Content uploaded by Benjamin Gardner
Author content
All content in this area was uploaded by Benjamin Gardner
Content may be subject to copyright.
This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted
PDF and full text (HTML) versions will be made available soon.
Towards parsimony in habit measurement: Testing the convergent and
predictive validity of an automaticity subscale of the Self-Report Habit Index
International Journal of Behavioral Nutrition and Physical Activity 2012,
9:102 doi:10.1186/1479-5868-9-102
Benjamin Gardner (b.gardner@ucl.ac.uk)
Charles Abraham (charles.abraham@pcmd.ac.uk)
Phillippa Lally (p.lally@ucl.ac.uk)
Gert-Jan de Bruijn (g.j.debruijn@uva.nl)
ISSN 1479-5868
Article type Methodology
Submission date 20 January 2012
Acceptance date 15 August 2012
Publication date 30 August 2012
Article URL http://www.ijbnpa.org/content/9/1/102
This peer-reviewed article can be downloaded, printed and distributed freely for any purposes (see
copyright notice below).
Articles in IJBNPA are listed in PubMed and archived at PubMed Central.
For information about publishing your research in IJBNPA or any BioMed Central journal, go to
http://www.ijbnpa.org/authors/instructions/
For information about other BioMed Central publications go to
http://www.biomedcentral.com/
International Journal of
Behavioral Nutrition and
Physical Activity
© 2012 Gardner et al. ; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Towards parsimony in habit measurement: Testing
the convergent and predictive validity of an
automaticity subscale of the Self-Report Habit Index
Benjamin Gardner1*
* Corresponding author
Email: b.gardner@ucl.ac.uk
Charles Abraham2
Email: charles.abraham@pcmd.ac.uk
Phillippa Lally1
Email: p.lally@ucl.ac.uk
Gert-Jan de Bruijn3
Email: g.j.debruijn@uva.nl
1 Health Behaviour Research Centre, Department of Epidemiology & Public
Health, University College London, Gower Street, London WC1E 6BT, UK
2 Peninsula College of Medicine & Dentistry, University of Exeter, St. Luke‟s
Campus, Heavitree Road, Exeter EX1 2LU, UK
3 Amsterdam School of Communication Research, University of Amsterdam,
Kloveniersburgwal 48, 1012 CX Amsterdam, The Netherlands
Abstract
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.
Keywords
Habit, Automaticity, Self-report, Measurement, Energy balance related behaviours
Background
Many energy-balance related behaviours (EBRBs; e.g., active travel, unhealthy snacking) are
performed habitually, with little forethought [1]. Habits are defined as behavioural patterns,
based on learned context-behaviour associations, that are elicited automatically upon
encountering associated contexts [2]. Habits are acquired through context-dependent
repetition [3], and, once formed, are hypothesised to have two effects on behaviour. First,
where associated contexts are consistently encountered and remain stable, habit strength will
correlate with behavioural frequency. Second, habit will override motivational determinants
of behaviour so that, as habit strengthens, the relationship between deliberative intentions and
behaviour will weaken. Subsequently, where habits and intentions conflict, behaviour will
tend to proceed in line with habit and not intention [4,5]. These hypotheses have been
empirically well-supported in EBRB determinant studies [4].
There is growing recognition of the importance of habit in EBRB change. Motivation-based
interventions may be insufficient to break established dietary or sedentary habits, because
people tend to behave in line with their habits even when motivated not to do so [6]. Effective
behaviour change may depend on disrupting the cue-response relationships that support
habitual EBRBs. Conversely, establishing habits for health-promoting EBRBs will facilitate
behavioural maintenance, by increasing the likelihood of behaviour persisting even where
motivation diminishes [7,8]. Recent work has sought to model the habit development process
[3,9,10], and EBRB interventions explicitly based on habit formation principles are being
trialled [11].
Assessing the extent to which EBRBs are habitual requires a practical, reliable and
conceptually robust habit measure. The most popular habit measure in the EBRB domain is
the Self-Report Habit Index (SRHI; [4,12]). The SRHI comprises twelve items reflecting on
three proposed characteristics of habit: automaticity (e.g. [„Behaviour X is something…‟]
„…I do without thinking‟), frequency („…I do frequently‟), and relevance to self-identity
(„…that‟s typically “me”‟). The SRHI has been found to detect the habit-behaviour
association and moderation of the intention-behaviour relationship in EBRB domains [4].
Recent findings have, however, questioned the parsimony of the SRHI [4,13-15]. Various
SRHI subscales have been used with no apparent losses in reliability [16-18], suggesting that
some items may be redundant. The SRHI may burden participants unnecessarily, which may
be especially problematic in EBRB research, in instances where a multitude of determinants
are proposed [19], multiple habits are measured (e.g. soft drink consumption and TV
viewing; [20]), or, in longitudinal research, habits are assessed at several timepoints [11]. For
example, in one weight-loss intervention trial, participants completed the 12 SRHI items in
relation to 14 behaviours over three timepoints (504 items in total [11]). Unsurprisingly,
dissatisfaction was expressed with questionnaire length. Such burden can lead to unreliable or
incomplete responses, or withdrawal from the study [21]. Development of a standardised
SRHI subscale may allow more „participant-friendly‟ assessment of energy-balance related
habits.
The conceptual basis of the SRHI has also been questioned. Strong reliability coefficients and
a single factor structure have been interpreted as support for a conceptualisation of habit
based on three identifiable components: automaticity, behavioural frequency and identity
[22]. However, higher numbers of items biases alpha coefficients towards higher values, and
factor analyses may be insensitive to potentially distinct factors on which only one item loads
(e.g. identity). A more robust analysis, in which the SRHI was supplemented by additional
self-identity items, found that the single SRHI identity item loaded on to a separate factor
from all other SRHI items [14], suggesting that identity-relevance is not a necessary
component of a habit.
Moreover, the incorporation of behaviour frequency indicators in the SRHI is problematic
when estimating the relationship between habit and behaviour frequency [4,14,15].
