition on the Time Taken to Form a Behavioral Habit
Model and variable Coefficient SE t p
95% CI
LL UL
Model predicting cortisol: R
2
.28, F(1, 40) 14.89, p.0004
Constant 1.627 .138 11.831 .000 1.349 1.905
Condition .531 .138 3.859 .001 .252 .809
Model predicting x
.95
without inclusion of the mediator: R
2
.20, F(3, 38) 2.34, p.09
Constant 288.495 172.121 1.671 .102 59.950 636.941
Condition 26.601 10.169 2.616 .013 47.187 6.015
Intention 30.528 35.453 .861 .395 102.299 41.244
Sex 25.043 20.269 1.236 .224 66.075 15.990
Model predicting x
.95
with inclusion of the mediator: R
2
.29, F(4, 37) 2.58, p.053
Constant 290.459 150.281 1.933 .061 14.044 594.962
Cortisol 22.413 10.779 2.079 .045 44.254 .572
Condition 14.218 11.213 1.268 .213 36.937 8.502
Intention 23.578 31.366 .752 .457 87.133 39.976
Sex 22.316 19.782 1.128 .267 62.398 17.767
Note.CIconfidence interval; LL lower limit; ULCI upper limit.
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3
CORTISOL AND HEALTHY HABIT DEVELOPMENT
the effect of the moment of execution on the acquisition of behav-
ioral habits.
Our study was designed to test habit formation in real life and
therefore makes an original contribution to the literature by having
greater ecologic validity than do the laboratory tasks (i.e., instru-
mental tasks) that are generally used to study the habit formation
process. The short SRBAI and smartphone reminder application
made it possible for us to track the participants for 3 months.
However, self-reported habit measures have been criticized (Hag-
ger, Rebar, Mullan, Lipp, & Chatzisarantis, 2015) because people
may have limited conscious knowledge of automatic behaviors.
Nevertheless, some researchers have still contested the belief that
people can provide valid information on their habits using such
self-reports (Orbell & Verplanken, 2015). In addition, there are no
current alternatives for measuring behavioral habits and automa-
ticity, particularly for frequent repeated measurements. To suc-
cessfully analyze the evolution of automaticity we used a new type
of learning curve that seems to better represent the habit-formation
process. Unlike power learning curves, which show a substantial
gain at the beginning, the logistic function suggests that some
Figure 2. Relation between salivary cortisol values and the time necessary for behavior automatization (x
.95
)
for the morning and evening groups.
Table 2
Regression Models Used to Determine the Mediating Role of Cortisol on the Effect of the
Product Representing the Condition Chronotype Interaction on the Time Taken to Form a
Behavioral Habit
Model and
variable Coefficient SE t p
95% CI
LL UL
Model predicting Cortisol: R
2
.33, F(1, 40) 20.699, p.0001
Constant 1.634 .132 12.366 .000 1.367 1.901
Condition MEQ .012 .003 4.544 .000 .007 .018
Model predicting x
.95
without inclusion of the mediator: R
2
.20, F(3, 38) 2.18, p.10
Constant 281.140 171.910 1.635 .110 66.879 629.159
Condition MEQ .566 .219 2.591 .013 1.009 .124
Intention 28.987 35.409 .819 .418 100.671 42.696
Sex 25.203 20.360 1.238 .223 66.419 16.014
Model predicting x
.95
with inclusion of the mediator: R
2
.28, F(4, 37) 2.51, p.058
Constant 284.753 148.792 1.914 .063 16.733 586.239
Cortisol 22.404 11.129 2.013 .051 44.955 .146
Condition MEQ .283 .248 1.141 .261 .787 .220
Intention 22.369 31.232 .716 .478 85.652 40.914
Sex 22.369 19.934 1.122 .269 62.760 18.023
Note.CIconfidence interval; LL lower limit; ULCI upper limit; MEQ Morningness-Eveningness
Questionnaire.
