# The association between implicit attitudes toward physical activity and physical activity behaviour: a systematic review and correlational meta-analysis

Guillaume Chevance, Paquito Bernard, Pier Eric Chamberland, Amanda Rebar

► To cite this version:

Guillaume Chevance, Paquito Bernard, Pier Eric Chamberland, Amanda Rebar. The association between implicit attitudes toward physical activity and physical activity behaviour: a systematic review and correlational meta-analysis. Health Psychology Review, 2019, 13 (3), pp.248-276. 10.1080/17437199.2019.1618726. [hal-05042458](https://hal.science/hal-05042458v1)

HAL Id: hal-05042458

https://hal.science/hal-05042458v1

Submitted on 22 Apr 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_1.png" /></figure>
# The association between implicit attitudes toward physical activity and physical activity behavior: A systematic review and correlational meta-analysis

Guillaume Chevance<sup>1, 2, 3</sup>, Paquito Bernard<sup>4, 5</sup>, Pier Eric Chamberland<sup>6</sup> & Amanda Rebar<sup>7</sup>

<footnote n="1"><sup>1</sup> SANTESIH (EA 4614), Univ. Montpellier, Montpellier, France</footnote>

<footnote n="2"><sup>2</sup> Exercise and Physical Activity Resource Center, University of California, San Diego, USA</footnote>

<footnote n="3"><sup>3</sup> Center for Wireless and Population Health Systems, University of California, San Diego, USA</footnote>

<footnote n="4"><sup>4</sup> Department of Physical Activity Sciences, Université du Québec à Montréal, Montréal, Quebec, Canada</footnote>

<footnote n="5"><sup>5</sup> Research Center, University Institute of Mental Health at Montreal, Montréal, Quebec, Canada</footnote>

<footnote n="6"><sup>6</sup> Department of Physical Activity Sciences, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, Canada</footnote>

<footnote n="7"><sup>7</sup> School of Health, Medical, and Applied Sciences, Central Queensland University, Rockhampton, Queensland, Australia</footnote>
## Contact Information

**Corresponding author:** GC, 9500 Gilman Dr, La Jolla, CA 92093, USA. Email: gchevance@eng.ucsd.edu, Phone: (858) 534-2230

PB: 141, Avenue du Président Kennedy, Montréal, Québec, Canada, H2X 1Y4. Email: bernard.paquito@uqam.ca, Phone: 514 987 3000

PEC: 3351 Boulevard des Forges, Trois-Rivières, QC G9A 5H7, Canada. Email: pier.e.chamberland@gmail.com, Phone: 1 800 365-0922

ALR: Building 7, Rm LG15, Rockhampton, QLD, Australia, 4703. Email: a.rebar@cqu.edu.au, Phone: +61 07 4923 2182
Acknowledgement

The authors wish to thank the Drs Franzisca Antoniewicz, Carolin Muschalik, Tanya Berry, Julie Boiché, Matthias Bluemke, Austin Baldwin and Ralph Brand for providing us with the necessary information for the meta-analysis.

PB is supported by Université du Québec à Montréal, Institut Universitaire de Santé Mentale de Montréal and by a salary award from the Fonds de recherche du Québec – Santé.
**Abstract:**

As a result of recent calls to attend to the implicit processes that regulate health behaviors, the study of implicit attitudes and physical activity behavior has grown rapidly in the past decade. The aim of this study was to summarize existing evidence on the extent to which implicit attitudes toward physical activity are associated with physical activity behavior. A systematic literature review was performed to retrieve studies reporting both a measure of implicit attitudes and physical activity. For the meta-analysis, effect size (Pearson's r) were extracted from eligible studies or retrieved from authors. A total of 26 independent studies, and 55 effect sizes, were eligible. There was a small, significant, and positive correlation between implicit attitudes and physical activity, a finding replicated across multiple meta-analytical strategies with sensitivity analyses applied. This association was not significantly moderated by study design or objective, participants' age or other characteristics, or measures of implicit attitudes or physical activity. This meta-analysis provides evidence that implicit attitudes toward physical activity are positively associated with physical activity in adults to a small degree.

**Key words:** automatic evaluations; dual-processes; non-conscious; exercise; motivation
# The association between implicit attitudes toward physical activity and physical activity behavior: A systematic review and correlational meta-analysis

Engaging in regular physical activity has been associated with reduced risk of 25 chronic health conditions (Rhodes, Janssen, Bredin, Warburton, & Bauman, 2017). Indeed, people who engage in at least 150 min of moderate to vigorous physical activity per week are less likely to experience depression and anxiety symptoms (Rebar et al., 2015), develop Alzheimer's disease (Reiner, Niermann, Jekauc, & Woll, 2013), cardiovascular diseases, type 2 diabetes, osteoporosis, and certain types of cancer (Warburton, Charlesworth, Ivey, Nettlefold, & Bredin, 2010). Despite the health benefits of physical activity being quite well known, most people engage in none or far less physical activity than is recommended for optimal health benefits. Self-reported estimates indicate that 31% of adults worldwide are physically inactive, ranging from 43% in the Americas and 35% in Europe to 17% in Southeast Asia (Hallal et al., 2012). Accelerometer-based assessments are even more concerning, with approximately 70% of 18-39-year-old adults and 90% of 60-79-year-old adults not achieving the recommended 150 minutes of weekly moderate-to-vigorous physical activity (Statistics Canada, 2015). These low physical activity prevalence rates highlight the need for new approaches to understand determinants of physical activity behavior and inform the development of effective behavior change interventions (Sheeran, Klein, & Rothman, 2017).

To this point, most efforts to understand physical activity and develop interventions have taken on a social-cognitive approach, applying, for example, the social cognitive theory (Bandura, 1977), theory of planned behavior (Ajzen, 1991), or the health action process approach (Schwarzer, 2008). These models mainly focus on cognitive behavioral determinants such as outcome expectancies, confidence in one's capacity to perform the behavior, rational formation of intentions and utilization of self-regulatory strategies (i.e., goals, planning; see Rhodes, 2017, and Rhodes, McEwan, & Rebar, 2019 for reviews).
Social cognitive theories are undoubtedly valuable for understanding physical activity.

Social cognitive theory-based interventions have a significant impact on physical activity to the order of *d* = .31; however which specific social-cognitive theory is the basis of a physical activity intervention makes no difference in behavior change efficacy (Gourlan et al., 2016). This finding may, at least partially, be the result of the extensive amount of conceptual overlap of social cognitive theories (see Ekkekakis & Zenko, 2016; Gainforth, West, & Michie, 2015; Gourlan et al., 2016; Rhodes, 2017; Sheeran et al., 2017). That interventions derived from social cognitive theories explain only a small portion of physical activity behavior suggests that these are not comprehensive models of motivation.

Relatively recently, new directions in the field have helped broaden perspectives on what types of processes impact physical activity behavior outside of social-cognitive constructs (Brand & Ekkekakis, 2018; Cheval et al., 2018; Conroy & Berry, 2017), with a major advancement being the distinction between “explicit” and “implicit” processes¹ (Rebar et al., 2016; Schinkoeth & Antoniewicz, 2017; Sheeran et al., 2016).

Usually implicit processes are presented as being housed within ‘dual process models’ such as the Reflective Impulsive Model (RIM, Hofmann, Friese, & Wiers, 2008) or the Associative-Propositional Evaluation model (APE, Gawronski & Bodenhausen, 2006).

Within these models, explicit processes are described as less efficient and more intentional, controllable and consciously regulated than implicit processes (Bargh, 1994).

These processes refer to facets of social-cognitive theories such as beliefs, expectations, intentions and the self-regulation of intention implementation (Rhodes, 2017). Implicit processes, on the contrary, are considered relatively more automatic (Bargh, 1994), such that their behavioral influences are presented as being more efficient, unintentional, uncontrollable, and less conscious than explicit processes (for a critical view of the distinction proposed here see: De Houwer & Moors, 2012; Melnikoff & Bargh, 2018).
While there is no empirical support yet toward a real distinction amongst the implicit
processes described in the literature, some authors have proposed a tripartite
classification and labeled implicit processes as either *cognitive, affective, or motivational*
(Sheeran et al., 2016; Sheeran, Gollwitzer, & Bargh, 2013). These authors have proposed
that cognitive, affective, and motivational implicit processes refer respectively to distinct
constructs such as *attentional bias, implicit attitudes and impulsive approach-avoidance
tendencies*. Among these implicit processes, the construct of implicit attitudes has
arguably received much more theoretical and empirical attention than the others in the
physical activity literature (Conroy & Berry, 2017; Rebar et al., 2016; Schinkoeth &
Antoniewicz, 2017).

Implicit attitudes were originally defined as “*a manifest as actions or judgments that
are under the control of automatically activated evaluation, without the performer’s
awareness of that causation*” (Greenwald, McGhee, & Schwartz, 1998, p. 1464).