Established habits can only be distinguished from frequent intentional behaviours by
automatic activation [1,23]. Commentators have thus proposed that the effects of habit on
action can be attributed to automaticity [15,24], and that it is because habits are automatically
elicited that habitual behaviour persists in associated contexts, and deliberative tendencies are
overridden [5,25]. According to this viewpoint, repeated performance is an antecedent (and
consequence) of automaticity [3,15,23], and so the contribution of past behaviour to habit
should be reflected by the extent to which behaviours are automatically activated. While
behavioural frequency items may be needed to distinguish habit from automatic actions
which do not develop through repeated performance, this distinction is rarely of interest in
EBRB prediction and habit formation or disruption studies. An automaticity measure may
therefore adequately capture habit in these settings. Frequency items are also problematic
from a practical perspective, because they can incorporate unidentified stable influences on
behaviour [26], and so can inflate habit-behaviour associations [4]. It has been suggested that
frequency indicators may not be needed to detect a moderating effect of habit-related
automaticity on the intention-behaviour relation [4]. Gardner et al. [4] called for an “SRHI
subscale which removes frequency and so may permit a truer estimate of the relationship
between cue-response association strength and behavioural performance” (p185).
The present study
This paper describes work to identify and test a SRHI subscale based on behavioural
automaticity. Identification of an SRHI shortform would have conceptual and practical
benefits for EBRB prediction and habit formation studies. Although automaticity-specific
SRHI subscales have been used to study EBRBs [3,16-18], no attempt has been made to
systematically identify automaticity indicators within the SRHI, and so there has been
disagreement about which items best capture automaticity.
We used content validity procedures to identify SRHI items consensually agreed by a panel
of researchers to capture automaticity. The convergent validity and predictive utility of the
resultant automaticity scale was tested using data from two sources. First, corresponding
authors of published SRHI studies, identified via systematic review, were asked to re-analyse
their findings using the automaticity subset, and these data were meta-analysed where
possible. To maximise data availability, data from all behavioural domains were eligible for
analysis. Second, two new primary datasets were collected. Previous SRHI studies have been
criticised for neglecting contexts in which habit and intention measures conflict [4], and
potential contextual cues to habitual action [15]. To assess the utility of the automaticity
subset in these settings, one new dataset measured habits (for unhealthy snacking) alongside
counterhabitual intentions (to avoid eating unhealthy snacks), and one dataset used habit
items worded to include a potential cue („drinking alcohol with the evening meal‟).
Availability of primary datasets in raw form enabled comparisons between the automaticity
index and a composite of SRHI items removed from the automaticity subset, which we did
not deem feasible to request from authors of published studies. A further two datasets, which
formed the basis of previously published work [16], were also available to us in raw form and
permitted comparisons with an additional habit measure (the transport-specific „Response-
Frequency Habit Measure‟ [27]). Together, the four primary datasets covered both sides of
the energy balance equation: energy expenditure (inactive and active commuting) and intake
(snacking, alcohol consumption [28]).
In all analyses, the automaticity index (which we term the „Self-Report Behavioural
Automaticity Index‟; SRBAI) was assessed against the criteria by which the SRHI has been
tested and become established: reliability, convergent validity, and predictive validity. We
hypothesised that:
Hypothesis 1. (a) The SRHI and SRBAI (and RFM) will be intercorrelated, and (b) will each
correlate with behaviour. However, (because of the removal of items which assess frequency
and identity) (c) the automaticity-specific SRBAI will be less strongly correlated with
behaviour than will the SRHI, or (d) a scale comprised of SRHI items removed from the
SRBAI.
Hypothesis 2. (a) The SRBAI and SRHI will each moderate the relationship between
intentions and behaviour, such that where habit is strong, intentions will have a weaker effect
on behaviour, and vice versa. Assuming that the moderating effect of habit is attributable to
automaticity, then, due to the removal of strong automaticity indicators, (b) a composite of
SRHI items omitted from the automaticity subset will fail to detect moderation of the
intention-behaviour relationship.
Support for Hypotheses 1a, 1b and 2a would show that the SRBAI can capture habit-
behaviour effects to the same extent as the SRHI. Support for Hypotheses 1c and 2b would
suggest that the SRBAI excludes items that may exaggerate true habit-behaviour associations
or obscure the expected habit x intention interaction.
Hypotheses 1a, 1b, 1c and 2a were assessed using secondary data and the four primary
datasets. Hypotheses 1d and 2b were assessed using the four primary datasets only.
Methods
Identification of SRHI automaticity items
We used Discriminant Content Validation [29], which permits statistical analyses of the
consistency of raters‟ judgements of face validity, to systematically extract automaticity items
from the SRHI. Judges were seven active social or health psychology researchers (not the
present authors), with expertise in social cognition but little or no experience of conducting
habit-related research. Judges were asked to estimate whether each of the twelve SRHI items
mapped on to literature-based definitions of automaticity, frequency, and self-identity (yes vs
no), and to rate their confidence for each judgement on an 11-point scale (0 = completely
uncertain; 10 = completely certain). Each judgement (yes [+1] vs no [−1]) was multiplied by
its confidence rating, producing inter-rater scores ranging from −10 (completely certain that
item does not match construct), through 0 (complete uncertainty), to +10 (completely certain
item matches construct). One-sample t-tests with a test value of zero independently classified
each item in relation to each of the three constructs.
Seven items (items 2, 3, 5, 7, 8, 9, and 10 of the SRHI) were judged to measure automaticity
only. Items 2 ([„Behaviour X is something…‟] „I do automatically‟), 3 („I do without having
to consciously remember‟), 5 („I do without thinking‟) and 8 („I start doing before I realize
I‟m doing it‟) were most confidently and consistently judged to capture automaticity
(ts > 45.00, ps < .001). To maximise parsimony and content validity, these four items were
selected to represent automaticity in subsequent analyses, on the basis that each judge was at
least 90% certain that each item represented automaticity (minimum mean inter-rater
score = 9.57, maximum SD = 0.54). The four-item composite is hereafter referred to as the
„Self-Report Behavioural Automaticity Index‟ (SRBAI).
Details of the content validation task are available from the first author.
Data collection
Secondary datasets: Systematic search strategy and results
Five psychology and health databases (PsycInfo, Medline, Embase, Web of Knowledge,
Scopus) were searched on 20th April 2011. In each, Verplanken and Orbell‟s [12] SRHI
paper was located and citing articles subsequently identified. No date limits were set.
Verplanken & Orbell‟s paper, and three then-in-press papers [30-32], were added.
Papers were eligible for inclusion if they were (a) written in English, (b) published full-text in
peer-reviewed journals, and (c) reported findings from a primary dataset which included (d)
the 12-item SRHI as (e) a measure of habit in relation to a behaviour. Papers focusing on
habitual thought or emotion (e.g. [33]) were excluded.
