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Habitual Exercise Instigation (vs. Execution) Predicts Healthy Adults' Exercise Frequency


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Habit is thought to be conducive to health behavior maintenance, because habits prompt behavior with minimal cognitive resources. The precise role of habit in determining complex behavioral sequences, such as exercise, has been underresearched. It is possible that the habit process may initiate a behavioral sequence (instigation habit) or that, after instigation, movement through the sequence is automated (execution habit). We hypothesized that exercise instigation habit can be empirically distinguished from exercise execution habit and that instigation habit strength is most predictive of future exercise and reflective of longitudinal exercise behavior change. Further, we evaluated whether patterned exercise action-that is, engaging in the same exercise actions from session to session-can be distinct from exercise execution habit. Healthy adults (N = 123) rated their exercise instigation and execution habit strengths, patterned exercise actions, and exercise frequency in baseline and 1-month follow-up surveys. Participants reported exercise engagement via electronic daily diaries for 1 month. Hypotheses were tested with regression analyses and repeated-measures analyses of variance. Exercise instigation habit strength was the only unique predictor of exercise frequency. Frequency profiles (change from high to low or low to high, no change high, no change low) were associated with changes in instigation habit but not with execution habit or patterned exercise action. Results suggest that the separable components of exercise sessions may be more or less automatic, and they point to the importance of developing instigation habit for establishing frequent exercise. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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Habitual Exercise Instigation (versus Execution) Predicts Healthy Adults’ Exercise Frequency
L. Alison Phillips
Benjamin Gardner
Author Note
L. Alison Phillips, Department of Psychology, Iowa State University, Ames, Iowa, USA
Benjamin Gardner, Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
(IoPPN), King’s College London, De Crespigny Park, London, SE5 8AF, UK
The authors wish to acknowledge Miriam Eisenberg, Steffi Renninger, Jessica Abrams, Ben
Laman-Maharg, Katie Thompson, and Margot Quinn for their invaluable assistance in collecting the data
for the current study.
Correspondence concerning this article should be addressed to L. Alison Phillips, W112
Lagomarcino Hall, Department of Psychology, Iowa State University, Ames, Iowa, 50010. Contact:; Phone: +1 515-294-3393; Fax: +1 515-294-6424
**This is an accepted manuscript for publication in Health Psychology and is copyrighted material (APA
copyright). The citation and DOI for this article are the following:
Phillips, L. A., & Gardner, B. (2015, July 6). Habitual Exercise Instigation (vs. Execution)
Predicts Healthy Adults Exercise Frequency. Health Psychology. Advance online publication.
This article may not exactly replicate the final version published in the APA journal. It is not the copy of
Objective. “Habit is thought to be conducive to health behavior maintenance, since habits prompt
behavior with minimal cognitive resources. The precise role of habit in determining complex behavioral
sequences, such as exercise, has been under-researched. It is possible that the habit process may initiate
a behavioral sequence (“instigation habit”), or that, after instigation, movement through the sequence is
automated (“execution habit”). We hypothesize that exercise instigation habit can be empirically
distinguished from exercise execution habit and that instigation habit strength is most predictive of
future exercise and reflective of longitudinal exercise behavior change. Further, we evaluate whether
patterned exercise actioni.e., engaging in the same exercise actions from session to session can be
distinct from exercise execution habit.
Method. Healthy adults (n=123) rated their exercise instigation and execution habit strengths, patterned
exercise action, and exercise frequency in a baseline and a 1-month follow-up survey. Participants
reported exercise engagement via electronic daily diaries for one month. Hypotheses were tested with
regression analyses and repeated-measures ANOVA.
Results. Exercise instigation habit strength was the only unique predictor of exercise frequency (self-
reported at follow-up, β=0.38, p=0.002; daily diary reports, β=0.31, p=0.01). Frequency profiles (change
from high to low or low to high; no change high; no change low) were associated with change in
instigation habit but not with execution habit or patterned exercise action.
Conclusions. Results suggest that the separable components of exercise-sessions may be more or less
automatic and point to the importance of developing instigation habit for establishing frequent exercise.
Keywords. habit development; exercise maintenance; exercise frequency; habit strength; automaticity
Habitual Exercise Instigation (versus Execution) Predicts Healthy Adults’ Exercise Frequency
Long-term health behavior maintenance is required for wellness (Artinian, Fletcher, Mozaffarian, et
al., 2010), yet interventions rarely achieve lasting behavior change (Marcus, Forsyth, Stone, et al., 2000).
The formation of habit i.e., a process whereby encountering a situation in which a behavior has been
consistently performed automatically generates an impulse to perform the behavior, due to learned
situation-behavior associations (Gardner, 2014) has been suggested as a mechanism for behavior
maintenance (Rothman, 2000; Rothman, Sheeran & Wood, 2009). Theoretically, if interventions develop
habits, regulation of behavior should move away from effortful, deliberative enactment and towards
automatic activation and long-term maintenance (Lally, Wardle, & Gardner, 2011). A wealth of research,
including longitudinal studies, has shown habit to predict behavioral frequency across health domains
(Gardner, de Bruijn & Lally, 2011). Rigorous research on the precise role of habit in health behaviors,
such as exercise, is required. This paper discusses current conceptualizations of exercise habit and
empirically evaluates whether components of exercise performance could usefully be separated and, if
so, whether habit-strength of these components are differentially predictive of future exercise behavior.
Some researchers have theorized that exercise can become habitual (i.e. automatically triggered by
contextual cues), but the meaning of habitual exercise has been debated (Hagger, Rebar, Mullan, Lipp,
& Chatzisarantis, 2014; Maddux, 1997). Aarts, Paulussen, and Schaalma (1997) conceptualized habitual
exercise as specific actions (e.g., going for a run, going to the gym) that occur when the goal to exercise
is automatically triggered by a situational cue. The exercise occurs with conscious awareness, but is
mentally efficient and follows a specific pattern. The notion of habits being automated (completed
without awareness or control; Bargh, 1989, 1992) also suggests instigation determines execution
through a “cascade” of habitual actions, each action triggering the next. These conceptualizations of
automated behavior have not been empirically examined in health domains, but they suggest that
exercise instigation may determine exercise execution rather than the two being separately determined.
Verplanken and Melkevik (2008) characterized habit as a goal-dependent sequence of behaviors that
has acquired a degree of automaticity, and is executed in stable contexts (p.17). They authors posited
that the decision to exercise may become habitually triggered by situational cues but that performance
of exercise activity itself may be mindful (see too Maddux, 1997). Whereas Aarts et al. (1997) imply that
habitual exercise is both automatically instigated (i.e. chosen) and executed (i.e. performed),
Verplanken and Melkevik (2008) imply that instigation and execution can be determined by different
factors (e.g., instigation becoming habitual, but execution remaining deliberative).
A recent theoretical review reasoned that behavioral instigation need not determine behavioral
execution (Gardner, 2014). Gardner suggested that for simple actions, characterized by fewer behavioral
sub-components, the distinction between instigation and execution may have little utility; when
pressing a light switch, for example, it is hard to identify a transition from deciding to press the switch
(instigation) to physically pressing the switch (execution). For more complex behaviors such as exercise,
however, the distinction between habitual deciding (instigation) and habitual doing (execution)
becomes meaningful. Consider a habit of exercising in the gym; the individual who regularly exercises
and is new to a particular gym may deliberate over whether to visit the gym (no instigation habit), but
once in the gym, may automatically enact a set of well-rehearsed sequences of specific activities
(execution habit). Conversely, another regular exerciser may never deliberate on whether to visit the
gym (instigation habit), but might deliberatively choose specific exercise actions for each particular
session (no execution habit), aiming for variability and balance in exercise for challenge or enjoyment. A
third possibility is that the individual may both be automatically prompted to visit the gym and
automatically enact a sequence of activities within the gym. Gardner (2014) argued that any of these
three examples could be termed exercise habit, as they are all facilitated at least partly by habit.