Greenwald and Banaji (1995) acknowledged that this definition was based on previous
ones that already included automaticity notions, like the Doob's (1947) definition, which
describes attitudes as "...an implicit, drive-producing response... (p. 136)".

Methodologically, the development of a new class of computerized implicit assessment
tools in the mid-1990s marked a burgeoning in the study of implicit attitudes (Gawronski &
Brannon, 2018). Unlike explicit attitudes, which are almost exclusively assessed via self-
report, the automatic nature of implicit attitudes infers they may not be accessible via
introspective reflective and therefore cannot be captured through self-report, so are rather
assessed through implicit (or indirect) measures. The main characteristic of implicit
measures is that reflections of the targeted construct (e.g., implicit attitudes toward
physical activity) are inferred, most of the time through response times and accuracy of
performance on categorization tasks (usually computer-based). The most prominent
instrument used to this point is the Implicit Association Test (IAT; Greenwald, McGhee, &
Schwartz, 1998) or variations thereof (e.g., Single-Category IAT, Karpinski & Steinman, 2006; for a review of implicit measures see Gawronski & De Houwer, 2014).

In the physical activity literature, implicit attitudes correspond to the expression of automatic evaluations of physical activity, which are the affective experiences that arise rapidly and involuntarily when the concept of physical activity is activated in a person's mind (Conroy & Berry, 2017). The more positive a person's implicit attitude toward physical activity (i.e., positive automatic evaluations toward physical activity), the more she/he will be physically active. In theory, implicit attitudes are likely to be associated with physical activity behavior directly and indirectly, through other implicit processes or explicit processes (Gawronski & Bodenhausen, 2006; Hofmann et al., 2008; Perugini, Richetin, & Zogmaister, 2010). To this point, studies have demonstrated that implicit attitudes are directly associated with the amount of physical activity people engage in, both self-reported or measured with accelerometers, even after controlling for the variability explained by explicit processes (see Rebar et al., 2016; Schinkoeth & Antoniewicz, 2017, for reviews). Indeed, several studies found that implicit and explicit attitudes toward physical activity are mostly unrelated (e.g., Brand & Antoniewicz, 2016; Hyde, Doerksen, Ribeiro, & Conroy, 2010) and independently associated with physical activity behavior (e.g., Calitri et al., 2009; Chevance, Caudroit et al., 2017; Padin et al., 2017). However, to the best of our knowledge, there is no evidence yet of indirect associations between implicit attitudes and physical activity through interactions with explicit processes (see for example Muschalik, Elfeddali, Candel, & de Vries, 2018; Chevance, Caudroit et al., 2018), or other implicit processes (e.g., attentional bias, approach-avoidance tendencies, see Oliver & Kemps, 2018).
## The Present Study

From an initial study conducted more than 10 years ago testing the role of implicit attitudes toward physical activity behavior (Eves, Scott, Hoppé, & French, 2007), multiple studies have been conducted investigating links between these two variables (see Cheval, Sarrazin, & Radel, 2016; Rebar et al., 2016; Schinkoeth & Antoniewicz, 2017). Amidst their systematic reviews with broader aims of understanding links between implicit processes and physical activity behavior, Cheval et al. (2016) and Rebar et al. (2016) reviewed studies testing associations between physical activity and implicit attitudes, concluding that the evidence at that point was largely correlational. Schinkoeth and Antoniewicz (2017) systematically reviewed studies testing implicit attitudes and exercise behavior associations, concluding that most studies found small-to-medium effects, and noting great heterogeneity between studies. Although the state of the literature is such that aggregative reviews are being conducted and theoretical advancements established (e.g., Conroy & Berry, 2017; Rebar, 2017), there has yet to be a quantitative synthesis of the direction and magnitude of the relationship between implicit attitudes and physical activity behavior. This estimation will provide evidence to ensure that future correlational studies are appropriately powered and evidence-based theoretical propositions about the existence and size of the link between implicit attitudes and behavior.

As highlighted by previous reviews, there is some interesting heterogeneity in the literature: the measurement of implicit attitudes varies across studies, as does the measurement and quantification of physical activity behaviors (Rebar et al., 2016; Schinkoeth & Antoniewicz, 2017). Additionally, there is heterogeneity in the timing and samples of the tested correlations; retrospective, cross-sectional and prospective associations have been tested; and studies have involved a variety of populations including university students, adults from the general population, exercisers, or people living with chronic diseases. All these divergences between studies may impact the
magnitude of the association between implicit attitudes and physical activity, but have not been tested through a meta-analysis.

The aim of this study is to summarize existing evidence on the extent to which implicit attitudes toward physical activity are directly associated with physical activity behavior. According to the correlations observed in the health psychology literature (Rooke, Hine, & Thorsteinsson, 2008), effect size in the small-to-medium range (Cohen, 1992) is expected. No a priori hypotheses were formulated regarding moderator analyses.

## Methods

The systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines (Moher, Liberati, Tetzlaff, Altman, & PRISMA Group, 2009, see supplemental materials), principles of the Meta-Analysis Reporting Methods (MARS, American Psychological Association, 2008), and recommendations provided by Quintana (2015), as well as Lakens, Hilgard, & Staaks (2016). The study protocol was uploaded on the *Open Science Framework* (OSF) prior to data collection and analyses (available at https://osf.io/mgv82/) all the study materials and data are provided in supplemental materials and on OSF). Differences between the protocol, the initial pre-print of this study and peer-reviewed version are also detailed in supplemental materials.

## Search Strategy

A systematic review of the literature was performed to retrieve studies reporting both a measure of implicit attitudes toward physical activity and physical activity behavior. The search was conducted using the databases PubMed, PsycARTICLES, PsycINFO, SPORTDiscus, and Open Grey (Conn, Valentine, Cooper, & Rantz, 2003). Studies were also sourced from three relevant systematic reviews (Cheval et al., 2016; Rebar et al., 2016; Schinkoeth & Antoniewicz, 2017). Articles published in peer-review journals in French or English, up to December 2018, were included. The following combination of
terms was used: ("implicit attitudes" OR "automatic evaluations" OR "dual-processes" OR "non-conscious") AND ("Exercise" OR "Exercising" OR "Exerciser" OR "Physical activity" OR "Walking"). The inclusion screening was based on article abstracts and titles. Two independent coders (GC, PB; GC, AR) conducted searches and screening; any discrepancies were identified and resolved (see https://osf.io/mgv82/). To reduce the risk of publication bias, authors of eligible studies were contacted and asked to provide any relevant unpublished data.

Criteria for Study Inclusion

Studies reporting both measures of implicit attitudes toward physical activity and physical activity behavior were included. Implicit attitudes were defined as automatic evaluations reflecting “the affective experiences that arise rapidly and involuntarily when the concept of physical activity is activated” (Conroy & Berry, 2017). For this study, a valid measure of implicit attitudes was defined through the utilization of (i) a validated implicit measurement procedure (for a review, see Gawronski & De Houwer, 2014), and (ii) physical activity or exercise stimuli combined with positive or negative stimuli. This second criterion excluded studies assessing only other implicit processes such as attentional bias (e.g., Berry, 2006), implicit identity (e.g., Banting, Dimmock, & Lay, 2009), approach/avoidance tendencies (e.g., Cheval, Sarrazin, Isoard-Gautheur, Radel, & Friese, 2015), or implicit attitudes toward specific object such as a physical activity promotion program (e.g., Yun & Berry, 2018) or toward sedentary behavior (e.g., Chevance, Caudroit, et al., 2018), as the measure stimuli were not exclusive to positive, negative and physical activity/exercise.

Physical activity was defined, “bodily of movement that results in a substantial
increase over the resting energy expenditure” (Caspersen, Powell, & Christenson, 1985).
This broad definition allowed for inclusion of different types and intensity of physical
activity (e.g., moderate to vigorous, light, total), expressed in different units (e.g., metabolic
equivalents, number of steps per day or week, mean time per day or week), and measured
through self-reported questionnaires, as well as pedometers or accelerometers.

Cross-sectional (i.e., implicit attitudes and physical activity measured during the same session), retrospective (i.e., physical activity measured before implicit attitudes) and prospective (i.e., physical activity measured after implicit attitudes) studies were included.

Studies adopting an experimental, quasi-experimental or interventional design were included, but only data of implicit attitudes and physical activity done before any intervention/experimental manipulation were retained for the meta-analysis. Studies involving adults (>18 years old) were included without a maximum age limitation.

**Data Extraction and Management**

Effect sizes were extracted from eligible studies or retrieved post hoc from the
authors when not reported in the article. An a priori data extraction form was developed
and data were coded from each paper by three coders (GC, PEC, AR). Discrepancies
were identified and resolved by re-referencing the articles. The following information was
extracted: first author's name and publication year, characteristics of the participants,
study design, the implicit attitudes measure and scoring procedures, the physical activity
measure, the correlation (r) between implicit attitudes and physical activity and sample
size. When available, we also extracted the associations between implicit attitudes and
physical activity after controlling for the variance explained by other explicit processes
(e.g., explicit attitudes, intentions), to investigate an additive correlational pattern (Perugini,
Richetin, & Zogmaister, 2010).