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4FOURNIER ET AL.
participants may actually start more slowly (see Murre, 2014),
which is often the case for complex learning such as the adoption
of a new healthy behavior that requires plenty of conscious effort
at the beginning to accommodate the behavior in the living context
and evaluate its costs and benefits (Wood & Neal, 2016). Only
when these obstacles have been overcome may the automatization
process begin.
A limitation of our work concerns the relatively small sample
size of this study due to a limited recruitment capacity and attrition
throughout the duration of the intervention. Although the signifi-
cant results indicate that sufficient evidence was obtained to reject
the null hypothesis despite the limited power, this study should be
replicated to ensure reproducibility. In future studies, other medi-
ators should also be tracked. We could not demonstrate a full
mediation effect, and it therefore seems likely that other factors
may come into play. As Schwabe, Tegenthoff, Höffken, and Wolf
(2012) indicated, cortisol alone does not affect habitual behavior,
and concurrent glucocorticoid and noradrenergic activity may have
a role since it is known that this concurrent activity is needed to
shift learning from goal-directed to habitual control. Some psy-
chological factors might also explain the effect. For example, it is
possible that the behavior was perceived as less difficult, more
satisfying, or more easily cued in the morning than in the evening.
Moreover, people assigned to the morning group were given a
prior cue (i.e., do the stretch after waking up), but those in the
evening group were not (i.e., do the stretch before going to bed).
Because cueing is a critical aspect of habit formation (Gardner,
2015), it could have led to reinforcement of automatization in the
morning condition.
Conclusion
In this study, we demonstrated that a newly adopted stretching
behavior became habitual more quickly when it was performed in
the morning as opposed to the evening. This effect was mediated
by cortisol levels— higher cortisol levels in the morning resulted in
accelerated automatization. By matching the intervention time
with individuals’ chronobiology, faster automatization and there-
with higher success rates are likely. The extent to which these
results can be translated to applied settings and more complex
behaviors should be further investigated.
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Received September 7, 2016
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Accepted March 3, 2017
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6FOURNIER ET AL.
... One focuses on the nature of the relationship between context-consistent behavioural repetition and habit formation; for example, whether each repetition has an equal impact on habit development (Schnauber-Stockmann & Naab, 2018). The other focuses on potential moderators of this relationship, such as whether habit develops more quickly for morning versus evening performances (Fournier, d'Arripe-Longueville, Rovere et al., 2017). These questions can be addressed by two broad study types, discerned by their sensitivity to habit formation parameters. ...
... Habit formation studies must only be conducted when habit is expected to form. Habit develops when a behaviour is repeated in a consistent context (Fournier, d'Arripe-Longueville, Rovere et al., 2017;Lally et al., 2010). Increases in behavioural repetition must be brought about by changes to one or more of three fundamental determinants: motivation, capability, and opportunity (Michie et al., 2011). ...
... Several studies have found habit to strengthen asymptotically over time, with rapid early gains slowing and levelling off (Fournier, d'Arripe-Longueville, Rovere et al., 2017;Lally et al., 2010; but see, Keller et al., 2021;Schnauber-Stockmann & Naab, 2018). Asymptotic growth renders the use of linear analyses to model habit formation problematic. ...
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Advances in understanding how habit forms can help people change their behaviour in ways that make them happier and healthier. Making behaviour habitual, such that people automatically act in associated contexts due to learned context-response associations, offers a mechanism for maintaining new, desirable behaviours even when conscious motivation wanes. This has prompted interest in understanding how habit forms in the real world. To reliably inform intervention design, habit formation studies must be conceptually and methodologically sound. This paper proposes methodological criteria for studies tracking real-world habit formation, or potential moderators of the effect of repetition on formation. A narrative review of habit theory was undertaken to extract essential and desirable criteria for modelling how habit forms in naturalistic settings, and factors that influence the relationship between repetition and formation. Next, a methodological review identified exemplary real-world habit formation studies according to these criteria. Fourteen methodological criteria, capturing study design (four criteria), measurement (six criteria), and analysis and interpretation (four criteria), were derived from the narrative review. Five extant studies were found to meet our criteria. Adherence to these criteria should increase the likelihood that studies will offer revealing conclusions about how habits develop in real-world settings.