We propose that, although they may be positively associated and both can develop through
consistent behavior repetition (Kaushal & Rhodes, 2015; Lally, van Jaarsveld, Potts, & Wardle, 2010;
Verplanken, 2006), exercise instigation habit is distinct from exercise execution habit, and that the
presence of one need not determine the other. We define instigation habit as a process whereby cues
automatically generate an impulse to initiate an (exercise) behavior, based on learned cue-behavior
associations (see too Gardner, 2014, 2015). The cued impulse is a low-level cognitive response that,
unless sufficiently opposed, generates the behavior automatically and effortlessly. While habit
instigation is a cognitive process, this does not mean that it is a conscious or deliberative process
requiring mental resources. People are typically unaware of habit-generated impulses, and so instigation
habit is likely subjectively experienced as the direct activation of action, without conscious mediation
(Gardner, 2015). In contrast, we define execution habit as a process whereby sub-actions within a
higher-level action unit are controlled by habit, such that progression through sub-actions within the
action unit (e.g. ‘lift weights’, ‘use treadmill’) is facilitated by the automatic activation of (an impulse to
engage in) the sub-actions, as based on learned cue-behavior associations. Execution may be wholly or
only partly determined by habit; that is, at least one, but not all, sub-actions must be habitually
activated for an execution habit to be said to exist. Instigation and execution habit are conceptually
separate: habitual instigation does not depend on habitual execution, and vice versa. The distinction
between instigation and execution habit has important implications for understanding and changing
behavior. If instigation and execution are separable, they may be differentially important for behavioral
maintenance, have different developmental trajectories, and have different determinants. Such
information could optimize interventions to promote long-term, regular exercise.
To evaluate whether instigation and execution are separable, habit measures must distinguish
between instigation and execution of behavior. Current measures do not make this distinction. The Self-
Report Habit Index (Verplanken & Orbell, 2003) and its automaticity subscale, the Self-Report Behavioral
Automaticity Index (Gardner, Abraham, Lally, & de Bruijn, 2012) measure general behavioral
automaticity, through agreement with items such as “Exercising is something I do automatically”. These
predict exercise behavior frequency (e.g., Rhodes, de Bruijn, & Matheson, 2010), but it is not clear
whether they capture exercise instigation, execution, or both. Wood, Tam, and Witt’s (2005) ‘frequency-
in-context’ measure of habit strength also fails to discern instigation from execution of behavior.
Available exercise-specific measures of habit strength have separable sub-scales, but none afford
testing of the distinction between behavioral instigation and execution. The Exercise Habit Survey (EHS;
Tappe & Glanz, 2013) assesses the degree to which exercise occurs at consistent times of day, in
consistent locations, and with consistent partners. A final component, exercise constancy, regards the
degree to which the same exercise actions are performed from day to day. This concept is similar to the
‘patterned action’ sub-component of Grove, Zillich, and Medic’s (2014) exercise habit strength. Exercise
constancy and patterned action are distinct from what we propose as exercise execution habit strength,
which is the degree to which exercise actions are executed habitually, i.e. without deliberation, rather
than the degree to which the same exact actions are executed in each exercise session. For example, an
individual might automatically transition from action to action during each exercise session (strong
execution habit) but exercise actions may be patterned at the outset, prior to habit formation.
The current study is the first to explore potential differences in the relative importance of
conceptually distinct habit components for predicting exercise. Habits develop when cues in the
environment are conditioned to trigger the behavior (Orbell & Verplanken, 2010; Wood & Neal, 2007).
These triggers activate a mental representation of behavior (i.e. an impulse) and so instigate that
behavior, and are therefore the necessary and likely most strongly predictive of behavioral frequency.
Longitudinally, greater exercise frequency should result in a strengthening of instigation habit, more so
than in a strengthening of execution habit. The long-term, regular exerciser that habitually instigates
exercise sessions on a daily basis may purposefully execute different exercise actions in order to
maximize enjoyment or physical challenge, or minimize boredom (Verplanken & Melkevic, 2008).
The current study used a longitudinal design to document exercise instigation and exercise execution
habit strengths as discrete predictors of exercise frequency over the subsequent 4 weeks. Three
hypotheses were tested: (1) Exercise instigation and exercise execution habit strength can be empirically
observed as distinct constructs. Further, both constructs will be distinct from the construct of patterned
exercise-action; (2) Exercise instigation habit strength (at baseline) will more strongly predict
subsequent exercise frequency than will exercise execution habit strength; (3) Change in exercise
frequency over the month of the study will be associated with change in instigation habit strength and
not with change in execution habit strength nor patterned exercise action.
Participants were university college students (n=87) and staff/faculty (n=36). Most were female (65%
of students and 89% of staff/faculty) and White/Caucasian (71% and 78%, respectively). Approximately
25% of the total sample had overweight to obese BMI values. At the study outset, 4% self-reported not
previously engaging in regular exercise but being willing to exercise for the duration of the study at least
twice per week for 20 minutes; 23% reported exercising some but not regularly; 15% reported exercising
regularly but for less than 6 months; 9% reported exercising regularly for 6-12 months; and 49%
reported exercising regularly and for longer than 12 months. The average age for students was 19.48
years (SD=2.08; range 18-33) and for staff/faculty, 37.61 years (SD=13.82; range 21-73).
Students and faculty/staff were recruited via advertisements for a longitudinal study regarding
physical activity and daily schedules. Student participants were recruited via an online web system for
administering course credit for research participation; staff/faculty participants were recruited via email
advertisements, flyers, and word of mouth. Inclusion criteria, made clear in the study advertisements,
were: 18 or older, currently engaging in exercise or willing to exercise for at least 20 minutes, twice per
week for the duration of the study, and written and verbal English proficiency. Participants (n=123)
came in to the lab, provided informed consent, and then completed baseline survey items on a
computer and received instructions on completing an electronic daily diary for the subsequent month.
Daily diary completion was monitored and reminder emails sent to participants non-compliant for two
or more days in a row. At the end of the month, participants completed a final online survey and were
compensated with partial course credit and/or cash. Students received $20 in addition to course credit;
staff/faculty participants received $40. Measures were also taken, for unrelated studies, of body image,
exercise motivation, and physical activity (via accelerometers), data for which are not reported here.
Most participants were using an accelerometer in addition to filling out the daily diaries; however,
accelerometer data was not used in the current analyses because accelerometers measure overall
physical activity (e.g. total steps in a day) and do not reliably capture frequency of purposeful, discrete
exercise sessions, which was the relevant outcome for the current hypotheses. All study procedures
were approved by the human ethics committee of the relevant institution.
A pilot test was conducted in a separate sample of 124 college-aged individuals to evaluate whether
an adaptation of an existing habit scale (the Self-Report Behavioral Automaticity Index; SRBAI) provided
face-valid items of exercise instigation habit separately from exercise execution habit. Participants were
given descriptions of instigation and execution habit and how they differ, and were asked to indicate
whether modified SRBAI variants developed by the authors captured each of the two concepts.