Papers were retained only when they reported (a) the reliability of the SRHI (Cronbach‟s
alpha), (b) the correlation between the SRHI and a matched (habit-consistent) or directly
opposed (counterhabitual) behaviour (Pearson‟s r), and/or (c) a test of the moderating effect
of the SRHI on the relationship between intention and a habit-consistent or counterhabitual
behaviour (using moderated multiple regression [MMR], whereby behaviour was modelled
on habit, intention, and a means-centred „habit x intention‟ interaction term [34]).
316 papers were identified, of which 135 were duplicates (see Additional file 1: Figure S1 for
search and screening flow chart). Title and abstract screening excluded 7 papers, and full-text
screening excluded a further 85 papers. 47 papers, based on 49 unique datasets, were
retained. BG screened all papers. PL independently screened 20% of papers. 100% agreement
was recorded on selection criteria and data extraction.
Authors of eligible papers were contacted by email, and asked to generate an SRHI-SRBAI
correlation coefficient and rerun, using the SRBAI, as many of the three analyses (reliability,
correlation with behaviour, moderation of intention-behaviour relation) as had been reported
in the published paper. Tailored SPSS syntax templates were sent to aid re-analysis. Authors
were invited to alternatively send clearly labelled SPSS datasets to allow us to conduct
reanalyses. Authors were also asked to indicate where datasets had been used for multiple
publications.
Authors were instructed to assess reliability using Cronbach‟s alpha, to generate bivariate
Pearson‟s r correlation coefficients, and to use MMR to investigate moderation, using a
composite of the four SRBAI items („[Behaviour X is something…]‟ „I do automatically‟, „I
do without having to consciously remember‟, „I do without thinking‟, „I start doing before I
realise I‟m doing it‟). We requested that MMR re-analyses control for the same variables as
in the published paper and that, regardless of statistical significance of the habit x intention
interaction term, simple slope analysis be undertaken to model intention effects at differing
habit levels.
Where habit or behaviour was measured at multiple timepoints, we requested habit data from
the earliest timepoint, and behaviour data from the earliest follow-up. Where papers reported
intervention evaluations, we invited either (a) baseline data only for all participants
combined, or (b) baseline habit and follow-up behaviour data for a no-treatment control
group. Where a single dataset contained multiple habit measures, relevant data were obtained
for all measures.
Twenty-seven authors were contacted. 21 provided all requested information, of whom 14
sent re-analyses and 7 provided raw datasets. Three authors did not provide sufficient
information for all possible re-analyses, and three did not respond, thereby excluding 7
papers. Two datasets were excluded because the raw data were entered into the primary
analyses reported below ([16], Studies 1 and 2).
The final dataset included 34 unique datasets (from 39 papers), generating 45 tests of
reliability and SRHI-SRBAI correlations. Habit-behaviour correlations were available from
24 datasets (allowing 28 tests), and moderation could be tested in 5 datasets (7 tests).
Primary datasets
Details of the two previously published datasets, which used prospective designs and related
to car (Dataset 1; N = 105) and bicycle commuting (Dataset 2; N = 102) respectively, are
available elsewhere [16], though the RFM was excluded from the published report. In
Datasets 1 and 2, participants completed ten RFM items, which were preceded by instructions
asking participants to indicate, as quickly as possible without much deliberation, whether
they would use a car, bus, train or any other transport mode in each of ten scenarios (e.g.
„visiting a friend‟, „taking a trip on a nice day‟ [35]). Each item was presented for a
maximum of 10 seconds, and any-key responses prompted presentation of the next item.
Participants completed 5 practice trials. RFM scores represented the summed frequency with
which the car (or bicycle) was chosen. SRHI-based habit indices, including a composite scale
of SRHI items removed from the SRBAI (i.e. SRHI items 1, 4, 6, 7, 9, 10, 11, and 12 [e.g.
„Behaviour X is something I have no need to think about doing‟] [12]; hereafter, the „non-
SRBAI‟), were reliable in both datasets (Dataset 1: minimum α = .92; Dataset 2: minimum
α = .91).
The two new datasets were collected via online questionnaires. All measures were self-
reported, and unless otherwise specified, responses ranged from 1 (strongly agree) to 7
(strongly agree). A priori power analysis for a medium-effects regression model with three
predictors indicated that N = 76 was sufficient for power at .80 where p < .05.
Dataset 3 (snacking) used a prospective design, whereby habit and intention were measured
at Time 1, and behaviour was measured via email one week later (T2). Participants were
recruited via an email sent to participant pool lists, and recipients were encouraged to forward
the email to others to create a „snowball‟ effect [36]. 188 UK non-diabetic adults with no
eating disorders completed all study items (49 males, 138 females, 1 unspecified; age range
18–76, M = 30.94, SD = 11.77). Behaviour was measured as the frequency with which each of
five high-calorie snacks (crisps, chocolate, cakes, sweets, biscuits) were eaten over the
previous week („0 times‟ [1] – „10 or more times‟ [7]), as summed to provide an index (range
1–35; M = 10.32, SD = 2.89). Intention measures related to avoiding eating high-calorie
snacks (e.g. „I will try to avoid eating high-calorie snacks over the next 7 days‟; 3 items, α =
.92; M = 4.06, SD = 1.81), and habit items related to „eating high-calorie snacks‟ (e.g. „…is
something I do automatically‟). Habit indices were reliable (minimum α = .81), and mean
scores suggested moderate snacking habits (SRHI: M = 3.50, SD = 1.19; SRBAI: M = 3.39,
SD = 1.55; „non-SRBAI‟: M = 3.55, SD = 1.13).
Dataset 4 (alcohol consumption) used a cross-sectional design. 204 members of a UK-based
health research panel, recruited via an email advertisement, completed all study items (50
males, 150 females, 4 unspecified; age data not recorded due to researcher error). (Past)
behaviour was calculated as a percentage, using a) number of evening meals with which at
least one alcoholic drink was consumed, and b) number of evening meals eaten, over the
preceding week (i.e. [a/b x 100]; M = 27.57, SD = 28.05). Intention was measured using two
items (e.g. „I intend to drink an alcoholic drink with my evening meal on most days over the
next week‟; α = .94; M = 2.11, SD = 1.80). Habit items related to „drinking an alcoholic drink
with my evening meal‟ (e.g. „…is something I do without having to consciously remember‟;
minimum α = .92). Mean scores indicated typically weak habits (SRHI: M = 2.14, SD = 1.41;
SRBAI: M = 1.90, SD = 1.35; „non-SRBAI‟: M = 2.26, SD = 1.48).
Further details of all primary datasets are available on request from the first author.
Analysis strategy
Data were analysed using procedures and techniques commonly used to quantify the
contribution of the SRHI to prediction of behaviour (e.g. [4]).