... Longitudinal studies, in which the intentional acquisition of habits is tracked within-person after each habit repetition, are scarce. We identified only five relevant published papers to date (Lally et al., 2010;Fournier et al., 2017;Stojanovic et al., 2020Stojanovic et al., , 2021Van der Weiden et al., 2020) and in none of them context stability has been tested for a possible influence on the habit building process. To fill this gap and add to the scarce longitudinal habit research, we measure perceived context stability in two longitudinal studies (including a context stability manipulation in Study 1), and test the effects of context stability during habit acquisition on automaticity and goal attainment, which are both variables tied to the quality of habit execution. ...
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In this paper, we investigate the effects of context stability on automaticity and goal attainment in intentional habit building. We used hierarchical growth curve modeling and multilevel mediation to test our hypotheses on two datasets. In Study 1, N = 95 university students ( N = 2,482 habit repetitions) built new study habits over a period of 6 weeks with manipulated context stability. One group was instructed to constantly vary the context of their habit repetitions by changing rooms and times and the other group was instructed to keep the context of habit performance stable. In Study 2, N = 308 habits ( N = 2,368 habit repetitions) from N = 218 users of a published habit building app were analyzed without manipulating but measuring context stability. We found the same pattern in both datasets: Context stability predicted more automaticity and higher habit repetition goal attainment. We also found that the effect of context stability on habit repetition goal attainment was partially mediated by automaticity in both datasets. These results show that context does not only act as a trigger for habit instigation but also has an ongoing effect on habit execution.
... In a definitive trial, we would conduct an exploratory analysis using the activPAL data alongside habit strength to investigate whether those who walk in the morning have higher habit strength than those who walk in the evening. Previous research suggests that habits form quicker in the morning than the evening [87]. Chronotype (individual differences in sleep timing and in preferences for a given time of day) will be considered a covariate in this analysis and assessed using a sub-scale of the Morningness-Eveningness questionnaire (MEQ) [88,89]. ...
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Background There are multiple health benefits from participating in physical activity after a cancer diagnosis, but many people living with and beyond cancer (LWBC) are not meeting physical activity guidelines. App-based interventions offer a promising platform for intervention delivery. This trial aims to pilot a theory-driven, app-based intervention that promotes brisk walking among people living with and beyond cancer. The primary aim is to investigate the feasibility and acceptability of study procedures before conducting a larger randomised controlled trial (RCT). Methods This is an individually randomised, two-armed pilot RCT. Patients with localised or metastatic breast, prostate, or colorectal cancer, who are aged 16 years or over, will be recruited from a single hospital site in South Yorkshire in the UK. The intervention includes an app designed to encourage brisk walking (Active 10) supplemented with habit-based behavioural support in the form of two brief telephone/video calls, an information leaflet, and walking planners. The primary outcomes will be feasibility and acceptability of the study procedures. Demographic and medical characteristics will be collected at baseline, through self-report and hospital records. Secondary outcomes for the pilot (assessed at 0 and 3 months) will be accelerometer measured and self-reported physical activity, body mass index (BMI) and waist circumference, and patient-reported outcomes of quality of life, fatigue, sleep, anxiety, depression, self-efficacy, and habit strength for walking. Qualitative interviews will explore experiences of participating or reasons for declining to participate. Parameters for the intended primary outcome measure (accelerometer measured average daily minutes of brisk walking (≥ 100 steps/min)) will inform a sample size calculation for the future RCT and a preliminary economic evaluation will be conducted. Discussion This pilot study will inform the design of a larger RCT to investigate the efficacy and cost-effectiveness of this intervention in people LWBC. Trial registration ISRCTN registry, ISRCTN18063498. Registered 16 April 2021.