**Quality Assessment**

Following the method of Molloy, O'Carroll, and Ferguson (2014), a custom tool of five
criteria were used to assess study quality including the: (i) study design (prospective study
= 1, cross-sectional or retrospective study = 0); (ii) measure of physical activity (utilization
of pedometers or accelerometers = 1, other = 0); (iii) sample size (more than 85
participants = 1, less than 85 = 0; N = 85 was chosen as the appropriate cut-off because it
is the minimum number required to have 80% power to detect a medium effect size using r
and an alpha of 0.05; Cohen, 1992); (iv) validity of physical activity assessment (validated
measure = 1, non-validated measure = 0); and (v) information regarding the reliability (e.g.,
Cronbach's alpha or split-half reliability) of the implicit measure (reliability reported = 1, not
provided = 0). The score was computed for each reported effect size and then averaged,
leading to a study score between 0 and 1, with higher score interpretable as higher study
quality. For sensitivity analyses, the study quality scores were split into a 'high' (≥ .08),
'medium' (.02 > score < .08) or 'low' (≤ .02) categorical variable.

Moderator Coding

Ten potential moderators were tested: (i) participants' mean age (i.e., treated as a continuous moderator); (ii) type of population (i.e., nominal moderator: people with chronic diseases versus participants from the general population versus University students); (iii) study design (i.e., nominal moderator: cross-sectional versus retrospective versus prospective); (iv, v) physical activity measure (i.e., nominal moderator: self-reported [this also included interview-based questionnaires and frequency of physical activity participation retrieved from someone other than the participant] versus accelerometers and pedometers); physical activity type (i.e., moderate to vigorous physical activity score versus other scores, including total physical activity, light intensity physical activity or incidental physical activity); (vi, vii) implicit attitudes assessment (i.e., nominal moderator: classification tasks versus priming tasks; IAT and relative tasks versus others tasks; (viii) study quality (i.e., treated as a continuous moderator). Two moderators were added post-registration of the protocol: (ix) given that most studies were conducted by a small set of research laboratories, one moderator corresponding to the research teams was proposed (i.e., categorical moderator: Berry et al., versus Brand et al., versus Boiché et al., versus Conroy et al., versus others), and it was explored if effect size varied according to the
study's main objective (i.e., categorical moderator: studies which aimed to explore the
correlation between implicit attitudes and physical activity versus studies with other main
aims [e.g., examining change in implicit attitudes]).

Statistical Analyses

Pearson's r was used as effect size, and Fisher's r-to-z transformation was
performed (Borenstein et al., 2009). For one study (Bluemke, Brand, Schweizer, & Kahlert,
2010), Spearman's correlations were converted to Pearson's correlations before Fisher's r-
to-z transformation according to Gilpin (1993). After the calculation, Fisher's z was
converted back to Pearson's r for reporting the average correlation and 95% confidence
interval (CI). Magnitude of effect sizes (r) were interpreted as small > .1, medium > .3, and
large > .5 (Cohen, 1992). The meta-analysis employed a robust variance estimation to
account for dependencies of having multiple effect sizes come from each study. Statistical
dependency is very common in psychology (Moeyaert et al., 2016), especially dependency
in the sampling errors that occurs if multiple measures of the variables are performed
within a same study (e.g., physical activity measured with both a questionnaire and an
accelerometer).

Robust variance estimation accounts for dependent effect sizes by estimating an
overall effect size across studies as a weighted mean of the observed effect sizes (see
Hedges et al., 2010, Tanner-Smith & Tipton, 2014; Tipton, 2015). This method allows
simultaneous analysis of multiple effect sizes per study with accurate estimates and
standard errors, even when information on the covariance of these effect sizes is
unavailable. If needed, the robust variance estimation method could also be adjusted for
"small" meta-analyses containing fewer than 40 studies (Tipton, 2015; this correction was
applied in the present study).

There are three advised options for managing with dependent effect sizes in meta-
analyses. Researchers can choose to aggregate effect sizes within-study, perform a multi-
level meta-analysis, or utilize robust variance estimation. Recently, Moeyaert et al. (2016) conducted a simulation study to compare the statistical performance of these three different approaches. They concluded that each of these options results in unbiased estimates; however, in scenarios in which there are a limited number of studies for analyses (N = 25), they recommend that the robust variance estimation method should be applied. To ensure we applied the most robust and conservative approach, we used the robust variance estimation method as the main meta-analytic approach. An aggregated meta-analysis and multi-level meta-analysis were also performed as sensitivity analyses (see below).

Traditional influence or publication bias analyses (e.g., Egger's regression test, multivariate outliers' diagnostics) are not implemented in the statistical package used to perform the robust variance estimation. To overcome this limitation, we followed the method used by Zelinsky and Shadish (2016). First, univariate outliers were inspected with the Grubbs test on all effect sizes (two iterations were performed). Then, we conducted influence and publication bias analyses using the aggregated method (one aggregated effect size per study). Within this aggregated analysis, Baujat Plot (Baujat, Mahé, Pignon, & Hill, 2002) and residual cook's distances were used to identify multivariate outliers (Viechtbauer & Cheung, 2010, see in supplemental results). Univariate and multivariate outliers detected with these methods were compared and referenced to inform the sensitivity analyses. Contour enhanced and traditional funnel plots were also performed within the aggregated meta-analyses and are provided as supplemental results.

Heterogeneity was quantified with I² for the robust variance estimation meta-analysis. An I² value of 25 was interpreted as having a low dispersal, 50 as moderate, and 75 as high dispersal (Higgins, Thompson, Deeks, & Altman, 2003). Moderator analyses using robust variance estimation and correction for small sample size were performed following Tipton (2015). Significant moderator effects were discussed only when the *df* < 4 and *p* <
.01 to prevent potential Type I error.

## Sensitivity analyses

As recommended by Greenhouse and Iyengar (2009), a set of sensitivity analyses were carried out to verify the robustness of ours estimated effect sizes. First, results from the robust variance estimation meta-analysis were compared with the aggregated (one effect size retained per study), and multi-level meta-analyses (Assink & Wibbelink, 2016). Then, combining results from the univariate and multivariate outliers' detection, the three models were computed a second time after removing the influent effect sizes. Finally, the three meta-analyses models were performed omitting effect sizes from low quality studies (i.e., quality score ≤ .02).

All analyses were carried out in R 3.2 (R Core Team, 2016) using the *robumeta* package (Fisher & Tipton, 2015) for the robust variance estimation meta-analysis, the *metaphor* package (Viechtbauer, 2010) for the multi-level meta-analysis, and the *MAc* package (Del Re & Hoyt, 2012) for the study aggregation. Analyses were performed by GC and independently replicated by PB. The dataset and script to perform the analyses are available as supplemental materials and on the OSF page of the project (https://osf.io/mgv82/).

## Results

### Systematic Review

As depicted in the flow diagram (see Figure 1), a first iteration resulted in a total of 112 extracted articles, with 2 additional included studies published after the last systematic review iteration, 3 additional included datasets from unpublished manuscripts, and one thesis identified in the grey literature. After removing duplicates and screening titles and abstract, 40 articles were extracted for full-text screening, which resulted in 26 eligible articles and 58 associations. Of the 7 authors contacted for additional data from their
published studies, 2 were unable to provide it, leading to a total of 55 effect sizes for use in
the meta-analysis.

[Insert Figure 1 about here]

Sample sizes range from N = 44 to N = 340, the majority (k = 15/26 studies) included a University student sample, used a questionnaire to estimate physical activity (k = 44/58 effect sizes), and an IAT or adapted task (i.e., SC-IAT; B-IAT) to measure implicit attitudes (k = 43/58 effect sizes). Among the 58 associations found, 21 were obtained from a cross-sectional design, 19 from a retrospective design, and 18 from a prospective design. Regarding physical activity type, half of the included effect sizes were representative of a moderate to vigorous physical activity measure (k = 34/58 effect sizes), whereas the others used total physical activity (i.e., planned exercise, as well as household, work, or commuting activity), the number of steps per day, or average activity counts measured with accelerometers.

Among the 26 independent studies, 10 examined an additive correlational pattern between implicit attitudes and physical activity after controlling the variance explained by social-cognitive constructs. Regression analyses included constructs such as intentions (controlled in 6 studies of 10), explicit attitudes (5/10), or self-belief measures such as self-efficacy or perceived behavioral control (3/10). Of these 10 studies, 6 reported positive and significant associations between implicit attitudes and physical activity after controlling for one (or more) social-cognitive construct (studies number 8, 9, 11, 12, 13, 22 in the Table 1), 5 reported non-significant associations (studies number 11, 14, 15, 20, 22), and one study reported a significant but negative correlation between implicit attitudes and physical activity (study number 21). An overview of these studies is provided in Table 1.