Participants were told, “With the different question wordings that you just saw in this survey, we were
trying to get at the distinction between deciding to exercise (‘exercise instigation habit’) and the specific
actions a person takes during his/her exercise routine (‘exercise execution habits’). Did this distinction
between exercise instigation and exercise execution seem clear to you as you rated the questions? If
not, can you describe what you thought we were asking instead or why the distinction wasn't clear?”
Most participants (109; 88.6%) reported that the distinction was clear. Of the remaining 14 participants,
9 reported that they did not even notice differences in wording, and 5 that they either disagreed with
the distinction, or did not understand it. Given that the majority of participants understood the
distinction, we were satisfied enough to use the items in the main analysis.
Exercise Instigation Habit Strength. The degree to which participants’ exercise sessions were
instigated without deliberation was measured using the four-item SRBAI (Gardner et al., 2012), as
adapted so that the common item stem (“Exercising is something…”) captured exercise instigation:
“Deciding to exercise is something…”. The four items were otherwise in original form: “…I do
automatically”, “…I do without thinking”, “…I do without having to consciously remember”, “…I start
doing before I realize I’m doing it”: strongly disagree (=1) to strongly agree (=5). Internal consistency was
good (α =0.88 at baseline and at follow-up). Change in instigation habit strength was represented by the
follow-up Instigation SRBAI minus the baseline Instigation SRBAI.
Exercise Execution Habit Strength. The degree to which participants’ exercise sessions were
executed without deliberation was measured using the SRBAI with the following statement stem: “Once
I am exercising, going through the steps of my routine is something…” (“…I do automatically”, “…I do
without thinking”, “…I do without having to consciously remember”, “…I start doing before I realize I’m
doing it”; strongly disagree (=1) to strongly agree (=5)). Internal consistency was good (α=0.85 at
baseline and follow-up). Change in execution habit strength was represented by the follow-up Execution
SRBAI minus the baseline Execution SRBAI.
Patterned Exercise Action. Specific items from existing measures were used to represent patterned
exercise action. Two items from the Exercise Habit Survey (EHS; Tappe & Glanz, 2013) represented
patterned exercise action: “I varied my exercise routine by performing different exercises on different
days” (reverse scored) and “Every day that I exercised, I performed the same exercise(s)” (Never, rarely,
sometimes, often, always). Two items from Grove et al (2014) were also used: “Most of my exercise
sessions follow the same pattern” and “I tend to do the same activities or exercises in each session (not
true for me (=1) to very true for me (=5)). Other items from the EHS and Grove et al.’s (2014) patterned
action subscale were not included as they may not be adequate measures. For example, “I exercise on
the same days each week” and “I exercise for about the same amount of time in each session” could
also be accurate for an individual who has varied exercise routines. Internal consistency of these 4 items
was 0.70, and mean scores on the four items represented the variable.
Exercise Frequency. Exercise frequency was measured via electronic daily diary reports for four
weeks post-baseline; participants would indicate each day whether they had engaged in purposeful
exercise for 20 or more minutes at moderate to vigorous intensity that day (yes/no). Frequency of
discrete exercise sessions were evaluated instead of total physical activity, because exercise sessions
may feasibly be instigated and/or executed habitually, so this provides an opportunity for comparing the
measures in a way that total physical activity duration or intensity across a given time period does not.
Daily diary exercise frequency was constructed as a proportion of applicable days on which the
participant reported having exercised. Participants’ daily diary scores were not calculated if they failed
to complete diaries on at least 75% of the days within a 24-hour window, as electronically verified by
submission time. This allowed inclusion of 96% of the sample (n=119) in final analyses with this variable.
Participants were also asked, at baseline and 1-month follow-up: “How often do you exercise: Never,
rarely (a few times a month), sometimes (1-2 times a week), quite often (3-4 times a week), frequently
(5-6 times a week), or daily?” At follow-up, the participants were asked to refer to the previous month
when answering the question. The follow-up item was used as one of the exercise frequency outcomes
in prospective analyses. Responses to exercise frequency items at baseline and follow-up were used to
create groups representing absolute exercise frequency and change in frequency over the study period.
Analysis Overview
To evaluate whether exercise instigation habit would be distinct from exercise execution habit
(Hypothesis 1), exploratory factor analyses (maximum likelihood extraction with direct oblimin rotation,
to permit inter-factor correlation) of the exercise instigation and exercise execution habit strength items
were conducted with the baseline data. The baseline factor structure was verified with follow-up data.
To evaluate whether exercise instigation strength would be the strongest predictor of future
exercise behavior, followed by execution habit strength and then patterned exercise action (Hypothesis
2)bivariate correlations between the theoretical predictors (exercise instigation and execution habit,
patterned exercise action) and exercise frequency outcomes (daily diary exercise frequency and self-
reported frequency at follow-up) were calculated. To test the relative importance of the predictors,
multiple regressions for the two exercise frequency outcomes were conducted separately, with exercise
instigation habit, exercise execution habit, and patterned exercise action as simultaneous predictors.
These models were tested with and without potentially important control variables (BMI, social-
desirability bias, student/staff status). A minimum sample size of 82 was calculated to yield sufficient
power (minimum of .80) to detect a medium effect size (f-squared=0.15) in a linear regression with
three predictors (calculated using G-Power, version; Faul, Erdfelder, Buchner, & Lang, 2009).
To evaluate whether change in exercise over the period of the study would be most strongly
associated with change in exercise instigation habit strength, followed by change in execution habit
strength and then patterned exercise action (Hypothesis 3), repeated measures ANOVAs were
. Participants were categorized into one of four groups: those who exercise infrequently (i.e.
<3 times per week) at baseline and remain infrequent exercisers at follow-up (low baseline frequency,
low follow-up frequency [low/low], no change); those who changed from infrequent to frequent
We also ran regressions to predict difference/change scores and follow-up scores when controlling for baseline
scores, and the same pattern of results was found for all analyses. The results obtained here are not therefore
merely a product of the analysis method. We report this analysis because it is statistically most coherent.
exercise (i.e., 3 times per week) over the month (low/high, increased frequency); those who changed
from high to low frequency (high/low, decreased frequency); and those who report high frequency of
exercise at both time points (high/high, no change). Outcomes were participants’ instigation habit
strength, execution habit strength, and patterned exercise action. We expected change in instigation
habit strength corresponding to frequency group (i.e. low instigation habit strength and no change for
the low/low group; high levels of instigation habit strength and no change for the high/high group;
increased instigation habit for the low/high group; and decreased instigation habit for the high/low
group). We did not expect execution habit strength or patterned exercise action from baseline to follow-
up to be associated with exercise frequency groups in this way.
Although dichotomization of a continuous variable (exercise frequency) can reduce statistical power
(Phillips, 2013), this analysis allows a test of the hypothesis without utilizing difference scores between
time points for the predictor and the outcome. Difference scores can be statistically unreliable (but see
Gollwitzer, Christ, & Lemmer, 2014), and do not allow evaluation of the effect of absolute levels of the
variables (Phillips, 2013). In the current study, we expected different levels of instigation habit strength,
for the four groups created to represent change and level of exercise frequency.
Regression assumptions were met. Mahalanobis distance values indicated no multivariate outliers on
the tested study variables (i.e., distances larger than expected by chance in 1/1000 cases, i.e. p<0.001;
see Tabachnik & Fidell, 2007). All variables were normally distributed around variable means.
Excluding six participants who not complete the follow-up survey, the final analytic sample was
n=118. All analyses were conducted on available data with no imputations for missing data, except for
the daily diary measure. Pairwise deletion was utilized, and so sample sizes ranged from 108 to 120.