... Among 96 participants performing a self-chosen dietary or physical activity behaviour in response to a self-chosen once-daily cue, an asymptotic curve -characterised by initial rapid gains that decelerate as a plateau is reached -provided a good fit to the data for many, and fitted best for those who most consistently performed the behaviour daily (Lally et al, 2010). An alternative asymptotic curve, depicting slower formation at the outset, fitted participants consistently performing a stretching exercise in the morning or evening (Fournier et al., 2017;see too Tobias, 2009). These findings imply that habit-forming interventions should provide most support at the early stages to maintain repetition before automaticity peaks, and that support may then be lessened, because habit alone should sustain action. ...
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Objective Habitual behaviours are triggered automatically, with little conscious forethought. Theory suggests that making healthy behaviours habitual, and breaking the habits that underpin many ingrained unhealthy behaviours, promotes long-term behaviour change. This has prompted interest in incorporating habit formation and disruption strategies into behaviour change interventions. Yet, notable research gaps limit understanding of how to harness habit to change real-world behaviours. Methods Discussions among health psychology researchers and practitioners, at the 2019 European Health Psychology Society ‘Synergy Expert Meeting’, generated pertinent questions to guide further research into habit and health behaviour. Results In line with the four topics discussed at the meeting, 21 questions were identified, concerning: how habit manifests in health behaviour (3 questions); how to form healthy habits (5 questions); how to break unhealthy habits (4 questions); and how to develop and evaluate habit-based behaviour change interventions (9 questions). Conclusions While our questions transcend research contexts, accumulating knowledge across studies of specific health behaviours, settings, and populations will build a broader understanding of habit change principles and how they may be embedded into interventions. We encourage researchers and practitioners to prioritise these questions, to further theory and evidence around how to create long-lasting health behaviour change.
Article
Background High automaticity in healthy nutrition behaviors is related to long-term maintenance of these behaviors. Drawing upon theoretical frameworks of habit formation, proposed antecedents such as intrinsic reward, anticipated regret, and self-efficacy are important correlates of automaticity, but not much is known about their day-by-day relationships with automaticity in healthy nutrition behaviors. This study tested previous-day within-person (i.e., from one day to the next) and same-day within-person associations of intrinsic reward, anticipated regret, and self-efficacy with automaticity of a healthy nutrition behavior, for which participants attempted to form a new habit. Methods Secondary analyses of a randomized controlled trial with two planning intervention conditions including a longitudinal sample of n = 135 participants (age: M = 24.82 years; SD = 7.27) are reported. Participants formed a plan on a self-selected healthy nutrition behavior to become a new habit and were followed up over 12 weeks assessing daily levels of plan-specific intrinsic reward, anticipated regret, self-efficacy, and automaticity. Lagged multilevel models with 84 study days nested in participants estimated previous-day within-person, same-day within-person, and between-person relationships of intrinsic reward, anticipated regret, and self-efficacy with automaticity. Findings Regarding within-level relationships, higher-than-usual levels of intrinsic reward, anticipated regret, and self-efficacy of the same day but not of the previous day were associated with higher within-person automaticity. With respect to between-level relationships, higher between-levels (i.e., higher person mean levels across the study period) of intrinsic reward, anticipated regret, and self-efficacy were linked with higher automaticity. Discussion Findings point towards the potential to intervene on intrinsic reward, anticipated regret, and self-efficacy when aiming to promote a new healthy nutrition habit.
Article
Insufficient sleep and mistimed sleep are prominent, yet underappreciated and understudied, contributors to poor mental health and to mental disorders. The evidence that improving sleep and circadian functioning is an important pathway to mental health continues to mount. The goal of this paper is to highlight three major challenges ahead. Challenge 1 points to the possibility that comorbidity is the norm not the exception for the sleep and circadian disorders that are associated with mental disorders. Hence, the sleep and circadian problems experienced by people diagnosed with a mental disorder may not fit into the neat diagnostic categories of existing nosologies nor be adequately treated with single disorder approaches. The Sleep Health Framework and the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) are discussed as alternative approaches. Challenge 2 points to the large time lag between the development of a treatment and the availability of that treatment in routine clinical practice. This is a key reason for the emergence of implementation science, which is a flourishing, well-developed and quickly moving field. There is an urgent need for more applications of implementation science within sleep and circadian science. Challenge 3 describes one of the greatest puzzles of our time—the need to unlock the fundamental elements of behavior change. There is potential to harness the science of behavior change to encourage widespread engagement in sleep health behavior and thereby reduce the staggering burden of sleep and circadian problems and the associated mental health problems.