[Insert Table 1 about here]
**Association between Implicit Attitudes and Physical Activity**

The robust variance meta-analysis corrected for small sample size revealed that there was a positive correlation between implicit attitudes toward physical activity and physical activity behavior [r = .11, 95% CI (.05, .17), *p* < .001; see Figure 2]. There was a moderate level of heterogeneity (I² = 51%). Two univariate outliers (Oliver & Kemps, 2018; Berry et al., 2011) were identified with the Grubbs tests performed on the 55 available effect sizes. In the aggregated analysis, one multivariate outlier (Oliver & Kemps, 2018) was identified (see in supplemental results). Robust variance estimation analyses omitting outlier effect sizes lead to comparable estimates [without Oliver & Kemps, 2018: r = .11, 95% CI (.05, .17), *p* < .001; without Oliver & Kemps, 2018 and Berry et al., 2011: r = .10, 95% CI (.04, .16), *p* < .001].

[Insert Figure 2 about here]

**Sensitivity analyses**

Sensitivity analyses were conducted to test the difference between the three meta-analysis models (i.e., robust variance estimation with correction for small sample size, aggregated and multi-level meta-analyses), compare models that included and excluded potential outliers, and studies with low quality ratings. Among these 12 models (see Figure 3), the lowest estimate was obtained with the multi-level meta-analysis, excluding low quality studies [k = 47 effect sizes, r = .07, 95% CI (.02, .13), *p* < .01]. The greatest estimate was obtained with aggregated meta-analysis excluding Oliver and Kemps's study [k = 24 effect sizes, r = .12, 95% CI (.08, .17), *p* < .001]. All these 12 estimates were statistically significantly different from zero.

[Insert Figure 3 about here]
## Moderator Analyses

Ten potential moderators were tested using robust variance estimation: participants' age, characteristics, study design, physical activity measure (2 moderators), implicit attitudes measure (2 moderators), study laboratory, study quality and study objective. Results revealed that none of them were statistically significant (Table 2).

[Insert Table 2 about here]

## Discussion

Our aim was to provide a systematic review and meta-analysis of the direct correlation between implicit attitudes toward physical activity and physical activity behavior. To our knowledge, this is the first meta-analysis of this emerging area of research interest. Implicit attitudes toward physical activity were significantly correlated with physical activity behavior, with a small effect size [r = .11, 95% CI (.05, .17)], and the association was robust over the different models and sensitivity analyses performed. This finding aligns with relatively newly introduced theory to physical activity research that physical activity behavior is partially regulated by automatic processes (Brand & Ekkekakis, 2018; Cheval et al., 2018; Conroy and Berry, 2017). Across other fields of health psychology, Rooke, Hine, & Thorsteinsson (2008) found a slightly stronger correlation of r = .27 [95% CI (.21, .31)] between implicit attitudes and substance use (i.e., alcohol, cigarettes, cocaine, marijuana). Greenwald et al. (2009) reported a sub-group summary correlation of .22 [95% CI (.15, .29)] between implicit attitudes and alcohol and drug use. Taken together (see Figure 4), the findings of the present meta-analysis suggest that implicit attitudes have a smaller association with physical activity behavior (around r = .10) than those of substance use. It may be that the association is smaller because physical activity encompasses more than just a single behavior which may be goal-driven or incidental; whereas drug and alcohol use describes precise actions.
Comparatively, the correlation between implicit attitudes and physical activity seems much smaller than those observed for other motivational determinants of physical activity. Indeed, McEachan et al. (2011) found a mean *r* of .45 between intentions toward physical activity and physical activity behavior; Rhodes, Fiala, and Conner (2009) found a summary *r* of .42 between affective judgments and physical activity. Gardner, de Bruijn, and Lally (2011) reported a mean *r* of .46 between scores of the self-reported habit index and physical activity behavior. It may be that implicit attitudes truly are less associated with physical activity than these motivational processes; however, conclusions should be tempered with consideration for alternative explanations of the size of these effects.

One potential methodological rationale that may explain this discrepancy in effect sizes is the principle of correspondence (Ajzen & Fishbein, 1977; Perugini, Richetin, & Zogmaister, 2010), in that the more a construct measure shares relevant features with a behavioral measure, the stronger their correlation. In previous meta-analyses (Gardner, de Bruijn, & Lally, 2011; McEachan et al., 2011; Rhodes, Fiala, & Conner, 2009), both the motivational variable and physical activity were typically assessed via self-report questionnaires (181 of the 197 studies include in these meta-analyses used a self-report measure of physical activity). On the contrary, the present findings are based on response timed implicit measurement and either monitor or self-report based measures of behavior. Therefore, it is not necessarily surprising that the correspondence between such disparate measures (i.e., self-reported *versus* reaction time-based measure) are lower than those assessed with more convergent measures.

A second reason that might explain the small correlation between implicit attitudes and physical activity behavior is that implicit attitudes are associated with physical activity only in some conditions, for some people, and regarding specific behaviors. Indeed, associations between implicit attitudes (and more broadly other implicit processes) and physical activity are likely to vary depending on several situational, dispositional and
behavioral moderators (Friese, Hofmann, & Schmitt, 2008). Situational moderators refer to state variables that are likely to vary in short temporal scales (i.e., minutes, hours, days) or due to environmental features such as stress, mood, or social desirability. Dispositional moderators mainly refer to more trait-like variables such as personality (i.e., impulsivity) or cognitive abilities (i.e., self-regulation) which are presumably more stable over time.

Behavioral moderators correspond to the features of the specific behavior under scrutiny, such as the difference between a planned and incidental behavior. In theory, the less a behavior is under cognitive control, the stronger implicit processes' role should be in predicting that behavior. Given that only a few studies among those included in the present systematic review included moderator analyses (4 of 26 studies), it is clear that more work is needed to understand the specific conditions under which implicit attitudes are, and or are not, associated with physical activity behavior.

To illustrate, two studies indicate that the relationship between implicit attitudes and physical activity could be moderated by dispositional variables. Padin et al. (2017) found that implicit attitudes toward exercise were associated to physical activity among students with low, but not high, effortful control (one aspect of self-regulation). It may be that individuals with higher capacity for self-regulation relied less on implicit attitudes toward physical activity to guide behavior than those with lower self-regulation. In a similar fashion, Chevance, Stephan et al. (2018) showed that implicit attitudes favorable to sedentary behaviors were prospectively associated with significantly less physical activity in obese adults with low and moderate, but not high, executive functions. It may be that people with high executive functioning may be able to overcome the influence of negative implicit attitudes toward physical activity (or resist influence from positive implicit attitudes toward sedentary behavior). To our knowledge, situational or behavioral moderators of the relationship between implicit attitudes and physical activity have not been investigated yet.

Other studies investigating the conditions in which implicit attitudes are specifically
associated with physical activity are thus needed, and this includes studies investigating
the moderating effects of other implicit and explicit processes (see for example Muschalik
et al., 2018; Oliver & Kemps, 2018).

The study-level moderators explored in this study were not found to significantly
impact the correlation between implicit attitudes and physical activity. However, given the
presumed low statistical power of our moderator analyses, this does not constitute strong
evidence for null effects (Hedges & Pigott, 2004). Other studies are clearly needed to
understand how study features such as measures of implicit attitudes (i.e., type of tasks,
stimuli, and scoring algorithms) impact the correlation between implicit measures and
physical activity (see Chevance et al., 2017b; Rebar et al., 2015; Zenko & Ekkekkakis,
2019 for methodological studies). For example, reliability of implicit measures is often not
reported in the literature (only 9 studies of 26 in the present study). Researchers are thus
encouraged to systematically compute the internal consistency of their implicit measures,
using preferentially standardized methods (see Richetin, Costantini, Perugini, &
Schönbrodt, 2015) and take appropriate steps to account for poor reliability if necessary.
Although the moderator analysis of study quality rating was not statistically significant, the
sensitivity analysis excluding low quality studies reported systematically lower correlations
between implicit attitudes and physical activity (see Figure 4). This suggests that the
implicit attitudes-physical activity correlation could be overestimated due to methodological
weaknesses (i.e., self-reported measure of physical activity, cross-sectional design, limited
sample size, validity/reliability of physical activity and implicit attitudes measures). Future
multi-lab investigations could be beneficial for producing more powerful, replicable, and
rigorous studies in the field (see for example, Lai et al., 2016).
Complementary Perspectives

The present review brought to light many unresolved issues that should be
addressed as the field advances. Firstly, in comparison with traditional socio-cognitive
models (e.g., the Theory of Planned Behavior, Ajzen, 1991) or more contemporary
inclusive models (e.g., The Multi-Process Action Control Approach, Rhodes, 2017), dual-
process models describe overarching heuristics hypotheses without specifying precise
pathways between underlying theoretical constructs (Deutsch, Gawronski & Hofmann,
2017). For example, the Theory of Planned Behavior (Ajzen, 1991) describes both specific
processes (e.g., explicit attitudes, intentions) and related hypotheses (e.g., intentions
mediate the relationship between explicit attitudes and behavior). On the contrary, dual-
process models put forth claims about the types of influences that are present (e.g.,
behaviors can be framed in terms of conflict between implicit and explicit processes)
without specifying the constructs or the nature of the links between constructs (Hofmann,
Friese, & Wiers, 2008; Gawronski & Bodenhausen, 2006). Several proposals are made
below to solve this question.