Hypothesis 1. Factor analysis yielded two latent factors in both the baseline and follow-up data;
factors corresponded to the instigation items and the execution items, respectively. In the baseline data
only, one item (Deciding to exercise is something I do without remembering”) cross-loaded on both
factors but was more strongly loaded with the other instigation items (pattern matrix loading 0.54) than
with the execution items (0.31; see Table 1). At follow-up, there were no items that cross-loaded on
factors (with loadings >0.29). The two factors were strongly correlated (baseline: r(119)=0.68; follow-up:
r(116)=0.71). The correlation between the two subscales created from the items was r(119)=0.67,
p<.001 (baseline), and r(116)=0.64, p<.001 (follow-up). Hypothesis 1 was supported.
Hypothesis 2. Exercise instigation habit strength and exercise execution habit strength were each
correlated with both exercise frequency outcomes (see Table 2). Patterned exercise action was not
related to either exercise frequency outcome nor to instigation and execution habit. Only instigation
habit strength significantly predicted exercise frequency at follow-up. When modeling daily diary
frequency, instigation habit strength was the only predictor (β=0.31, t(3,108)=2.56, p=0.01; execution
habit strength: β=0.06, t(3,108)=0.06, p=0.62; patterned exercise action: β=0.16, t(3,108)=1.79, p=0.08).
When modeling self-reported exercise frequency, only instigation habit strength was a predictor
(β=0.39, t(3,107)=3.16, p=0.002; execution habit strength: β=0.05, t(3,107)=0.44, p=0.66; patterned
exercise action: β=0.06, t(3,107)=0.69, p=0.49). No meaningful differences were found between
predictors and outcomes when controlling for BMI, social-desirability bias, and student/staff status, nor
were these variables significantly related to either outcome. Hypothesis 2 was supported.
Hypothesis 3. The number of individuals falling into the four exercise frequency groups was: low
baseline frequency, low follow-up frequency (n= 48); low/high (n=13); high/low (n=11); and high/high
(n=37). Repeated measures ANOVAs showed differences between frequency groups on change and level
of instigation habit strength, as the frequency group x time interaction was significant (F(3, 107)=2.85,
p=0.04, ƞp2=0.08, but no differences in change and level of execution habit strength (F(3, 108)=0.02,
p=0.99, ƞp2=0.001) nor patterned exercise action (F(3, 107)=0.25, p=0.86, ƞp2<0.01). Relationships
between groups for instigation habit strength as the outcome were as expected: the low/low frequency
(no change) group showed low instigation habit strength and no change in instigation habit strength
over the month of the study; the high/high frequency (no change) group showed high instigation habit
strength over the month; the high/low frequency group showed a decrease and the low/high frequency
group showed an increase in instigation habit strength (see Figure 1). Only the simple slope (i.e. the
change in instigation habit strength) of the high/low frequency group was statistically significant
(slope=-0.46, p=0.02). The positive slope for the low/high group was not significant (slope=0.21, p=0.22).
As Figure 1 shows, there were no relationships between exercise frequency change and execution
habit strength change, and between exercise frequency change and patterned exercise action change.
This study evaluated whether the theorized distinction between exercise instigation habit and
exercise execution habit could be empirically observed, and estimated the relative importance of the
two concepts for predicting exercise frequency. Instigation and execution habit were found to be
underpinned by separate latent factors. Further, both scales positively and prospectively predicted
exercise frequency, assessed via daily diary and retrospectively at 1-month follow-up; however, in
multivariate analyses, only instigation habit strength was a significant predictor of exercise frequency.
Results also showed that patterned exercise-action was distinct from execution habit strength and did
not predict exercise behavior. Change in frequency over one month was associated with change in
instigation habit, but not change in execution habit nor in patterned action.
Results suggest that instigation habits are empirically discernable from execution habits in the
exercise domain. Findings support Verplanken and Melkevic’s (2008) proposition that deciding to
exercise, or instigating exercise, is the mechanism underpinning commonly observed habit-exercise
behavior associations (see Gardner et al., 2011). Further, exercise habit need not be scripted or
automated, once the “goal” or decision to exercise is triggered (as posited by Aarts et al., 1997). Our
instigation and execution habit items were adapted from the Self-Report Habit Index, which is the most
commonly used habit measure (Gardner, 2014), but in its original form (e.g. Exercise is something I do
automatically”) does not distinguish between instigation and execution. Our findings do not reveal
whether previously observed associations between exercise habit and behavior reflect instigation or
execution habit. Empirical assessment of whether non-specific measures of habit better represent
instigation or execution would help researchers interpret more precisely the role(s) played by habit in
previous studies. For example, if non-specific measures of habit are found to measure execution habit,
or a combination of instigation and execution habits, then previously observed exercise habit-frequency
relationships may have underestimated the true relationship between individuals’ exercise habit
strength and behavioral frequency. Moreover, work is needed to replicate the present findings in other
behavioral domains. We expect that the distinction will be less relevant, or less observable, for simpler
actions characterized by fewer sub-actions (e.g. pulling a light switch, taking medication), for which
initiating is less easily separable from executing the sequence. For complex behaviors such as exercise,
however, we believe the distinction is important. Empirical evidence is required to evaluate the relative
importance of instigation and execution habit in other complex behaviors, such as dietary intake.
Patterned exercise action was unrelated to execution habit and exercise outcomes. This may be due
to the measures used. Following Grove et al (2014) and Tappe & Glanz (2013), items assessed whether
exactly the same actions are done every time a participant exercises. If patterned action is defined more
broadly, however, to recognize that people can have more than one exercise routine, and may engage in
patterned action each time one of their routines is ‘chosen’, then patterned action may correlate more
strongly with execution habit. Even if patterned action is shown to be equivalent to exercise execution
habit strength, however, our results suggest having set routine(s) and execution habits does not predict
exercise maintenance. Inclusion of patterned action in existing habit measures may be unfounded.
Our results have important implications for intervention design and evaluation. For behaviors that
have separable instigation and execution components, interventions may differentially target these
habits. Our findings call for greater specification of the intended outcomes of purported habit-formation
interventions; that is, are participants being encouraged to learn to habitually select one health behavior
over alternative options, or to automate rigid sequences of health-promoting actions? Our results
suggest that the former habit type (instigation habit) may better promote frequent action, at least in the
exercise domain. While further experimental studies or intervention trials are needed to empirically
establish the extent to which modifying instigation habits changes behavior frequency, our findings
tentatively suggest that setting cues for exercise performance instigation, but not necessarily
encouraging adherence to an exercise routine, may be optimal. This resolves a tension in the habit
literature, in that it appears possible to promote exercise habit formation in a manner conducive to
behavior maintenance while also allowing individuals to vary the exercise activities they perform, so
preventing boredom that may arise from repeating the same routine (Gardner, Lally & Wardle, 2012;
Verplanken & Melkevik, 2008). This need not mean that patterned exercise action is unhelpful for
initiating exercise; reducing the complexity of exercise may make people more likely to intend to
exercise and more confident to exercise, which are known determinants of behavior repetition and so
habit formation (Bandura, 1986; Lally & Gardner, 2013). Patterned execution may therefore foster
development of instigation habits. Alternatively, it may be that focusing on instigation habit but
promoting non-habitual execution of the behavior to avoid boredom may better maintain intrinsic
motivation for the behavior, which has been shown to predict generic exercise habit strength (Gardner
& Lally, 2013). Exploring the effect of execution habit on enjoyment could inform interventions designed
to optimize maintenance of healthy behaviors through habitual and non-habitual means. Longitudinal
studies of individuals new to a behavior, and evaluation of change in both types of habit strength, may
address these questions.