Article
Background: Physical and mental health benefits can be attained from persistent, long-term performance of mindfulness meditation with a mobile meditation app, but in general, few mobile health app users persistently engage at a level necessary to attain the corresponding health benefits. Anchoring or pairing meditation with a mobile app to an existing daily routine can establish an unconsciously initiated meditation routine that may improve meditation persistence. Objective: The purpose of this study was to test the use of either personalized anchors or fixed anchors for establishing a persistent meditation app routine with the mobile app, Calm. Methods: We conducted a randomized controlled trial and randomly assigned participants to one of 3 study groups: (1) a personalized anchor (PA) group, (2) fixed anchor (FA) group, or (3) control group that did not use the anchoring strategy. All participants received app-delivered reminder messages to meditate for at least 10 minutes a day using the Calm app for an 8-week intervention period, and app usage data continued to be collected for an additional 8-week follow-up period to measure meditation persistence. Baseline, week 8, and week 16 surveys were administered to assess demographics, socioeconomic status, and changes in self-reported habit strength. Results: A total of 101 participants across the 3 study groups were included in the final analysis: (1) PA (n=56), (2) FA (n=49), and (3) control group (n=62). Participants were predominantly White (83/101, 82.2%), female (77/101, 76.2%), and college educated (ie, bachelor's or graduate degree; 82/101, 81.2%). The FA group had a significantly higher average odds of daily meditation during the intervention (1.14 odds ratio [OR]; 95% CI 1.02-1.33; P=.04), and all participants experienced a linear decline in their odds of daily meditation during the 8-week intervention (0.96 OR; 95% CI 0.95-0.96; P<.001). Importantly, the FA group showed a significantly smaller decline in the linear trend of their odds of daily meditation during the 8-week follow-up (their daily trend increased by 1.04 OR from their trend during the intervention; 95% CI 1.01-1.06; P=.03). Additionally, those who more frequently adhered to their anchoring strategy during the intervention typically used anchors that occurred in the morning and showed a significantly smaller decline in their odds of daily meditation during the 8-week follow-up period (1.13 OR; 95% CI 1.02-1.35; P=.007). Conclusions: The FA group had more persistent meditation with the app, but participants in the FA or PA groups who more frequently adhered to their anchoring strategy during the intervention had the most persistent meditation routines, and almost all of these high anchorers used morning anchors. These findings suggest that the anchoring strategy can create persistent meditation routines with a mobile app. However, future studies should combine anchoring with additional intervention tools (eg, incentives) to help more participants successfully establish an anchored meditation routine. Trial registration: ClinicalTrials.gov NCT04378530; https://clinicaltrials.gov/ct2/show/NCT04378530.
Article
Habits affect nearly every aspect of our physical and mental health. Although the science of habit formation has long been of interest to psychological scientists across disciplines, we propose that applications to clinical psychological science have been insufficiently explored. In particular, evidence-based psychological treatments (EBPTs) are interventions targeting psychological processes that cause and/or maintain mental illness and that have been developed and evaluated scientifically. An implicit goal of EBPTs is to disrupt unwanted habits and develop desired habits. However, there has been insufficient attention given to habit-formation principles, theories, and measures in the development and delivery of EBTPs. Herein we consider whether outcomes following an EBPT would greatly improve if the basic science of habit formation were more fully leveraged. We distill six ingredients that are central to habit formation and demonstrate how these ingredients are relevant to EBPTs. We highlight practice points and an agenda for future research. We propose that there is an urgent need for research to guide the application of the science of habit formation and disruption to the complex “real-life” habits that are the essence of EBPTs.