In this paper we chose to align with the classification proposed by Sheeran,
Gollwitzer and Bargh (2013) that specify three different categories of implicit processes:
cognitive (e.g., attentional bias), affective (e.g., implicit attitudes), and motivational
constructs (e.g., non-conscious goal pursuit). In future, it would be interesting to
empirically test this classification, how these different implicit constructs, and whether their
relative measures, are (or are not) associated, and to what extent they independently
contribute to explain physical activity behavior (see Oliver & Kemps, 2018). For example, it
could be interesting to explore whether different measures developed to reflect implicit
attitudes share variability that is not shared with measures of other constructs (i.e.,
attentional bias), accounting for method and random residual error (see Bar-Anan &
Vianello, 2018). More specific hypotheses, such as the mediating role of impulsive
approach-avoidance tendencies in the relationship between implicit attitudes and physical activity could also be explored (see Chen & Bargh, 1999; Rotteveel et al., 2015). Such studies might help to understand the mechanisms that relates implicit attitudes to physical activity behaviors.

Additionally, there is no evidence yet determining whether implicit attitudes interact with other explicit motivational constructs to influence physical activity. For example, the Temporal Self-regulation Theory (Hall & Fong, 2007) proposes that intention-behavior gap (i.e., the variability in behavior left unexplained by intentions) could be partially explained by unfavorable implicit processes (see Cheval et al., 2015). To our knowledge, this hypothesis has been tested at least twice in prospective designs, both showing non-significant moderation effects of implicit attitudes on intention-behavior associations (Chevance, Caudroit et al., 2018; Muschalik et al., 2018). Recently, Oliver and Kemps (2018) also reported non-significant moderation effects of implicit attitudes on associations of physical activity behavior with controlled and autonomous forms of motivation (motivation constructs based on self-determination theory). Other studies are thus needed to understand the process by which implicit attitudes and explicit processes interact to explain physical activity variability. In the same vein, measuring the discrepancy between explicit and implicit attitudes measures (see Brand & Antoniewicz, 2016) could also help to understand how these two processes conjointly influence physical activity within individuals.

Beyond teasing apart the mechanism by which implicit attitudes are associated with behavior, bi-directional relationships between implicit attitudes and physical activity could also be hypothesized. For example, a recent retrospective study showed that childhood memories of physical education are associated with explicit attitudes toward physical activity and sedentary behaviors in adulthood (Ladwig, Vazou, & Ekkekakis, 2018). Moreover, studies indicate that affective responses to short bouts of exercise are
subsequently associated with affective judgments about physical activity and, possibly,
physical activity behavior directly (Ekkekakis & Dafermos, 2012; Rhodes & Kates, 2015).
As implicit attitudes are malleable over time (Hyde et al., 2012), it may be that implicit
attitudes toward physical activity vary, in both short and long term, as a consequence of
past experiences with exercise and physical activity, and these changes may ultimately
impact subsequent physical activity (see Brand & Ekkekakis, 2018; Hyde et al., 2012).
More broadly, the origins, development and determinants of implicit attitudes have not
been yet studied in the physical activity context. In future, it could be interesting to explore
how implicit attitudes toward physical activity evolve during childhood (Dunham, Baron, &
Banaji, 2008), how parents' and peers' implicit attitudes are shared in the social
environment (see Guidetti, Conner, Prestwich, & Cavazza, 2012; Sherman, Chassin,
Presson, Seo, & Macy, 2009), and what factors are prospectively associated with implicit
attitudes development and change over time.

The field is advancing such that it is becoming increasingly clearer that motivation
needs to account for competing alternative behavioral options to physical activity, and
literature is emerging on the role of implicit attitudes toward conflicting behaviors such as
sedentary behavior (i.e., time spend sitting, Tremblay et al., 2017) in the prediction of
physical activity (sometimes this behavioral choice is incorporated as a relative choice in
implicit measures of relative preferences such as the IAT). Indeed, recent theoretical
reviews have suggested that “behaviors minimizing energetic cost”, such as sedentary
behaviors or physical inactivity, could be rewarding and imped physical activity
participation (Cheval et al., 2018; Brand & Ekkekakis, 2018). In line with these hypotheses,
Chevance, Caudroit et al. (2018) found a trending negative association (p = .06) between
implicit attitudes toward sedentary behaviors (i.e., automatic evaluation of sedentary
behaviors stimuli as pleasant or unpleasant) and physical activity measured four months
later with accelerometers, after controlling for implicit attitudes toward physical activity and
additional covariates. Hence, investigating how implicit processes toward alternative
choice behaviors could negatively be associated with physical activity may extend our
understanding of original dual-process model-based hypotheses of conflict between
explicit and implicit processes (Hofmann, Friese, & Wiers, 2008).

Finally, two frameworks were recently developed to guide the experimental
manipulation of implicit processes (see Hollands, Marteau, & Fletcher, 2016; Papies,
2016). Papies (2016) pointed out that these interventions might change the features of the
environment to influence the activation of implicit processes in specific time and place (i.e.,
*cueing interventions*) and/or directly change the implicit processes that drive behaviors
(i.e., *training interventions*). To date, a handful of studies have started exploring training
interventions in the physical activity context. Retraining approach-avoidance tendencies
(Cheval, Sarrazin, Pelletier, & Friese, 2016), evaluative conditioning (Antoniewicz & Brand,
2016b), delivering tailored exercise-related messages (Berry, 2016), and mental imagery
(Markland, Hall, Duncan, & Simatovic, 2015) have all shown promise in modifying implicit
processes. However, these studies have been conducted with students in a laboratory
context, and the next step would be to examine their relevance in more ecologically valid
settings (see Chevance et al., 2019 for a null findings).

## Limitations

Conclusions from the present meta-analysis should be tempered by a number of limitations. First, the majority of the study included (15/26) were conducted among students which impact the generalizability and transferability of the findings. Future research should include more heterogeneous population samples. Second, it would have been interesting to estimate the contribution of implicit attitudes toward physical activity behavior after controlling for key explicit processes (i.e., incremental validity), as it has been done in some individual studies (e.g., Chevance et al., 2017; Conroy et al., 2010). While there is some evidence in the field that implicit attitudes are independently
associated with physical activity behavior after controlling different traditional socio-cognitive constructs (5 to 7 studies of 10 in the present systematic review), lower effect sizes could be expected between implicit attitudes and physical activity after controlling for those "explicit" processes. In the same manner, controlling for past physical activity could have added important insight into how implicit attitudes are associated with behavior change. Incorporating past behavior into other meta-analyses has shown mitigating effects of the link between psychological determinants of physical activity (i.e., intentions) and physical activity behavior (Hagger, Chatzisarantis, & Biddle, 2001). This intriguing question could not be tested in the present study given multiple physical activity behavioral measures were rare in the included studies. The role of other motivational variables and past physical activity in the link between implicit attitudes and physical activity should be more tested further in future studies (see Blanton, Burrows, & Jaccard, 2016).

Furthermore, it could be interesting in future meta-analyses to consider the measurement error (i.e., internal consistency) of implicit measures in the estimation of the mean effect size (Kurdi et al., submitted).

## Conclusion

In conclusion, this meta-analysis provides evidence that implicit attitudes toward physical activity are positively associated with physical activity behavior in adults to a small degree. The field of implicit processes is still in its infancy in the physical activity context and many other studies are needed to better understand how, for whom and in which situations, implicit attitudes are associated with physical activity behavior. Following the evolution of these process questions, it is essential to investigate behavior change theory insights such as where implicit attitudes toward physical activity come from, how implicit attitudes could be modified, and whether such modification could lead to lasting physical activity behavior change.
Ajzen, I. (1991). The theory of planned behavior. *Organizational Behavior and Human Decision Processes, 50*, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I., & Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and review of *Empirical Research, Psychological Bulletin*, 84, 888–918.