Limitations of this study must be acknowledged. Whereas change in frequency was more closely
associated with change in instigation habit strength than in execution habit strength or patterned
exercise action, the trend of increasing instigation habit strength with increasing exercise frequency was
not significant, as the simple slope for instigation habit change over time for the low-to-high frequency
group was no greater than zero. The size of this frequency group likely reduced power to detect an
effect. The percentage of individuals who fell into each frequency-change group in this study may be
used to determine a sample size large enough to achieve sufficient power in future analyses. Further,
since we were analyzing change in habit as a cause of change in frequency, our two-panel data is
equivalent to a cross-sectional view of change, so instigation habit change could be argued to cause
exercise frequency change instead, or a bidirectional relationship may exist between the two variables.
Three time points of data would allow investigation of cross-lagged change in both variables, and
detection of whether and to what extent habit change precedes behavior change, and vice versa.
Notably, however, using regression as an alternative (albeit statistically suboptimal) method of probing
differences in exercise frequency on habit strength, based either on modeling change in frequency over
the study period, or follow-up frequency when controlling for baseline frequency, instigation habit
emerged as a significant predictor and execution habit did not, thus corroborating our main findings.
The lack of a relationship between execution habit and exercise frequency in multivariate analyses
may be a methodological artifact, as execution items were less compatible with frequency scores than
were instigation items. The compatibility principle proposes that items that specify the same behavioral
target are likely to correlate more strongly (and truly; Ajzen, 1988). Instigation items referred to
‘deciding to exercise’, which corresponded more closely with the exercise frequency measure than did
execution items, which referred to ‘going through the steps of my routine’. However, bivariate
relationships between execution habit strength and exercise frequency were significant, and the
execution habit items loaded on a separate factor from the instigation habit items, which provides some
confidence in the validity of the execution habit items relative to the instigation habit items. Similarly,
habit items related to ‘exercising’ and so were not directly compatible with behavior frequency
measures, which referred to ‘purposeful exercise’. Behavior wording was chosen to differentiate
exercise from instrumental physical activity, and habit wording to minimize ambiguity that may have
arisen from framing the behavior as ‘deciding to exercise purposefully’. Nonetheless, it is possible that
incompatibility may have reduced the magnitude of observed habit-behavior relationships. Also, we
measured exercise as frequency, rather than duration or intensity, which may have inflated instigation
habit-behavior effects, or suppressed execution habit-behavior effects. Findings require replication
using alternative exercise and habit measures. However, we have no reason to expect execution habit to
correlate more closely with other exercise measures: two people could, for example, have identical
execution habits, yet one may do each sub-action within their routine for longer or at a higher intensity.
Conversely, two people could perform the same routine for the same duration and intensity but rate
their execution habits differently, one performing the routine mindfully and the other automatically.
Moreover, both habit types were measured by self-report. Concerns have been raised around the
validity of assessing habit via self-report; people may have little insight into unconscious processes,
which can limit the accuracy of self-reported habit strength (Hagger et al., 2015). Some have argued that
habit can be inferred from its consequences (e.g. ‘I can’t remember actively deciding to go to the gym,
yet I am now walking towards the gym, so I must have made the decision habitually’), thus providing an
adequate proxy measure of habit (e.g. Gardner, 2014; Sniehotta & Presseau, 2012), but such inferences
can lack precision (Hagger et al, 2015; but see Orbell & Verplanken, 2015). This problem may have been
compounded in our study. People often fail to recognize actions within a higher-level behavior sequence
as discrete, instead perceptually organizing them into a single unit (e.g. ‘putting on sneakers’ and
‘leaving the house’ are clustered together as part of ‘going for a run’; see Vallacher & Wegner, 1987). It
is therefore possible that respondents may have less insight into the role of habit within a sequence
(execution habit) than at the outset of the sequence (instigation habit). Alternatively, given research
indicating that people can be environmentally cued to behave in certain ways without awareness of the
cues (Nisbett & Wilson, 1977), it may be that people have less insight into habitual activation of a
sequence (instigation habit) than enactment (execution habit).
More fundamentally, respondents often misinterpret self-report habit items (Gardner & Tang, 2014).
This may have been exacerbated here, given the subtlety of the distinction between instigation and
execution, and ostensibly similar instigation and execution item wording. Instigation habit item stems in
particular may have been misinterpreted. These were intended to capture unconsciously ‘choosingto
engage in exercise in response to conditioned contextual cues. Items were worded in relation to
“deciding” instead of “instigating” to articulate, in lay terms, the instigation habit concept and its
distinction from execution. It is possible, however, that respondents interpreted these items to refer to
deciding to exercise at a later time (e.g. deciding before going to work to exercise after work).
Instigation habit refers to the activation of behavior immediately prior to its execution, and is not
synonymous with habitual planning. Had respondents misinterpreted instigation items however, the
relationship between instigation habit and frequency would likely have been attenuated, due to the
possibility of internal or external forces (e.g., preference reversals, structural barriers; Tversky & Thaler,
1990) obstructing translation into action. Moderate-to-strong correlations between instigation habit and
exercise frequency suggest this was not the case. Indeed, pilot study data suggested most participants
recognized the distinction between habit types and items. Nonetheless, empirical validation is needed of
instigation and execution habit items against more objective measures of decision-making automaticity
and procedural automaticity respectively (Keatley, Chan, Caudwell, Chatzisarantis & Hagger, 2015).
While self-report measures may be imperfect for tapping habit (Hagger et al, 2015), one means of
improving them is to better specify the concept being measured, as we have done here. The current
study demonstrates that exercise behavior has separable components that can develop into habits and
predict behavior differentially. Specifically, the extent to which individuals habitually decide to engage in
exercise, rather than the automaticity of their exercise routines, better predicts exercise frequency.
Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-directed
behavior. Journal of Personality and Social Psychology, 78, 53-63. DOI: 10.1037//0022-3514.78.1.53.
Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: On the conceptualization and
formation of habitual health behaviors. Health Education Research, 12(3), 363-374. DOI:
Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Dorsey Press.
Artinian, N. T., Fletcher, G. F., Mozaffarian, D., …Burke, L. E. (2010). Interventions to promote physical
activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: A scientific
statement from the American Heart Association. Circulation, 122, 406-441. DOI:
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood
Cliffs, NJ: Prentice-Hall.
Bargh, J. (1989). Conditional automaticity: Varieties of automatic influence in social perception and
cognition. In J. S. Uleman & J. A. Bargh (Eds.) Unintended thought (pp. 3-51). New York: Guilford
Bargh, J. (1992). The ecology of automaticity. Toward establishing the conditions needed to produce
automatic processing effects. American Journal of Psychology, 105, 181-199. DOI: 10.2307/1423027.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1:
Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160. DOI:
Gardner, B. (2014). A review and analysis of the use of ‘habit’ in understanding, predicting and
influencing health-related behaviour. Health Psychology Review, DOI:
Gardner, B. (2015) Defining and measuring the habit impulse: Response to commentaries. Health
Psychology Review, DOI: 10.1080/17437199.2015.1009844.
Gardner, B., Abraham, C., Lally, P., & de Bruijn, G. (2012). Towards parsimony in habit measurement:
Testing convergent and predictive validity of an automaticity subscale of the Self-Report Habit Index.