Analysis of secondary datasets
Correlation coefficients were entered into meta-analysis to generate weighted summary
effects for comparison. Within-dataset and meta-analysed SRBAI-behaviour and SRHI-
behaviour correlations were compared statistically by the present authors, following Meng,
Rosenthal and Rubin‟s guidelines [37]. Moderation effects, as partial correlations, were not
meta-analysed because of variation across studies in the variables controlled within MMR
models. Instead, a vote-count procedure was employed. Where sample sizes were
inconsistent across effects in a single dataset (e.g. larger N available for reliability than for
correlations; 16 datasets), the smallest N was used for all effects generated from that dataset.
Fixed-effect meta-analysis of correlations was undertaken using Comprehensive Meta-
Analysis software [38]. Sample-weighted average effect sizes (r+) were calculated using
Fisher‟s Z transformations, and 95% confidence intervals were generated. Negative
correlations between habit and counterhabitual behaviour were reversed prior to weighting. A
priori power analysis, conducted using conservative estimates for unknown parameters,
indicated that, for 27 tests where average within-study N = 50 and one-tailed p < .05, power to
detect a small effect (r+ = .10) was 0.84 [39]. Bivariate Pearson‟s r correlations of .10, .30,
and .50 were interpreted as small, medium and large effects respectively [40].
Three datasets yielded multiple and conceptually independent habit-behaviour correlations
(e.g. TV viewing habits and behaviour, soft drink consumption habits and behaviour). All
such effects were retained, but sample sizes were divided by the number of relevant habit-
behaviour correlations, and rounded downwards where this did not produce an integer (e.g.
538 / 3 = 179.33 ≈ 179 [30,31,41]). While this violates the independence assumption of meta-
analysis, we do not view this as problematic, because analysis was undertaken to compare
habit measures, not to generate reliable effect size estimates.
Analysis of primary datasets
Cronbach‟s alpha coefficients were calculated to test the reliability of the SRHI, SRBAI, and
„non-SRBAI‟ indices. Pearson‟s r correlation coefficients were generated for relationships
between the habit indices and behaviour, and differences in the magnitude of habit-behaviour
correlation coefficients were evaluated statistically [37].
MMR was used to test for moderation. Behaviour was modelled on habit, intention, and a
„habit x intention‟ interaction term (i.e. the multiplicative product of means-centred habit and
intention variables). Significant interaction terms denote moderation, and were deconstructed
using simple slope analysis to plot intention-behaviour effects at one SD below the mean
(weak habit), at the mean (moderate habit), and one SD above the mean of the habit variable
(strong habit [34]). Multiple models were run to compute effects for each habit index (SRHI,
SRBAI, „non-SRBAI‟).
Results
Secondary datasets
Reliability
Of 45 reliability assessments of the SRBAI, 23 found α within the range .90-.97, 17 found an
alpha between .80-.89, four an alpha between .70-.79, and one alpha equalled .68 (see
Additional file 2: Table S1 for study characteristics). The SRBAI thus appeared largely
reliable.
Correlations of SRHI, SRBAI, and behaviour
The SRHI and SRBAI correlated at r+ = .92 (95% CI: .91, .92, p < .001; range ± .79-.97;
k = 45, N = 11,257; Table 1), supporting Hypothesis 1a. Of 28 habit-behaviour correlation
tests, 21 found the SRBAI-behaviour correlation to be lower than the SRHI-behaviour
correlation (p < .05), and in 7 there was no difference. Across tests, the weighted SRBAI-
behaviour correlation (r+ = .41; 95% CI: .39, .43, p < .001) was significantly lower than the
SRHI-behaviour correlation (r+ = .47; 95% CI: .45, .48, p < .001; Z = 14.31, p < .001; k = 28,
N = 8,492). Hypotheses 1b and 1c were mostly supported.
Table 1 Secondary datasets: Meta-analysis of SRHI-SRBAI and habit-behaviour
correlation coefficients
k
N
SRHI-SRBAI r (95% CI)
Habit-behaviour
SRHI
SRBAI
Z
r (95% CI)
r (95% CI)
45
11,257
.92***
(.91, .92)
28
8,492
.47***
.41***
14.31***
(.45, .48)
(.39, .43)
***p < .001. k = number of datasets, N = total number of participants across datasets,
Z = difference between SRHI-behaviour and SRBAI-behaviour correlation coefficients (see
[37])
Moderation tests
Of 7 tests of the moderating impact of habit on the intention-behaviour relation, the SRHI and
SRBAI yielded similar effects in 5 tests: in four tests, both measures found moderation, and
in one test, neither measure found moderation (Table 2). In one test, the SRHI found a
moderation effect (p < .04) but the SRBAI did not (p = .13). In another test, the SRBAI found
a tendency towards moderation that approached statistical significance (p = .052) but the
SRHI did not (p = .11). The latter test generated an unexpected „effect‟ whereby the impact of
intentions on behaviour increased as habit strengthened; a similar pattern of intention-
behaviour relations at varying habit levels was also observed here using the SRHI.
Hypothesis 2a thus received mixed support.