Article
Objective /background: School closure and home quarantine has been implemented worldwide during the coronavirus disease 2019 (COVID-19) outbreak. The study aims to assess the associations of circadian rhythm abnormalities (CRA) during the COVID-19 outbreak and mental health in Chinese undergraduates. Methods A nationwide cross-sectional university-based survey was conducted from 4th February to 12th, 2020. Based on different geographical locations and purposive sampling approach, 19 universities from 16 provinces or municipalities in the mainland of China were selected. A total of 14 789 participants were recruited by using multistage stratified random sampling. The data of CRA were collected by self-reported questionnaires consist of 4 items involved rest-activity cycle, diet rhythm, wake up rhythm and sleep rhythm. The Patient Health Questionnaire and the Generalized Anxiety Disorder were applied to evaluate the symptoms of depression and anxiety. Chi-square test and ordinal logistic regression models were used to describe the distributions and associations of CRA and mental health. Results A total of 11 787 students [female: 6731(57.1%)] aged 15-26 years old (M=20.45, SD=1.76) were analyzed (response rate: 79.7%). The results showed the percentage of CRA were 17.5-28.7%. The prevalence of depression and anxiety were significantly higher in students with single CRA. Students who reported the coexistence of four CRA were more likely to be with the symptoms of depression (OR: 4.43, 95% CI: 3.91–5.03) and anxiety (OR: 3.11, 95% CI: 2.70–3.60). Dose-response relationships were found between multiple CRA and mental problems. Conclusion Circadian rhythm abnormalities are positively associated with mental health among university studies. Mental health care is needed for college students during the COVID-19 epidemic period.
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Background. The term ‘habit’ is widely used to predict and explain behaviour. This paper examines use of the term in the context of health-related behaviour, and explores how the concept might be made more useful. Method: A narrative review is presented, drawing on a scoping review of 136 empirical studies and eight literature reviews undertaken to document usage of the term ‘habit’, and methods to measure it. A coherent definition of ‘habit’, and proposals for improved methods for studying it, were derived from findings. Results: Definitions of ‘habit’ have varied in ways that are often implicit, and not coherently linked with an underlying theory. A definition is proposed whereby habit is a process by which a stimulus generates an impulse to act as a result of a learned stimulus-response association. Habit-generated impulses may compete or combine with impulses and inhibitions arising from other sources, including conscious decision-making, to influence responses, and need not generate behaviour. Most research on habit is based on correlational studies using self-report measures. Conclusion: Adopting a coherent definition of ‘habit’, and a wider range of paradigms, designs and measures to study it, may accelerate progress in habit theory and application.
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Background The twelve-item Self-Report Habit Index (SRHI) is the most popular measure of energy-balance related habits. This measure characterises habit by automatic activation, behavioural frequency, and relevance to self-identity. Previous empirical research suggests that the SRHI may be abbreviated with no losses in reliability or predictive utility. Drawing on recent theorising suggesting that automaticity is the ‘active ingredient’ of habit-behaviour relationships, we tested whether an automaticity-specific SRHI subscale could capture habit-based behaviour patterns in self-report data. Methods A content validity task was undertaken to identify a subset of automaticity indicators within the SRHI. The reliability, convergent validity and predictive validity of the automaticity item subset was subsequently tested in secondary analyses of all previous SRHI applications, identified via systematic review, and in primary analyses of four raw datasets relating to energy‐balance relevant behaviours (inactive travel, active travel, snacking, and alcohol consumption). Results A four-item automaticity subscale (the ‘Self-Report Behavioural Automaticity Index’; ‘SRBAI’) was found to be reliable and sensitive to two hypothesised effects of habit on behaviour: a habit-behaviour correlation, and a moderating effect of habit on the intention-behaviour relationship. Conclusion The SRBAI offers a parsimonious measure that adequately captures habitual behaviour patterns. The SRBAI may be of particular utility in predicting future behaviour and in studies tracking habit formation or disruption.