International Journal of Behavioral Nutrition and Physical Activity, 9, 102-114. DOI: 10.1186/1479-
Gardner, B., de Bruijn, G. J., & Lally, P. (2011). A systematic review and meta-analysis of applications of
the Self-Report Habit Index to nutrition and physical activity behaviors. Annals of Behavioral
Medicine, 42(2), 174-187. DOI: 10.1007/s12160-011-9282-0.
Gardner, B., Lally, P., & Wardle, J. (2012). Making health habitual: The psychology of ‘habit-formation’
and general practice. The British Journal of General Practice, 62(605), 664-666. DOI:
Gardner, B., & Tang, V. (2014). Reflecting on non-reflective action: An exploratory think-aloud study of
self-report habit measures. British Journal of Health Psychology, 19(2), 258-273. DOI:
Gollwitzer, M., Christ, O., & Lemmer, G. (2014). Individual differences make a difference: On the use and
the psychometric properties of difference scores in social psychology, European Journal of Social
Psychology, 44, 673-682. DOI: 10.1002/ejsp.2042.
Grove, J. R., Zillich, I., & Medic, N. (2014). A process-oriented measure of habit strength for moderate-
to-vigorous physical activity. Health Psychology & Behavioural Medicine, 2(1), 379-389. DOI:
Hagger, M. S., Rebar, A. L., Mullan, B., Lipp, O. V., & Chatzisarantis, N. L. (2014). The subjective
experience of habit captured by self-report indexes may lead to inaccuracies in the measurement of
habitual action. Health Psychology Review, DOI: 10.1080/17437199.2014.959728.
Kaushal, N., & Rhodes, R.E. (2015) Exercise habit formation in new gym members: a longitudinal study.
Journal of Behavioral Medicine, DOI: 10.1007/s10865-015-9640-7.
Keatley, D.A., Chan, D.K.C., Caudwell, K., Chatzisarantis, N.L.D., & Hagger, M.S. (2015). A consideration of
what is meant by automaticity and better ways to measure it. Frontiers in Psychology, 5, 1537. DOI:
Lally, P., & Gardner, B. (2013). Promoting habit formation. Health Psychology Review, 7(S1), S137-S158.
DOI: 10.1080/17437199.2011.603640.
Lally, P., van Jaarsveld, C., Potts, H., & Wardle, J. (2010). How are habits formed: Modelling habit
formation in the real world. European Journal of Social Psychology, 40(6), 998-1009. DOI:
Lally, P., Wardle, J., & Gardner, B. (2011). Experiences of habit formation: A qualitative study.
Psychology, Health, & Medicine, 16(4), 484-489. DOI: 10.1080/13548506.2011.555774.
Maddux, J. E. (1997). Habit, health, and happiness. Journal of Sport & Exercise Psychology, 19(4), 331-
Marcus, B. H., Forsyth, L. H., Stone, E. J., Dubbert, P. M., McKenzie, T. L., Dunn, A. L., & Blair, S. N. (2000).
Physical activity behavior change: Issues in adoption and maintenance. Health Psychology, 19(S1), 32-
Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological
Bulletin, 132(2), 297-326. DOI: 10.1037/0033-2909.132.2.297.
Orbell, S., & Verplanken, B. (2010). The automatic component of habit in health behavior: Habit as cue-
contingent automaticity. Health Psychology, 29(4), 374-383. DOI: 10.1037/a0019596.
Orbell, S., & Verplanken, B. (2015). The strength of habit. Health Psychology Review, DOI:
Policy Statement (AAP): The Built Environment: Designing Communities to Promote Active Children.
Pediatrics. 2009; 123(6):15911598. Reaffirmed January 2013.
Rhodes, R., de Bruijn, G. J., & Matheson, D. H. (2010). Habit in the physical activity domain: Integration
with intention temporal stability and action control. Journal of Sport and Exercise Psychology, 32(1),
Rothman, A. (2000). Toward a theory-based analysis of behavioral maintenance. Health Psychology,
19(1), 64-69. DOI: 10.1037/0278-6133.19.Suppl1.64.
Rothman, A. J., Sheeran, P., & Wood, W. (2009). Reflective and automatic processes in the initiation and
maintenance of dietary change. Annals of Behavioral Medicine, 38(S1), S4-S17. DOI: 10.1007/s12160-
Schneider, W., & Chein, J. M. (2003). Controlled & automatic processing: Behavior, theory, and biological
mechanisms. Cognitive Science, 27, 525-559. DOI: 10.1016/S0364-0213(03)00011-9
Sniehotta, F. F., & Presseau, J. (2012). The habitual use of the self-report habit index. Annals of
Behavioral Medicine, 43, 139140. DOI: 10.1007/s12160-011-9305-x.
Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. Boston: Allyn & Bacon.
Tappe, K. A., & Glanz, K. (2013). Measurement of exercise habits and prediction of leisure-time activity
in established exercise. Psychology, Health, & Medicine, 18(5), 601-611. DOI:
Tremblay, M. S., Esliger, D. W., Tremblay, A., & Colley, R. (2007). Incidental movement, lifestyle-
embedded activity and sleep: New frontiers in physical activity assessment. Applied Physiology,
Nutrition, and Metabolism, 32(S2E), S208-S217. DOI: 10.1139/H07-130
Tversky, A., & Thaler, R. H. (1990). Anomalies: Preference reversals. Journal of Economic Perspectives,
4(2), 201-211. DOI: 10.1257/jep.4.2.201
Vallacher, R.R., & Wegner, D.M. (1987) What do people think they’re doing? Action identification and
human behavior. Psychological Review, 94, 3-15.
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of Social Psychology,
45, 639-656. DOI: 10.1348/014466605X49122.
Verplanken, B., Aarts, H., & van Knippenberg. (1997). Habit, information acquisition, and the process of
making travel mode choices. European Journal of Social Psychology, 27, 539-560.
Verplanken, B., & Melkevik, O. (2008). Predicting habit: The case of physical exercise. Psychology of
Sport and Exercise, 9, 15-26. DOI: 10.1016/j.psychsport.2007.01.002.
Verplanken, B. & Orbell, S. (2003) Reflections on past behavior: A self-report index of habit strength.
Journal of Applied Social Psychology, 33(6), 1313-1330. DOI: 10.1111/j.1559-1816.2003.tb01951.x.
Wood, W., & Neal, D. T. (2007). A new look at habits and the habit-goal interface. Psychological Review,
114(4), 843-863. DOI: 10.1037/0033-295X.114.4.843.
Wood, E., Tam, L., & Witt, M. G. (2005). Changing circumstances, disrupting habits. Journal of
Personality and Social Psychology, 88, 918-933. DOI: 10.1037/0022-3514.88.6.918.
Figure 1.
Results of the repeated measures ANOVA for test of Hypothesis 3. The interaction between time
and exercise frequency groups in predicting the outcome was only significant for instigation
habit strength as the outcome. The exercise frequency groups are identified in the legend: “Low
T1-Low T2 Frequency” represents those who reported low frequency exercise at both baseline
and at follow-up, etc.
Table 1.
Item-factor loadings (pattern matrix) for instigation habit strength and execution habit strength items,
from baseline data and follow-up data. Items are listed in the order from greatest to lowest factor loading
in the baseline analysis.
Baseline Data
Factor 1
Factor 2
Factor 1
Factor 2
Instigation Habit Strength:
“Deciding to exercise is something…”
“…I do without thinking”
“…I do automatically”
“…I start doing before I realize I’m doing it”
“…I do without having to consciously remember”
Execution Habit Strength:
“Once I am exercising, going through the steps of my
routine is something…”
“…I do without thinking”
“…I start doing before I realize I’m doing it”
“…I do automatically”
“…I do without having to consciously remember”
Table 2.