Table 2 Secondary datasets: SRHI vs SRBAI as moderator of the intention-behaviour relationship
SRHI
SRBAI
Reference
N
Habit
Intention
Covariates
in regression
model
Significance
of moderation
effect (p)
Intention-behavior β (p)
Significance
of moderation
effect (p)
Intention-behavior β (p)
Weak or
no habit
Moderate
habit
Strong
habit
Weak or no
habit
Moderate
habit
Strong habit
De Bruijn [41] /
De Bruijn &
Gardner [30]/ De
Bruijn & Rhodes
[31]
538
Eating at
least 2 pieces
of fruit per
day
To eat two
pieces of fruit
per day
I, H, PBC,
A, SN
.001
.39(<.001)
.34(<.001)
.16(.01)
.003
.44(<.001)
.38(<.001)
.24(<.001)
De Bruijn [41] /
De Bruijn &
Gardner [30]/ De
Bruijn & Rhodes
[31]
538
Using a
bicycle
To use a
bicycle
I, H, PBC,
IA, AA, SN,
DG, DA
.04
.28(<.001)
.16(.004)
.15(.01)
.13
.31(<.001)
.23(<.001)
.22(<.001)
De Bruijn [41] /
De Bruijn &
Gardner [30] / De
Bruijn & Rhodes
[31]
538
Exercising
for at least 20
mins per day
To engage in
vigorous
exercise
I, H, PBC,
IA, AA, SN
.01
.22(.001)
.15(.007)
.06(.38)
.04
.27(<.001)
.22(<.001)
.15(.02)
De Bruijn, Kroeze
et al. [42]
748
Watching the
amount of fat
in my diet
To watch the
amount of fat
in my diet
I, H, PBC,
IA, AA, SN
.007
-.29(<.001)
-.19(<.001)
.07(.14)
.007
-.36(<.001)
-.23(<.001)
-.11(<.001)
De Bruijn,
Kremers, Singh et
al. [43]
317
Using a
bicycle as a
means of
transportation
To use
bicycle for
transportation
OB, D, I,
PBC, A, SN,
H
<.001
.67(<.001)
.37(<.001)
.10(.11)
<.001
.56(<.001)
.37(<.001)
.18(.007)
Norman [44]
109
Binge-
drinking
To engage in
binge-
drinking
I, H
.11
.28(.01)
.42(.001)
.57(<.001)
.052
.35(.001)
.52(<.001)
.68(<.001)
Rhodes, De Bruijn
& Matheson [45]
153
Leisure time
active sport
or vigorous
PA
To engage in
PA (x) times
per week
I, H, PBC,
AA, IA, SN,
IS, IxIS
.35
.42(.03)
.34(.001)
.78(<.001)
.40
.52(.005)
.38(<.001)
.48(.01)
PA = Physical activity. Covariates: I = Intention, H = Habit (SRHI/SRBAI), PBC = Perceived behavioral control, A = Attitude, IA = Instrumental attitude, AA = Affective
attitude, SN = Subjective norm, IS = Intention stability, IxIS = Intention x intention stability, D = (various) demographics, OB = engagement in (various) other behaviors.
DG = Demographic: Gender. DA = Demographic: Age. Italicised references indicate papers based on same data but in which the focal moderation test was not reported
Primary datasets
Correlations of habit indices and behaviour
In all four datasets, the SRBAI, SRHI and non-SRBAI indices were strongly intercorrelated
(SRHI-SRBAI rs ≥ .90), and correlated with behaviour (rs ≥ .42; Table 3; see also Additional
file 3: Table S2a and Additional file 4: Table S2b for full descriptive and intercorrelations).
In Datasets 1 and 2, the SRBAI, SRHI and non-SRBAI also correlated strongly with the RFM
(rs ≥ .49). Hypotheses 1a and 1b were thus supported. The SRBAI-behaviour correlation was
weaker than the SRHI-behaviour correlation (ps < .001) in Datasets 1, 3 and 4 but not Dataset
2 (p = 1.0), and weaker than the „non-SRBAI‟-behaviour correlation in Datasets 3 and 4
(p ≥ .04), but not Datasets 1 or 2 (minimum p = .16). There was no difference between SRHI-
behaviour and „non-SRBAI‟-behaviour correlations in any of the datasets (maximum
Z = 1.57, minimum p = .06). There was mixed support for Hypotheses 1c and 1d.
Table 3 Primary datasets: Habit indices as correlates of behaviour and moderators of intention-behaviour relationship in four primary
datasets
Source
N†
Behaviour
Habit
Intention
Habit index (α)
Correlations††
Moderation of intention-behaviour relationship
SRHI-
SRBAI
Habit-
RFM
Habit-
behaviour
Model
R2†††
Significance
of moderation
effect†††† (p)
Intention-behaviour β
Weak or
no habit
Moderate
habit
Strong
habit
Dataset 1:
([16], Study 1)
105
Inactive (car)
commuting
“Using a car to
commute to
campus”
“To use a car to
commute to campus
on most days”
SRHI(.95)
.94
.52
.82a
.75
.001
.54***
.27*
.01
SRBAI(.92)
.52
.76b
.75
<.001
.69***
.41***
.12
Non-SRBAI(.91)
.49
.81a
.73
.01
.57***
.37**
.16
Dataset 2:
([16], Study 2)
102
Active
(bicycle)
commuting
“Using a
bicycle to
commute to
campus”
“To use a bicycle to
commute to campus
on most days”
SRHI(.95)
.97
.67
.86 a
.77
.04
.16
.02
-.12
SRBAI(.93)
.65
.86 a
.77
.04
.21*
.08
-.05
Non-SRBAI(.91)
.67
.84 a
.74
.04
.26**
.12
-.02
Dataset 3: New
dataset
188
Unhealthy
snacking
“Eating high-
calorie snacks”
“To avoid high-
calorie snacks”
SRHI(.89)
.90
-
.50a
.26
.89
SRBAI(.84)
-
.42b
.19
.35
Non-SRBAI(.81)
-
.50a
.27
.95
Dataset 4: New
dataset
204
Alcohol
consumption
with the
evening meal
“Drinking an
alcoholic drink
with my
evening meal”
“To drink an
alcoholic drink with
my evening meal”
SRHI(.89)
.95
-
.80 a
.68
.14
SRBAI(.84)
-
.75 b
.64
.02
.56***
.46***
.35***
Non-SRBAI(.81)
-
.80 a
.68
.18
*p < .05, ** p < .01, *** p < .001. Further details and analyses of all datasets are available on request from the first author
† Ns are reduced for correlations with RFM in Datasets 1 (N = 102) and 2 (N = 99) due to missing RFM data
†† Differing superscript letters in „habit-behaviour‟ column indicate differences in the magnitude of habit-behaviour correlations at p < .05 (see [37]). Correlations with the
transport-specific RFM were only available in Datasets 1 and 2. All correlations significant at p < .01
††† All regression models were significant at p < .001
†††† „Moderation effect‟ refers to the predictive impact of a means-centred habit x intention interaction term on behaviour, controlling for habit and intention as independent
predictors. Simple slope coefficients are provided for significant moderation effects only (p < .05)
Moderation tests
In Datasets 1 and 2, the SRBAI, SRHI, and „non-SRBAI‟ indices each moderated the
intention-behaviour relationship in line with theoretical predictions (maximum p = .04), with
intention-behaviour relations strongest at low habit, and weakening as habit strength
increased. No moderation was found using any index in Dataset 3 (minimum p = .35). In
Dataset 4, the SRHI (β = −.10, p = .14) and „non-SRBAI‟ (β = −.09, p = .18) did not moderate
the intention-behaviour relationship, but the SRBAI did (β = −.16, p = .02), such that the
impact of intention on action weakened as habit strength increased. Thus, both Hypotheses 2a
and 2b received mixed support. In Datasets 3 and 4, the SRBAI-based model explained less
variance in behaviour (R2 = .19 and .64, respectively) than did the SRHI (R2 = .26 and .68) or
SRBAI (R2 = .26 and .68), likely due to omission of items relating more to behaviour
frequency than automaticity.