Antoniewicz, F., & Brand, R. (2014). Automatic evaluations and exercise setting preference in frequent exercisers. *Journal of Sport & Exercise Psychology, 36*, 631–636. https://doi.org/10.1123/jsep.2014-0033

Antoniewicz, F., & Brand, R. (2016a). Dropping Out or Keeping Up? Early-Dropouts, Late-Dropouts, and Maintainers Differ in Their Automatic Evaluations of Exercise Already before a 14-Week Exercise Course. *Frontiers in Psychology, 7*. https://doi.org/10.3389/fpsyg.2016.00838

Antoniewicz, F., & Brand, R. (2016b). Learning to Like Exercising: Evaluative Conditioning Changes Automatic Evaluations of Exercising and Influences Subsequent Exercising Behavior. *Journal of Sport & Exercise Psychology, 38*, 138–148. https://doi.org/10.1123/jsep.2015-0125

Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. *The Quantitative Methods for Psychology, 12*, 154–174. https://doi.org/10.20982/tqmp.12.3.p154

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. *Psychological Review, 84*, 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Banting, L. K., Dimmock, J. A., & Lay, B. S. (2009). The role of implicit and explicit components of exerciser self-schema in the prediction of exercise behaviour. *Psychology of Sport and Exercise, 10*, 80–86.
Bar-Anan, Y., & Nosek, B. A. (2014). A comparative investigation of seven indirect attitude measures. *Behavior Research Methods, 46*, 668–688.

https://doi.org/10.3758/s13428-013-0410-6

Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In *Handbook of social cognition: Basic processes; Applications, Vols. 1-2, 2nd ed* (pp. 1–40). Hillsdale, NJ, US: Lawrence Erlbaum Associates, Inc.

Baujat, B., Mahé, C., Pignon, J.-P., & Hill, C. (2002). A graphical method for exploring heterogeneity in meta-analyses: application to a meta-analysis of 65 trials. *Statistics in Medicine, 21*, 2641–2652. https://doi.org/10.1002/sim.1221

Berry, T. R. (2006). Who's Even Interested in the Exercise Message? Attentional Bias for Exercise and Sedentary-Lifestyle Related Words. *Journal of Sport and Exercise Psychology, 28*, 4–17. https://doi.org/10.1123/jsep.28.1.4

Berry, T. R., Spence, J. C., & Clark, M. E. (2011). Exercise Is In! Implicit Exercise and Sedentary-Lifestyle Bias Held by In-Groups1. *Journal of Applied Social Psychology, 41*, 2985–2998. https://doi.org/10.1111/j.1559-1816.2011.00857.x

Berry, T. R. (2016). Changes in implicit and explicit exercise-related attitudes after reading targeted exercise-related information. *Psychology of Sport and Exercise, 22*, 273–278. https://doi.org/10.1016/j.psychsport.2015.09.001

Blanton, H., Burrows, C. N., & Jaccard, J. (2016). To accurately estimate implicit influences on health behavior, accurately estimate explicit influences. *Health Psychology, 35*, 856–860. https://doi.org/10.1037/hea0000348

Blair, S. N., Haskell, W. L., Ho, P., Paffenbarger, R. S., Vranizan, K. M., Farquhar, J. W., & Wood, P. D. (1985). Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments. *American Journal of Epidemiology, 122*, 794–804.
Bluemke, M., & Friese, M. (2008). Reliability and validity of the Single-Target IAT (ST-IAT): Assessing automatic affect towards multiple attitude objects. *European Journal of Social Psychology, 38*, 977–997. https://doi.org/10.1002/ejsp.487

Bluemke, M., Brand, R., Schweizer, G., & Kahlert, D. (2010). Exercise might be good for me, but I don't feel good about it: do automatic associations predict exercise behavior? *Journal of Sport & Exercise Psychology, 32*, 137–153.

Booth, M. (2000). Assessment of physical activity: an international perspective. *Research Quarterly for Exercise and Sport, 71*, 114–120.
https://doi.org/10.1080/02701367.2000.11082794

Brand, R., & Schweizer, G. (2015). Going to the gym or to the movies?: situated decisions as a functional link connecting automatic and reflective evaluations of exercise with exercising behavior. *Journal of Sport & Exercise Psychology, 37*, 63–73.
https://doi.org/10.1123/jsep.2014-0018

Brand, R., & Antoniewicz, F. (2016). Affective Evaluations of Exercising: The Role of Automatic-Reflective Evaluation Discrepancy. *Journal of Sport & Exercise Psychology, 38*, 631–638. https://doi.org/10.1123/jsep.2016-0171

Brand, R., & Ekkekakis, P. (2018). Affective-Reflective Theory of physical inactivity and exercise. *German Journal of Exercise and Sport Research, 48*, 48–58.
https://doi.org/10.1007/s12662-017-0477-9

Bull, F. C., Maslin, T. S., & Armstrong, T. (2009). Global physical activity questionnaire (GPAQ): nine country reliability and validity study. *Journal of Physical Activity & Health, 6*, 790–804.

Calitri, R., Lowe, R., Eves, F. F., & Bennett, P. (2009). Associations between visual attention, implicit and explicit attitude and behaviour for physical activity. *Psychology & Health, 24*, 1105–1123. https://doi.org/10.1080/08870440802245306
Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. *Public Health Reports, 100*, 126-131.

Chen, M., & Bargh, J. A. (1999). Consequences of Automatic Evaluation: Immediate Behavioral Predispositions to Approach or Avoid the Stimulus. *Personality and Social Psychology Bulletin, 25*, 215-224.

https://doi.org/10.1177/0146167299025002007

Cheval, B., Radel, R., Neva, J. L., Boyd, L. A., Swinnen, S. P., Sander, D., & Boisgontier, M. P. (2018). Behavioral and Neural Evidence of the Rewarding Value of Exercise Behaviors: A Systematic Review. *Sports Medicine, 48*, 1389-1404.

https://doi.org/10.1007/s40279-018-0898-0

Cheval, B., Sarrazin, P., Isoard-Gautheur, S., Radel, R., & Friese, M. (2015). Reflective and impulsive processes explain (in)effectiveness of messages promoting physical activity: a randomized controlled trial. *Health Psychology, 34*, 10-19.

https://doi.org/10.1037/hea0000102

Cheval, B., Sarrazin, P., & Pelletier, L. (2014). Impulsive Approach Tendencies towards Physical Activity and Sedentary Behaviors, but Not Reflective Intentions, Prospectively Predict Non-Exercise Activity Thermogenesis. *PLoS ONE, 9*.

https://doi.org/10.1371/journal.pone.0115238

Cheval, B., Sarrazin, P., Pelletier, L., & Friese, M. (2016). Effect of Retraining Approach-Avoidance Tendencies on an Exercise Task: A Randomized Controlled Trial. *Journal of Physical Activity & Health, 13*, 1396-1403.

https://doi.org/10.1123/jpah.2015-0597

Cheval, B., Sarrazin, P., & Radel, R. (2016). Processus automatiques et activités physiques bénéfiques pour la santé, Abstract. *L'Année psychologique, 116*, 295-347. https://doi.org/10.4074/S0003503316000348
Chevance, G., Berry, T., Boiché, J., & Heraud, N. (2019). Changing implicit attitudes for physical activity with associative learning. *German Journal of Exercise and Sport Research*. doi:10.1007/s12662-018-0559-3

Chevance, G., Caudroit, J., Henry, T., Guerin, P., Boiché, J., & Héraud, N. (2018). Do implicit attitudes toward physical activity and sedentary behavior prospectively predict objective physical activity among persons with obesity? *Journal of Behavioral Medicine*, 41, 31–42. https://doi.org/10.1007/s10865-017-9881-8

Chevance, G., Caudroit, J., Romain, A. J., & Boiché, J. (2017). The adoption of physical activity and eating behaviors among persons with obesity and in the general population: the role of implicit attitudes within the Theory of Planned Behavior.

*Psychology, Health & Medicine*, 22, 319–324.

https://doi.org/10.1080/13548506.2016.1159705

Chevance, G., Héraud, N., Varray, A., & Boiché, J. (2017a). Change in explicit and implicit motivation toward physical activity and sedentary behavior in pulmonary rehabilitation and associations with postrehabilitation behaviors. *Rehabilitation Psychology*, 62, 119–129. https://doi.org/10.1037/rep0000137

Chevance, G., Héraud, N., Guerrieri, A., Rebar, A., & Boiché, J. (2017b). Measuring implicit attitudes toward physical activity and sedentary behaviors: Test-retest reliability of three scoring algorithms of the Implicit Association Test and Single Category-Implicit Association Test. *Psychology of Sport and Exercise*, 31, 70–78.

https://doi.org/10.1016/j.psychsport.2017.04.007

Chevance, G., Stephan, Y., Héraud, N., & Boiché, J. (2018). Interaction between self-regulation, intentions and implicit attitudes in the prediction of physical activity among persons with obesity. *Health Psychology*, 37, 257–261.

https://doi.org/10.1037/hea0000572

Cohen, J. (1992). A power primer. *Psychological Bulletin*, 112(1), 155–159.
Conn, V. S., Valentine, J. C., Cooper, H. M., & Rantz, M. J. (2003). Grey Literature in Meta-Analyses. *Nursing Research, 52*, 256-261. doi:10.1097/00006199-200307000-00008

Conroy, D. E., & Berry, T. R. (2017). Automatic Affective Evaluations of Physical Activity. *Exercise and Sport Sciences Reviews, 45*, 230-237.
https://doi.org/10.1249/JES.0000000000000120

Conroy, D. E., Hyde, A. L., Doerksen, S. E., & Ribeiro, N. F. (2010). Implicit attitudes and explicit motivation prospectively predict physical activity. *Annals of Behavioral Medicine, 39*, 112-118. https://doi.org/10.1007/s12160-010-9161-0

De Houwer, J. (2003). The Extrinsic Affective Simon Task. *Experimental Psychology, 50*, 77-85. https://doi.org/10.1026/1618-3169.50.2.77

De Houwer, J. D., & Moors, A. (2012). How to Define and Examine Implicit Processes? In R. Proctor & J. Capaldi (Eds.), *Implicit and explicit processes in the psychology of science* (pp. 183-198). New York: Oxford University Press.