Descriptive statistics and correlations among study variables, with scale internal consistency alpha
values in diagonal cells, where applicable.
Mean (SD)
Instigation Habit Strength, T1
2.84 (0.99)
Execution Habit Strength, T1
3.28 (0.87)
Patterned Exercise Action, T1
3.21 (0.77)
Instigation Habit Strength, T2
2.83 (0.98)
Execution Habit Strength, T2
3.24 (0.92)
Patterned Exercise Action, T2
3.43 (0.78)
Exercise Frequency, Self-
Report, T1
3.02 (1.16)
Exercise Frequency, Self-
Report, T2
2.92 (1.13)
Exercise Frequency, Daily
Diary Measure
0.61 (0.24)
Note. Mean value for exercise frequency from the daily diary measure is a proportion of days on which
individuals reported exercising out of the total days they filled out daily diaries. Variables 1-4 had a
possible range of 1 to 5. All correlations were calculated with pairwise deletion, and sample sizes ranged
from 108 to 120; correlations of magnitude 0.22 or greater were statistically significant at p<0.05;
correlations of magnitude 0.28 or greater were statistically significant at p<0.01.
Time 1 Time 2
Execution Habit Strength
... Responders were those who implemented both selected goals consistently, while non-responders implemented at least one measure inconsistently. The BCT directed at responders was habit formation to develop a habit of implementing hygienic measures without consciously remembering (Phillips & Gardner, 2016). Participants were encouraged to identify situational, contextual, or time-based cues that could prompt them to implement the chosen hygienic, safe food-handling measures. ...
... It is possible to succinctly deliver this advice to participants, and interventions using this technique have shown positive changes in behaviour [56]. Distinction has been made between the habit of initiating a behaviour (i.e. an instigation habit) and that of performing it (i.e. an execution habit), and it is the instigation habit that predicts behaviour maintenance [57][58][59]. The advice given in the intervention leaflet and phone calls therefore recommend participants focus on forming instigation habits for doing physical activity, while gradually increasing the amount or intensity of activity they do on each occasion. ...
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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.
... Responders were those who implemented both selected goals consistently, while nonresponders implemented at least one measure inconsistently. The BCT directed at responders was habit formation to develop a habit of implementing hygienic measures without consciously remembering [46]. Participants were encouraged to identify situational, contextual, or time-based cues that could prompt them to implement the chosen hygienic, safe food-handling measures. ...
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Antimicrobial-resistant (AMR) bacteria spread via food to humans and can seriously impair infection treatment. Hygienic food handling is an effective measure to avoid the transmission of bacteria. Therefore, we tested three types of interventions (tailored, generic, and no intervention) for their effectiveness in improving consumers' hygienic food handling against the spread of anti-microbial-resistant bacteria through foods in a longitudinal randomized control trial. We based the determinants of hygienic food-handling behavior on the Health Action Process Approach (HAPA). The tailored intervention raised self-reported hygienic food handling, self-efficacy, and perceived likelihood of risk compared to no intervention. Moreover, interventions yielded different effects for participants with high vs. low intentions to improve their food-handling behavior. However, there were no differences between the tailored and generic interventions. More research is needed to find out whether including other behavior change techniques in the tailored intervention may increase the effect of tailoring.
... Self-monitoring behaviors are thought to diminish at the end of an intervention [77] while goal setting strategies are considered more durable [78]. Once HPFIs had been discontinued, some sustained users may have stopped tracking because of a loss of so-called habitual exercise instigation [79]. This means that bidirectional tracking and physical activity behaviors were still contingent on HPFIs. ...
Background The effectiveness of mobile health (mHealth) approaches that employ wearable technology to promote physical activity have been the subject of concern due to the declining active use observed in trial settings. Objective To better contextualize active use, this study aimed to identify the barriers and enablers to engagement in a tracker-based mHealth initiative among young men who had recently completed a 19-week residential weight loss program. Methods A mixed methods study was conducted among 167 young men who had voluntarily enrolled in the national steps challenge (NSC), an mHealth physical activity promotion initiative, following a residential weight loss intervention. A subsample of 29 enrollees with a body mass index of 29.6 (SD 3.1) participated in semistructured interviews and additional follow-up assessments. Quantitative systems data on daily step count rates were used to describe active use. Qualitative data were coded and analyzed to elicit barriers and enablers to microlevel engagement in relation to the NSC, focusing on tracker and smartphone use. We further elicited barriers and enablers to macrolevel engagement by exploring attitudes and behaviors toward the NSC. Using triangulation, we examined how qualitative engagement in the NSC could account for quantitative findings on active use. Using integration of findings, we discussed how the mHealth intervention might have changed physical activity behavior. Results Among the 167 original enrollees, active use declined from 72 (47%) in week 1 to 27 (17%) in week 21. Mean daily step counts peaked in week 1 at 10,576 steps per day and were variable throughout the NSC. Barriers to engagement had occurred in the form of technical issues leading to abandonment, device switching, and offline tracking. Passive attitudes toward step counting and disinterest in the rewards had also prevented deeper engagement. Enablers of engagement included self-monitoring and coaching features, while system targets and the implicit prospect of reward had fostered new physical activity behaviors. Conclusions Our study showed that as the NSC is implemented in this population, more emphasis should be placed on technical support and personalized activity targets to promote lasting behavior change.
... While instigation habits reflect the habit when deciding to perform certain behaviors, execution habits reflect the habit when actually beginning to perform certain behaviors [68]. Phillips and Gardner [69] reported that instigation habits significantly predicted exercise behavior, but that execution habits did not. The lack of careful discrimination of these two types of habit strength in the present study might have caused their contamination when answering the survey, leading to inconsistent results on the relationship between habit strength and exercise behavior. ...
Background Although self-regulation interventions are effective in promoting exercise behaviors, moderators and mediators of interventions among older adults are not well established. This study aimed to examine whether (1) self-regulation intervention promoted exercise behavior, (2) health literacy and habit strength moderated the intervention effect, and (3) self-regulation and habit strength mediated the intervention effect among older adults.Methods This study was a randomized, non-blinded, controlled crossover trial. The baseline questionnaire survey assessed the average amount of exercise time per day, self-regulation, habit strength, health literacy, and socio-demographic factors. After the baseline survey, 393 community-dwelling older adults were randomly assigned to either the immediate intervention or the delayed intervention group. For the immediate group, print-based materials were provided once a week for 7 weeks before a second questionnaire survey. For the delayed group, the materials were provided only after the second survey. Finally, a third survey was conducted for both groups.ResultsThe mixed models showed that the average exercise time was increased after the intervention in both groups. Multiple regression analyses revealed that no factor moderated the intervention effect. From the path analyses, the mediating effect of self-regulation on the relationship between intervention and changes in average exercise time was supported, but the mediating role of habit strength was not clearly indicated.Conclusions Although the mediating roles of habit strength for the intervention effects are still inconclusive, self-regulation intervention can promote exercise behavior among older adults, regardless of their health literacy level, habit strength, and socio-demographic characteristics.