Discussion
We have argued that the impact of habit on behavioural repetition can be more
parsimoniously captured by a subset of automaticity items from the Self-Report Habit Index
(SRHI). It has been suggested elsewhere that automaticity is the „active ingredient‟ of habit
[15,24], and so we extracted from the SRHI a four-item automaticity subscale (the „Self-
Report Behavioural Automaticity Index‟; SRBAI). We assessed the utility of the SRBAI in a
re-analysis of data from all available previous SRHI applications, and primary analyses of
four energy-balance related behaviours (EBRBs): inactive (car) commuting, active (bicycle)
commuting, unhealthy snacking, and alcohol consumption. The SRBAI was found to meet
criteria that have been taken to reflect the adequacy of the SRHI for detecting health habits:
reliability, convergent validity, and predictive validity. The SRBAI was reliable, and
correlated strongly with the SRHI, and, in travel mode choice applications, the Response-
Frequency Habit Measure (RFM). The SRBAI was also sensitive to effects predicted by
theory [5], correlating with behaviour frequency, and typically detecting the moderating
impact of habit on the intention-behaviour relation. In an application to alcohol consumption,
the SRBAI was more sensitive to hypothesised moderation than was the SRHI, or the eight-
item subset excluded from the SRBAI (the „non-SRBAI‟). On balance, the SRBAI operated
in close concordance with the SRHI in detecting habit-behaviour relationships, despite the
removal of eight items.
The SRHI is the most commonly used measure of nutrition and activity habits [4], but its
popularity does not necessarily reflect parsimony. Similar sensitivity of the SRBAI and SRHI
to hypothesised habit effects suggests that our measure is more succinct and practical than the
SRHI. This makes the SRBAI well suited to study of EBRBs, in which multiple
determinants, habits and behaviours contribute to a positive energy balance. While SRHI
subscales have been used previously [3,16-18], our work is the first to have systematically
extracted core items using robust content validity techniques. Adoption of the SRBAI as the
standard SRHI shortform, at least in determinant studies and studies of habit formation and
disruption, would aid future research by offering an SRHI subscale that best captures
characteristic habit-behaviour effects [15,24,25]. Use of this measure would also ensure
homogeneity of measurement for future research syntheses.
There are also conceptual advantages to our subscale. It assesses one characteristic of
behaviour patterns, distinguishing automaticity from behavioural frequency and behavioural
identification. It has been proposed that automaticity is the „active ingredient‟ of habit, and
that the inclusion of non-automaticity indicators in the SRHI may confound detection of true
habit-behaviour effects [4,15,24]. Social cognition theories posit that habit will moderate the
intention-behaviour relation, so that where habit strengthens, the intention-behaviour link is
attenuated [5]. Across all datasets, the SRBAI was equally sensitive to the SRHI in detecting
hypothesised moderation in eight of eleven tests. One test showed the SRHI to detect
moderation where the SRBAI did not, and in another test, the SRBAI detected moderation to
which neither the SRHI nor the „non-SRBAI‟ was sensitive. In a further test, a tendency
towards moderation was observed using the SRBAI, though this „effect‟ was underpinned by
stronger intention-behaviour relations as habit strength increased [44]. Notably however, in
this test the SRHI tended towards moderation in the same direction as did the SRBAI.
Although empirically well supported elsewhere [4], the validity of moderation as a criterion
for a habit index was challenged by the failure of either the SRHI or SRBAI to reliably detect
moderation in three datasets, and the unexpected strengthening of intention-behaviour
relations in another dataset. These findings may perhaps reflect measurement artefacts arising
from consistency biases or limited range in habit or intention measures. Further work is
needed to explore the conditions in which the hypothesised moderating impact of habit on the
intention-behaviour relation best holds when assessed by self-report data.
The SRHI, and a subscale of items excluded from the SRBAI, predicted more variance in
behaviour than did the SRBAI in snacking and alcohol consumption applications. It might
therefore be argued that while we have added parsimony to the SRHI, by doing so we have
compromised its predictive validity. We do not however believe that stronger habit-behaviour
correlations necessarily reflect superior predictive validity of the SRHI: if the impact of habit
on action can be solely attributed to automaticity [15,24] then the additional variance
accounted for by the SRHI and „non-SRBAI‟ scales may not be reliably attributable to habit.
Previous research has shown that self-identity, which is also measured by the SRHI,
correlates with behavioural frequency but, unlike habit, does not predict behaviour directly
[14]. Concerns have also been raised about the validity of including frequency indicators in
the SRHI when estimating habit-behaviour relationships, because behaviour frequency can
capture both automatic (habitual) and reflective (non-habitual) influences on action [26]. We
suggest that the eight items excluded from the SRBAI, which likely capture identity-
relevance, behavioural frequency, and weaker automaticity indicators, may therefore
correlate with behaviour independently of automaticity, so inflating true relationships
between automatic cue-responding and behaviour frequency [4]. Our preliminary content
validation procedure identified seven potential automaticity indicators however, and future
work might test this explanation by assessing whether a seven-item automaticity index
improves on the predictive validity of the SRBAI. Any such gains in predictive validity
would however need to be sufficiently sized to justify foregoing the parsimony benefits
afforded by the four-item SRBAI.
A measure of a psychological construct can be considered useful in at least two respects: first,
detection of the corollaries of the construct, and second, demarcation of the construct. Habits
are distinct from other forms of automaticity in that they are learned through repetition in
stable contexts, and are ongoing, having previously been enacted and remaining likely to be
enacted in future encounters with associated environmental cues [2,3]. Our results suggest
that the SRBAI can adequately and concisely detect the effects of habit on behaviour, but it is
unlikely to distinguish habit-related automaticity from other forms of automaticity, such as
behaviour prompted by implementation intentions (i.e. one-off, pre-planned and highly
specific cue-responses [46]), or ideomotor or primed behaviours [47,48]. Items relating to
repetition history may be needed to distinguish habit from non-habit-related automaticity, and
for these reasons, we term our measure an index of automaticity, rather than a measure of
habit per se. The SRHI is however most commonly applied to behaviour prediction and habit
formation studies, in which such a distinction is not of interest, and in these research contexts
the SRBAI offers a more practical and parsimonious alternative to the SRHI.