Denman, D.C., & Baldwin, A.S. (2015). Implicit influences on physical activity: understanding failures to exercise when exercise is intended (master's thesis).
Dedman College of Humanities & Sciences, Southern Methodist University, Dallas, United States.

Dunham, Y., Baron, A. S., & Banaji, M. R. (2008). The development of implicit intergroup cognition. *Trends in Cognitive Sciences, 12*, 248-253.
https://doi.org/10.1016/j.tics.2008.04.006

Ekkekakis, P., & Dafermos, M. (2012). Exercise is a many-splendored thing, but for some it does not feel so splendid: Staging a resurgence of hedonistic ideas in the quest to understand exercise behavior. In *The Oxford handbook of exercise psychology* (pp. 295-333). New York, NY, US: Oxford University Press.
https://doi.org/10.1093/oxfordhb/9780195394313.013.0016
Endrighi, R., Basen-Engquist, K., Szeto, E., Perkins, H., Baum, G., Cox-Martin, M., ...

Waters, A. J. (2016). Self-reported and automatic cognitions are associated with exercise behavior in cancer survivors. *Health Psychology*, 35, 824–828.

https://doi.org/10.1037/hea0000385

Eves, F. F., Scott, E. J., Hoppé, R., & French, D. P. (2007). Using the affective priming paradigm to explore the attitudes underlying walking behaviour. *British Journal of Health Psychology*, 12, 571–585. https://doi.org/10.1348/135910706X153775

Fazio, R. H. (2001). On the automatic activation of associated evaluations: An overview.
*Cognition and Emotion*, 15, 115–141.

Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behaviour? Empirical evidence for the moderating role of opportunity, motivation, and process reliance. *European Review of Social Psychology*, 19, 285–338. https://doi.org/10.1080/10463280802556958

Gainforth, H. L., West, R., & Michie, S. (2015). Assessing Connections Between Behavior Change Theories Using Network Analysis. *Annals of Behavioral Medicine*, 49, 754–761. https://doi.org/10.1007/s12160-015-9710-7

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 behaviours. *Annals of Behavioral Medicine, 42*, 174–187.

https://doi.org/10.1007/s12160-011-9282-0

Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: an integrative review of implicit and explicit attitude change.

*Psychological Bulletin*, 132, 692–731. https://doi.org/10.1037/0033-2909.132.5.692

Gerber, M., Ehrbar, J., Brand, R., Antoniewicz, F., Brand, S., Colledge, F., ... Ludyga, S. (2018). Implicit attitudes towards exercise and physical activity behaviour among in-
patients with psychiatric disorders. *Mental Health and Physical Activity*, 15, 71–77.

https://doi.org/10.1016/j.mhpa.2018.08.001

Gilpin, A. R. (1993). Table for Conversion of Kendall’S Tau to Spearman’S Rho Within the Context of Measures of Magnitude of Effect for Meta-Analysis. *Educational and Psychological Measurement*, 53, 87–92.

https://doi.org/10.1177/0013164493053001007

Gill, D. P., Jones, G. R., Zou, G. Y., & Speechley, M. (2008). The Phone-FITT: a brief physical activity interview for older adults. *Journal of Aging and Physical Activity*, 16, 292–315.

Godin, G., & Shephard, R. J. (1985) A simple method to assess exercise behavior in the community. *Canadian Journal of Applied Sport Sciences*, 10, 141–146

Gourlan, M., Bernard, P., Bortolon, C., Romain, A. J., Lareyre, O., Carayol, M., ... Boiché, J. (2016). Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials. *Health Psychology Review*, 10, 50–66. https://doi.org/10.1080/17437199.2014.981777

Greenhouse, J. B., & Iyengar, S. (2009). Sensitivity analysis and diagnostics. In *The handbook of research synthesis and meta-analysis, 2nd ed* (pp. 417–433). New York, NY, US: Russell Sage Foundation.

Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity.

*Journal of Personality and Social Psychology*, 97, 17–41.

https://doi.org/10.1037/a0015575

Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the implicit association test: I. An improved scoring algorithm. *Journal of Personality and Social Psychology, 85*, 197–216.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual
differences in implicit cognition: the implicit association test. *Journal of Personality
and Social Psychology, 74*, 1464–1480.

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. *Psychological Review, 102*, 4–27. https://doi.org/10.1037/0033-295X.102.1.4

Guidetti, M., Conner, M., Prestwich, A., & Cavazza, N. (2012). The transmission of attitudes towards food: twofold specificity of similarities with parents and friends. *British Journal of Health Psychology, 17*, 346–361. https://doi.org/10.1111/j.2044-8287.2011.02041.x

Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A Meta-Analytic Review of the Theories of Reasoned Action and Planned Behavior in Physical Activity: Predictive Validity and the Contribution of Additional Variables. *Journal of Sport and Exercise Psychology, 24*, 3-32. https://doi.org/10.1123/jsep.24.1.3

Hall, P. T., & Fong, G. T. (2007). Temporal self-regulation theory: A model for individual health behavior. *Health Psychology Review, 1*, 6–52.
https://doi.org/10.1080/17437190701492437

Hallal, P. C., Andersen, L. B., Bull, F. C., Guthold, R., Haskell, W., Ekelund, U., & Lancet Physical Activity Series Working Group. (2012). Global physical activity levels: surveillance progress, pitfalls, and prospects. *Lancet, 380*, 247–257.
https://doi.org/10.1016/S0140-6736(12)60646-1

Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. *Research Synthesis Methods, 1*, 39–65.
Hedges, L. V., & Pigott, T. D. (2004). The power of statistical tests for moderators in meta-analysis. *Psychological Methods*, *9*, 426–445. https://doi.org/10.1037/1082-989X.9.4.426

Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. *BMJ*, *327*, 557–560.
https://doi.org/10.1136/bmj.327.7414.557

Hofmann, W., Friese, M., & Wiers, R. W. (2008). Impulsive versus reflective influences on health behavior: a theoretical framework and empirical review. *Health Psychology Review*, *2*, 111–137. https://doi.org/10.1080/17437190802617668

Hollands, G. J., Marteau, T. M., & Fletcher, P. C. (2016). Non-conscious processes in changing health-related behaviour: a conceptual analysis and framework. *Health Psychology Review*, *10*, 381–394. https://doi.org/10.1080/17437199.2015.1138093

Hyde, A. L., Elavsky, S., Doerksen, S. E., & Conroy, D. E. (2012). The Stability of Automatic Evaluations of Physical Activity and Their Relations With Physical Activity. *Journal of Sport and Exercise Psychology*, *34*, 715–736.
https://doi.org/10.1123/jsep.34.6.715

Karpinski, A., & Steinman, R. B. (2006). The single category implicit association test as a measure of implicit social cognition. *Journal of Personality and Social Psychology*, *91*, 16–32. https://doi.org/10.1037/0022-3514.91.1.16

Kurdi, B., Seitchik, A. E., Axt, J., Carroll, T., Karapetyan, A., Kaushik, N., ... Banaji, M. R. (2018). Relationship between the Implicit Association Test and intergroup behavior: A meta-analysis. *PsyArXiv*. https://doi.org/10.31234/osf.io/582gh

Ladwig, M. A., Vazou, S., & Ekkekakis, P. (2018). “My Best Memory Is When I Was Done with It”: PE Memories Are Associated with Adult Sedentary Behavior. *Translational Journal of the American College of Sports Medicine*, *3*, 119.
https://doi.org/10.1249/TJX.000000000000067
Lakens, D., Hilgard, J., & Staaks, J. (2016). On the reproducibility of meta-analyses: Six practical recommendations. *BMC Psychology*, 4. doi:10.1186/s40359-016-0126-3

Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., ... Nosek, B. A. (2016). Reducing implicit racial preferences: II. Intervention effectiveness across time. *Journal of Experimental Psychology. General*, 145, 1001–1016.
https://doi.org/10.1037/xge0000179