... Like all layers of the M-PAC pyramid, however, habit and identity are proposed to arise as a natural consequence of repeated successful behavioral outcomes (see snaking arrow, Figure 1) and can be intervened upon through specific external behavior change techniques (e.g., associations, repetition, identity formation strategies; see left aligned box in Figure 1). Action control habits are based on the premise that complex behaviors, such as PA, do not comprise of an all-or-nothing habit response, but instead assist in automating certain sub-components of a larger behavioral sequence Phillips and Gardner, 2016;Rhodes and Rebar, 2018;Gardner et al., 2020). Habit in M-PAC assists primarily as a form of selection bias toward intended action (see for a detailed review). ...
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The gap between the decision to engage in physical activity and subsequent behavioral enactment is considerable for many. Action control theories focus on this discordance in an attempt to improve the translation of intention into behavior. The purpose of this mini-review was to overview one of these approaches, the multi-process action control (M-PAC) framework, which has evolved from a collection of previous works. The main concepts and operational structure of M-PAC was overviewed followed by applications of the framework in physical activity, and concluded with unanswered questions, limitations, and possibilities for future research. In M-PAC, it is suggested that three layered processes (reflective, regulatory, reflexive) build upon each other from the formation of an intention to a sustained profile of physical activity action control. Intention-behavior discordance is because of strategic challenges in goal pursuit (differences in outcome vs. behavioral goals; balancing multiple behavioral goals) and automatic tendencies (approach-avoidance, conservation of energy expenditure). Regulatory processes (prospective and reactive tactics) are employed to hold the relationship between reflective processes and behavior concordant by countering these strategic challenges and automatic tendencies until the development of reflexive processes (habit, identity) begin to co-determine action control. Results from 29 observational and preliminary experimental studies generally support the proposed M-PAC framework. Future research is needed to explore the temporal dynamic between reflexive and regulatory constructs, and implement M-PAC interventions in different forms (e.g., mobile health), and at different levels of scale (clinical, group, population).
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Behavioural theories, predictions, and interventions should be relevant to complex, real-world health behaviours and conditions. Habit theory and habit formation interventions show promise for predicting and promoting, respectively, longer-term behaviour change and maintenance than has been attained with theories and interventions focused only on deliberative behavioural factors. However, the concept of habit has largely been treated as uniform across different types of behaviours. In this conceptual review, we contend that the definitional aspects of habit differ at a conceptual level for simple versus more complex behaviours, with ramifications for prediction, promotion, and measurement of habits. Specifically, habits are defined as direct context-response associations learned through repeatedly rewarded responding-but what is meant by "response" and "reward" depends upon the complexity of the behaviour. We review literature that suggests (1) responses in complex habits have separable and substitutable components (vs a single and static, unitary component) and (2) rewards for complex habits are necessarily continued and intrinsic (vs temporary and extrinsic, respectively). We discuss some empirical and theoretical questions raised by these issues around behavioural complexity and habit. Lastly, we outline the ramifications of these issues for habit measurement (habit strength and habit formation) via self-report and objective measures.
How do habit and skill relate to one another? Among many traditions of habit research, we suggest that 'slip-of-action' habits are the type most likely to relate to motor skill. Habits are traditionally thought of as a property of behavior as a whole. We suggest, however, that habits are better understood at the level of intermediate computations and, at this level, habits can be considered to be equivalent to the phenomenon of automaticity in skill learning – improving speed of performance at the cost of flexibility. We also consider the importance of habits in learning complex tasks given limited cognitive resources, and suggest that deliberate practice can be viewed as an iterative process of breaking and restructuring habits to improve performance.
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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.
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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.
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Existing models of exercise behavior are insufficient in predicting outcomes, this point is shown by the relatively high levels of unexplained variance in exercise behavior in meta-analyses of social cognitive theories and models (Chatzisarantis et al., 2003; Hagger and Chatzisarantis, 2009). Researchers are beginning to recognize the importance of implicit, automatic processes in the prediction of health behaviors (Dimmock and Banting, 2009; Keatley et al., 2012, 2013b). The research by de Bruijn et al. (2014) is useful for highlighting the importance of automaticity in exercise behavior. We commend the authors on investigating an important approach to automaticity and exercise behavior. There were, however, some points with which we disagree. We think that the authors do not provide a clear account of what they mean by automaticity–an issue that is essential for the operationalization of the construct. Bargh (1994), for instance, suggested automaticity has four characteristics: awareness, intention, efficiency, and control; it is not clear whether de Bruijn and colleagues automaticity adheres to this. In particular, we contend that the explicit measure of automaticity used in their research is not an optimal way to assess implicit, impulsive processes. Furthermore, we contend that implicit measures, such as the implicit association test (IAT; Greenwald et al., 1998) would be better positioned as measures of non-conscious processes. The present commentary focuses on pre-behavior automatic associations, which we contend are better assessed by existing implicit measures, rather than during-behavior automatic “processes.”
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Purpose: Habitual action is an important aspect of health behaviour, but the relevance of various habit strength indicators continues to be debated. This study focused specifically on moderate-to-vigorous physical activity (MVPA) and evaluated the construct validity of a framework emphasizing patterned action, stimulus-response bonding, automaticity, and negative consequences for nonperformance as indicators of habit strength for this form of exercise. Methods: Upper-level undergraduates (N = 124) provided demographic information and responded to questionnaire items assessing historical MVPA involvement, current MVPA involvement, and the four proposed habit strength dimensions. Factor analyses were used to examine the latent structure of the habit strength indicators, and the model’s construct validity was evaluated via an examination of relationships with repetition history and current behaviour. Results: At a measurement level, findings indicated that the proposed four-component model possessed psychometric integrity as a coherent set of factors. Criterion-related validity was also demonstrated via significant changes in three of the four factors as a function of past involvement in MVPA and significant correlations with the frequency, duration, and intensity of current MVPA. Conclusions: These findings support the construct validity of this exercise habit strength model and suggest that it could provide a template for future research on how MVPA habits are developed and maintained. [This is an open access publication. It can be viewed at ]
This study rested the idea of habits as a form of goal-directed automatic behavior. Expanding on the idea that habits are mentally represented as associations between goals and actions, it was proposed that goals are capable of activating the habitual action. More specific, when habits are established (e.g., frequent cycling to the university), the very activation of the goal to act (e.g., having to attend lectures at the university) automatically evokes the habitual response (e.g., bicycle). Indeed, it was tested and confirmed that, when behavior is habitual, behavioral responses are activated automatically. in addition, the results of 3 experiments indicated that (a) the automaticity in habits is conditional on the presence of an active goal (cf. goal-dependent automaticity; J. A. Bargh, 1989), supporting the idea that habits are mentally represented as goal-action links, and (b) the formation of implementation intentions (i.e., the creation of a strong mental link between a goal and action) may simulate goal-directed automaticity in habits.
Much social psychological research is concerned with the question whether and how behavior changes because of a “treatment” (e.g., a situation that triggers a psychological reaction). One easy way to investigate such changes would be to analyze intraindividual differences before (Time 1) and after the treatment (Time 2). Interestingly, many scholars refrain from using difference scores because they think they are inherently unreliable. However, the bad reputation of difference scores is, in many cases, unwarranted: difference scores can be sufficiently reliable when standard deviations differ between measurement occasions, and standard deviations are likely to differ between measurement occasions because of differential treatment effects (i.e., interindividual differences in responsiveness to a treatment) and/or “strong situation” treatments. In the present article, we will (1) summarize classic and current arguments regarding the reliability of difference scores, (2) discuss the use of residual change scores as an alternative to difference scores, and (3) argue that latent difference score models are a particularly useful tool that social psychologists should consider using more frequently. Copyright © 2014 John Wiley & Sons, Ltd.