Criticisms of the SRHI have been raised which are not addressed by our subscale. For
example, the validity of self-reports on action which may proceed outside of awareness has
been questioned [15,49]. The utility of the SRHI suggests that these concerns may be
overstated because people are commonly aware when reflecting retrospectively on their
behaviour that they were not consciously monitoring the behaviour when it was enacted.
Validation of both the SRHI and SRBAI against lab-based measures of automated action is
needed to support this assertion [50]. Commentators have also suggested that the SRHI is
limited because it typically omits cues to habits [15,20]. Our alcohol consumption application
demonstrated that the measure could be worded to include a contextual component (i.e.
„drinking alcohol with my evening meal‟), but the idiosyncratic nature of habit cues, and
potential lack of awareness of the specific cues to habitual action, remains problematic for the
validity of the SRHI and SRBAI. SRHI applications have also been criticised for relying on
correlational and often cross-sectional population-level survey data [15], and we recognise
the limitations of using such data to understand person- and context-specific cue-response
associations. Nonetheless, the SRHI has come to be accepted as an adequate measure of habit
on the basis of analyses of such data. Our data thus indicate that the SRBAI meets the same
criteria by which the SRHI has previously been judged, as applied in the research contexts in
which the SRHI has been most frequently used.
Conclusion
We have argued that the impact of habit on behaviour can be measured more parsimoniously
by using clearly-defined automaticity items from the Self-Report Habit Index. Our four-item
SRHI subscale is more succinct and easier to administer than extant measures. This measure,
the Self-Report Behavioural Automaticity Index, correlates highly with existing measures,
and appears sensitive to effects that characterise habits. It offers practical benefits for
detection of EBRB habits, and we recommend its use in behaviour prediction studies, and
studies that track habit formation or disruption.
Abbreviations
EBRB, Energy-balance related behaviour; MMR, Moderated multiple regression; RFM,
Response-frequency habit measure; SRHI, Self-report habit index; SRBAI, Self-report
behavioural automaticity index
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
BG and CA formulated the initial research ideas and generated specific hypotheses in
collaboration with PL and GJdB. BG collected and analysed all data, and drafted the
manuscript. CA oversaw design, collection and reporting of work on Datasets 1 and 2 as
supervisor of BG‟s doctoral research. GJdB and PL assisted with selection and coding of
secondary data. GJdB assisted with analysis of secondary data. All authors contributed to
redrafts of the manuscript, and read and approved the final manuscript.
Acknowledgements
We thank the many authors who kindly re-analysed their data for the secondary data
analyses, and David Neal, Robert West and Wendy Wood for helpful comments on earlier
drafts of the manuscript. This work received no external funding. BG is funded by the UK
Higher Education Funding Council for England (HEFCE). CA is funded by the National
Institute for Health Research (NIHR) UK. PL is funded by a Cancer Research UK
studentship (CRUK). GJdB is funded by University of Amsterdam. The views expressed are
those of the authors and do not necessarily represent those of the organisations that fund the
authors. HEFCE, NIHR and CRUK played no role in design, collection, analysis or
interpretation of the data, nor in writing of the manuscript, nor the decision to submit the
manuscript for publication.
References
* References reporting data included in both the secondary data analyses and Table
2 are marked with an asterisk. All other references reporting data included in the
secondary data analyses are listed in a Supplementary References file (Additional
file 5).
1. Ouellette JA, Wood W: Habit and intention in everyday life: The multiple processes by
which past behavior predicts future behavior. Psychol Bull 1998, 124:54–74.
2. Verplanken B, Aarts H: Habit, attitude, and planned behaviour: Is habit an empty
construct or an interesting case of goal-directed automaticity? Eur Rev Soc Psychol 1999,
10:101–134.
3. Lally P, van Jaarsveld CHM, Potts HWW, Wardle J: How are habits formed: Modelling
habit formation in the real world. Eur J Soc Psychol 2010, 40:998–1009.
4. Gardner B, de Bruijn GJ, Lally P: A systematic review and meta-analysis of
applications of the Self-Report Habit Index to nutrition and physical activity
behaviours. Ann Behav Med 2011, 42:174–187.
5. Triandis H: Interpersonal behavior. Monterey, CA: Brooks-Cole; 1977.
6. Wood W, Tam L, Witt MG: Changing circumstances, disrupting habits. J Pers Soc
Psychol 2005, 88:918–933.
7. Rothman AJ, Sheeran P, Wood W: Reflective and automatic processes in the initiation
and maintenance of dietary change. Ann Behav Med 2009, 38(Suppl1):S4–S17.
8. Verplanken B, Wood W: Interventions to break and create consumer habits. J Pub
Policy Mark 2006, 25:90–103.
9. Lally P, Gardner B: Promoting habit formation. Health Psychol Rev, .
doi:10.1080/17437199.2011.603640. In press.
10. Lally P, Wardle J, Gardner B: Experiences of habit formation: A qualitative study.
Psychol Health Med 2011, 16:484–489.
11. Lally P, Chipperfield A, Wardle J: Healthy habits: Efficacy of simple advice on weight
control based on a habit-formation model. Int J Obes 2008, 32:700–707.
12. Verplanken B, Orbell S: Reflections on past behavior: A self-report index of habit
strength. J Appl Soc Psychol 2003, 33:1313–1330.
13. Gardner B, Abraham C, Lally P, de Bruijn G-J: 'The habitual use of the Self-report
Habit Index': A reply. Ann Behav Med 2012, 43:141–142.
14. Gardner B, de Bruijn G-J, Lally P: Habit, identity, and repetitive action: A prospective
study of binge-drinking in UK students. Brit J Health Psychol 2012, 17:565–581.
15. Sniehotta FF, Presseau J: The habitual use of the Self-Report Habit Index. Ann Behav
Med 2012, 43:139–140.
16. Gardner B: Modelling motivation and habit in stable travel mode contexts. Transp
Res F: Traff Psychol Behav 2009, 12:68–76.
17. Honkanen P, Olsen SO, Verplanken B: Intention to consume seafood - the importance
of habit. Appetite 2005, 45:161–168.
18. Rhodes R, de Bruijn GJ: Automatic and motivational correlates of physical activity:
Does intensity moderate the relationship? Behav Med 2010, 36:44–52.
19. Kremers SPJ, Visscher TLS, Seidell JC, Van Mechelen W, Brug J: Cognitive
determinants of energy balance-related behaviours: Measurement issues. Sports Med
2005, 35:923–933.
20. de Bruijn GJ, van den Putte B: Adolescent soft drink consumption, television viewing
and habit strength. Investigating clustering effects in the Theory of Planned Behaviour.
Appetite 2009, 53:66–75.