Article
The interaction of homeostatic and circadian processes in the regulation of waking neurobehavioral functions and sleep was studied in six healthy young subjects. Subjects were scheduled to 15–24 repetitions of a 20-h rest/activity cycle, resulting in desynchrony between the sleep-wake cycle and the circadian rhythms of body temperature and melatonin. The circadian components of cognitive throughput, short-term memory, alertness, psychomotor vigilance, and sleep disruption were at peak levels near the temperature maximum, shortly before melatonin secretion onset. These measures exhibited their circadian nadir at or shortly after the temperature minimum, which in turn was shortly after the melatonin maximum. Neurobehavioral measures showed impairment toward the end of the 13-h 20-min scheduled wake episodes. This wake-dependent deterioration of neurobehavioral functions can be offset by the circadian drive for wakefulness, which peaks in the latter half of the habitual waking day during entrainment. The data demonstrate the exquisite sensitivity of many neurobehavioral functions to circadian phase and the accumulation of homeostatic drive for sleep.
Article
Instrumental learning occurs through both goal-directed and habit memory systems, which are supported by anatomically distinct brain systems. Interestingly, stress may promote habits at the expense of goal-directed performance, since stress before training in an instrumental task was found to cause individuals to carry on with the learned association in spite of a devalued outcome. These findings nevertheless left pending questions, and it has been difficult to determine which system is primarily affected by stress (an improved habit system, an impaired goal-directed system, or both) and at what point the stress acts (at the moment of learning by making more resistant habits, or after devaluation by making individuals less sensitive to change in the outcome value). The present study (N=72 participants, 63 males and 9 females) aimed to answer these questions with (i) an instrumental task that dissociates the two memory systems and (ii) three conditions of psychosocial stress exposure (Trier Social Stress Test): stress induced before learning, before devaluation, and not induced for the control group. The study confirms that exposure to psychosocial stress leads to habitual performance. Moreover, it provides new insight into this effect by locating its origin as an impairment in the capacity of the goal-directed system rather than a reinforcement in habit learning. These results are discussed in light of recent neurobiological models of stress and memory.
Article
Interventions to change health behaviors have had limited success to date at establishing enduring healthy lifestyle habits. Despite successfully increasing people's knowledge and favorable intentions to adopt healthy behaviors, interventions typically induce only short-term behavior changes. Thus, most weight loss is temporary, and stepped-up exercise regimens soon fade. Few health behavior change interventions have been successful in the longer term. In this article, we unpack the behavioral science of health-habit interventions. We outline habit-forming approaches to promote the repetition of healthy behaviors, along with habit-breaking approaches to disrupt unhealthy patterns. We show that this two-pronged approach—breaking existing unhealthy habits while simultaneously promoting and establishing healthful ones—is best for long-term beneficial results. Through specific examples, we identify multiple intervention components for health policymakers to use as a framework to bring about lasting behavioral public health benefits.
Article
Across two studies comparing younger and older adults, age differences in optimal performance periods were identified (Study 1), and then shown to be an important determinant of memory differences (Study 2). A norming study showed that while most younger adults were Evening or Neutral types, as determined by a standard questionnaire, the vast majority of older adults were Morning types. A second study compared the recognition performance of younger and older adults tested in the morning or in the late afternoon. Substantial age differences were found in the late afternoon, when younger but not older adults were at their optimal times. However, no age differences in memory performance were found in the morning, when older but not younger adults were at their peak period. Thus, synchrony between optimal performance periods and the time at which testing is conducted may well be a critical variable in determining group differences in intellectual performance, particularly between older and younger adults.
Article
The study under report examined the effects of time of day on prose recall in morning- and evening-type individuals. Subjects listened to two easy and two difficult passages at either 09.00, 14.00 or 20.00. Immediately after listening to a taperecorded version of each story, subjects were asked to write their recalls. The results indicated that recall decreased across time of day for morning types but increased for evening types. The effects of importance level were similar for passages at both difficulty levels at all times of day; however, time-of-day effects were largest for highly important-idea units from difficult passages. The results demonstrated that time of day influences immediate recall of prose in adults, and the pattern of these effects depended upon whether the subject was a morning or evening type. It was suggested that subsequent examinations of time of day and prose memory should utilize concurrent measures of encoding effort to assess whether processing strategies change across time of day.