21. Drolet AL, Morrison DG: Do we really need multiple-item measures in service
research? J Serv Res 2001, 3:196–204.
22. Verplanken B, Myrbakk V, Rudi E: The measurement of habit. In The routines of
decision making. Edited by Betsch T, Haberstroh S. Mahwah, NJ: Lawrence Erlbaum
Associates; 2005:231–247.
23. Mittal B: Achieving higher seat belt usage: The role of habit in bridging the attitude-
behavior gap. J Appl Soc Psychol 1988, 18:993–1016.
24. Gardner B: Habit as automaticity, not frequency. Euro Health Psychologist 2012,
14:32–36.
25. Orbell S, Verplanken B: The Automatic Component of Habit in Health Behavior:
Habit as Cue-Contingent Automaticity. Health Psychol 2010, 29:374–383.
26. Ajzen I: Residual effects of past on later behavior: Habituation and reasoned action
perspectives. Pers Soc Psychol Rev 2002, 6:107–122.
27. Verplanken B, Aarts H, van Knippenberg A, van Knippenberg C: Attitude versus
general habit: Antecedents of travel mode choice. J Appl Soc Psychol 1994, 24:285–300.
28. Suter PM: Is alcohol consumption a risk factor for weight gain and obesity? Crit Rev
Clin Lab Sci 2005, 42:197–227.
29. Pollard B, Johnston M: Operationalisation of constructs within theoretical models
using existing measures: a method to establish content validity of health status
measures. P Brit Psychol Soc 2005, 13:87.
30. * de Bruijn GJ, Gardner B: Active commuting and habit strength: an interactive and
discriminant analyses approach. Am J Health Promot 2011, 25:e27–e36.
31. * de Bruijn GJ, Rhodes RE: Exploring exercise behavior, intention and habit strength
relationships. Scand J Med Sci Spor 2011, 21:482–491.
32. Norman P, Cooper Y: The theory of planned behaviour and breast self-examination:
Assessing the impact of past behaviour, context stability and habit strength. Psychol
Health 2011, 26:1156–1172.
33. Verplanken B, Velsvik R: Habitual negative body image thinking as psychological
risk factor in adolescents. Body Image 2008, 5:133–140.
34. Aiken LS, West SG: Multiple regression: Testing and interpreting interactions. London:
Sage; 1991.
35. Verplanken B, Aarts H, van Knippenberg A, Moonen A: Habit versus planned
behaviour: A field experiment. Br J Soc Psychol 1998, 37:111–128.
36. Gardner B: Incentivised snowballing. The Psychologist 2009, 22:768–769.
37. Meng X-L, Rosenthal R, Rubin DB: Comparing correlated correlation coefficients.
Psychol Bull 1992, 111:172–175.
38. Borenstein M, Hedges L, Higgins J, Rothstein H: Comprehensive meta-analysis.
Englewood, NJ: Biostat; 2010. 2.2.057.
39. Hedges LV, Pigott TD: The power of statistical tests in meta-analysis. Psychol
Methods 2001, 3:203–217.
40. Cohen J: A power primer. Psychol Bull 1992, 112:155–159.
41. * de Bruijn GJ: Understanding college students' fruit consumption. Integrating habit
strength in the theory of planned behaviour. Appetite 2010, 54:16–22.
42. * de Bruijn GJ, Kroeze W, Oenema A, Brug J: Saturated fat consumption and the
Theory of Planned Behaviour: Exploring additive and interactive effects of habit
strength. Appetite 2008, 51:318–323.
43. * de Bruijn GJ, Kremers SPJ, Singh A, van den Putte B, Van Mechelen W: Adult Active
Transportation: Adding Habit Strength to the Theory of Planned Behavior. Am J Prev
Med 2009, 36:189–194.
44. * Norman P: The theory of planned behavior and binge drinking among
undergraduate students: Assessing the impact of habit strength. Addict Behav 2011,
36:502–507.
45. * Rhodes R, de Bruijn GJ, Matheson DH: Habit in the physical activity domain:
Integration with intention temporal stability and action control. J Sport Exerc Psychol
2010, 32:84–98.
46. Gollwitzer PM, Sheeran P: Implementation intentions and goal achievement: A meta-
analysis of effects and processes. Adv Exp Soc Psychol 2006, 38:249–268.
47. Bargh JA, Chen M, Burrows L: The automaticity of social behavior: Direct effects of
trait concept and stereotype activation on action. J Pers Soc Psychol 1996, 71:230–244.
48. Dijksterhuis A, van Knippenberg A: The relation between perception and behaviour,
or how to win a game of Trivial Pursuit. J Pers Soc Psychol 1998, 74:865–877.
49. Eagly AH, Chaiken S: The psychology of attitudes. Fort Worth: Harcourt Brace
Jovanovich; 1993.
50. de Bruijn G-J, Keer M, Conner M, Rhodes R: Using implicit associations towards fruit
consumption to understand fruit consumption behaviour and habit strength
relationships. J Health Psychol 2012, 17:479–489.
Additional files
Additional_file_1 as DOC
Additional file 1: Figure S1. Results of systematic search strategy and screening procedure
Additional_file_2 as DOC
Additional file 2: Table S1. Secondary datasets: Study characteristics, reliabilities, and
habit-behaviour and SRHI-SRBAI correlations.
Additional_file_3 as DOC
Additional file 3: Table S2a. Primary datasets: Descriptives and intercorrelations (Datasets 1
and 2)
Additional_file_4 as DOC
Additional file 4: Table S2b. Primary datasets: Descriptives and intercorrelations (Datasets
3 and 4)
Additional_file_5 as DOC
Additional file 5. References for Supplementary Material.
Additional files provided with this submission:
Additional file 1: Supplementary Figure 1 - Systematic search strategy and
screenin, 31K
http://www.ijbnpa.org/imedia/8361017237562934/supp1.doc
Additional file 2: Supplementary Table 1 - Secondary datasets - Study
characteristi, 90K
http://www.ijbnpa.org/imedia/4031277377562944/supp2.doc
Additional file 3: Supplementary Table 2a - Descriptives and intercorrelations,
Dat, 41K
http://www.ijbnpa.org/imedia/1221593827756294/supp3.doc
Additional file 4: Supplementary Table 2b - Descriptives and intercorrelations,
Dat, 36K
http://www.ijbnpa.org/imedia/1529776117562956/supp4.doc
Additional file 5: Supplementary References.doc, 32K
http://www.ijbnpa.org/imedia/1802386516756295/supp5.doc