Markland, D., Hall, C. R., Duncan, L. R., & Simatovic, J. (2015). The effects of an imagery intervention on implicit and explicit exercise attitudes. *Psychology of Sport and Exercise*, 17, 24–31. https://doi.org/10.1016/j.psychsport.2014.11.007

Melnikoff, D. E., & Bargh, J. A. (2018). The Mythical Number Two. *Trends in Cognitive Sciences*, 22, 280–293. https://doi.org/10.1016/j.tics.2018.02.001

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. *PLoS Medicine*, 6, e1000097. https://doi.org/10.1371/journal.pmed.1000097

Molloy, G. J., O'Carroll, R. E., & Ferguson, E. (2014). Conscientiousness and medication adherence: a meta-analysis. *Annals of Behavioral Medicine*, 47, 92–101.
https://doi.org/10.1007/s12160-013-9524-4

Moeyaert, M., Ugille, M., Beretvas, S. N., Ferron, J., Bunuan, R., & Noortgate, W. V. (2016). Methods for dealing with multiple outcomes in meta-analysis: A comparison between averaging effect sizes, robust variance estimation and multilevel meta-analysis. *International Journal of Social Research Methodology*, 20, 559-572.
doi:10.1080/13645579.2016.1252189

Muschalik, C., Elfeddali, I., Candel, M. J. J. M., & de Vries, H. (2018). A longitudinal study on how implicit attitudes and explicit cognitions synergistically influence physical activity intention and behavior. *BMC Psychology*, 6, 18.
https://doi.org/10.1186/s40359-018-0229-0
Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. *Social Cognition*, **19**, 625–666. https://doi.org/10.1521/soco.19.6.625.20886

Oliver, S., & Kemps, E. (2018). Motivational and implicit processes contribute to incidental physical activity. *British Journal of Health Psychology*, **23**, 820–842.
https://doi.org/10.1111/bjhp.12318

Padin, A. C., Emery, C. F., Vasey, M., & Kiecolt-Glaser, J. K. (2017). Self-Regulation and Implicit Attitudes Toward Physical Activity Influence Exercise Behavior. *Journal of Sport & Exercise Psychology*, **39**, 237–248. https://doi.org/10.1123/jsep.2017-0056

Papies, E. K. (2016). Health goal priming as a situated intervention tool: how to benefit from nonconscious motivational routes to health behaviour. *Health Psychology Review*, **10**, 408–424. https://doi.org/10.1080/17437199.2016.1183506

Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. *Journal of Personality and Social Psychology*, **89**, 277–293. doi: 10.1037/0022-3514.89.3.277

Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2008). Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. *Journal of Clinical Epidemiology*, **61**, 991–996.
https://doi.org/10.1016/j.jclinepi.2007.11.010

Perugini, M. Richetin, J., & Zogmaister, C. (2010). Prediction of behavior. In B. Gawronski, & K. Payne (Eds.), *Handbook of Implicit Social Cognition: Measurement, Theory, and Applications* (255-277). New York: Guilford.

Quintana, D. S. (2015). From pre-registration to publication: a non-technical primer for conducting a meta-analysis to synthesize correlational data. *Frontiers in Psychology, 6*. https://doi.org/10.3389/fpsyg.2015.01549
Rebar, A. L. (2017). Automatic Regulation Used in Sport and Exercise Research. *Oxford Research Encyclopedia of Psychology*.

doi:10.1093/acrefore/9780190236557.013.231

Rebar, A. L., Dimmock, J. A., Jackson, B., Rhodes, R. E., Kates, A., Starling, J., & Vandelanotte, C. (2016). A systematic review of the effects of non-conscious regulatory processes in physical activity. *Health Psychology Review, 10*, 395–407.

https://doi.org/10.1080/17437199.2016.1183505

Rebar, A. L., Stanton, R., Geard, D., Short, C., Duncan, M. J., & Vandelanotte, C. (2015). A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. *Health Psychology Review, 9*, 366–378.

https://doi.org/10.1080/17437199.2015.1022901

Rebar, A. L., Ram, N., & Conroy, D. E. (2015). Using the EZ-Diffusion Model to Score a Single-Category Implicit Association Test of Physical Activity. *Psychology of Sport and Exercise, 16*, 96–105. https://doi.org/10.1016/j.psychsport.2014.09.008

Reiner, M., Niermann, C., Jekauc, D., & Woll, A. (2013). Long-term health benefits of physical activity--a systematic review of longitudinal studies. *BMC Public Health, 13*,

813. https://doi.org/10.1186/1471-2458-13-813

Rhodes, R. E., Mcewan, D., & Rebar, A. L. (2019). Theories of physical activity behaviour change: A history and synthesis of approaches. *Psychology of Sport and Exercise*.

doi:10.1016/j.psychsport.2018.11.010

Rhodes, R. E. (2017). The Evolving Understanding of Physical Activity Behavior: A Multi-Process Action Control Approach. In A. J. Elliot (Ed.), *Advances in Motivation Science* (Vol. 4, pp. 171–205). Elsevier.

https://doi.org/10.1016/bs.adms.2016.11.001

Rhodes, R. E., Fiala, B., & Conner, M. (2009). A review and meta-analysis of affective judgments and physical activity in adult populations. *Annals of Behavioral Medicine*:
A Publication of the Society of Behavioral Medicine, 38, 180–204.

https://doi.org/10.1007/s12160-009-9147-y

Rhodes, R. E., Janssen, I., Bredin, S. S. D., Warburton, D. E. R., & Bauman, A. (2017).

Physical activity: Health impact, prevalence, correlates and interventions.

*Psychology & Health*, 32, 942–975.

https://doi.org/10.1080/08870446.2017.1325486

Rhodes, R. E., & Kates, A. (2015). Can the Affective Response to Exercise Predict Future
Motives and Physical Activity Behavior? A Systematic Review of Published

Evidence. *Annals of Behavioral Medicine: A Publication of the Society of Behavioral
Medicine*, 49, 715–731. https://doi.org/10.1007/s12160-015-9704-5

Richetin, J., Costantini, G., Perugini, M., & Schönbrodt, F. (2015). Should We Stop

Looking for a Better Scoring Algorithm for Handling Implicit Association Test Data?
Test of the Role of Errors, Extreme Latencies Treatment, Scoring Formula, and
Practice Trials on Reliability and Validity. *PloS ONE*, *10*, e0129601.

https://doi.org/10.1371/journal.pone.0129601

Rooke, S. E., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance
use: a meta-analysis. *Addictive Behaviors*, *33*, 1314–1328.

https://doi.org/10.1016/j.addbeh.2008.06.009

Rosenbaum, S., Ward, P. B., & International Working Group. (2016). The Simple Physical
Activity Questionnaire. *The Lancet. Psychiatry*, 3, e1.

https://doi.org/10.1016/S2215-0366(15)00496-4

Rotteveel, M., Gierholz, A., Koch, G., van Aalst, C., Pinto, Y., Matzke, D., ...
Wagenmakers, E.-J. (2015). On the automatic link between affect and tendencies to
approach and avoid: Chen and Bargh (1999) revisited. *Frontiers in Psychology, 6*.

https://doi.org/10.3389/fpsyg.2015.00335
Sallis, J.F., Haskell, W.L. & Wood, P.D. (1985). Physical activity assessment methodology in the Five-City Project. *American Journal of Epidemiology*, 121, 91–106.

Schinkoeth, M., & Antoniewicz, F. (2017). Automatic Evaluations and Exercising: Systematic Review and Implications for Future Research. *Frontiers in Psychology*, 8. https://doi.org/10.3389/fpsyg.2017.02103

Schwarzer, R. (2008). Modeling Health Behavior Change: How to Predict and Modify the Adoption and Maintenance of Health Behaviors. *Applied Psychology*, 57, 1–29.
https://doi.org/10.1111/j.1464-0597.2007.00325.x

Sheeran, P., Bosch, J. A., Crombez, G., Hall, P. A., Harris, J. L., Papies, E. K., & Wiers, R. W. (2016). Implicit processes in health psychology: Diversity and promise. *Health Psychology*, 35, 761–766. https://doi.org/10.1037/hea0000409

Sheeran, P., Gollwitzer, P. M., & Bargh, J. A. (2013). Nonconscious processes and health.
*Health Psychology*, 32, 460–473. https://doi.org/10.1037/a0029203

Sheeran, P., Klein, W. M. P., & Rothman, A. J. (2017). Health Behavior Change: Moving from Observation to Intervention. *Annual Review of Psychology*, 68, 573–600.
https://doi.org/10.1146/annurev-psych-010416-044007

Sherman, S. J., Chassin, L., Presson, C., Seo, D.-C., & Macy, J. T. (2009). The Intergenerational Transmission of Implicit and Explicit Attitudes Toward Smoking. *Journal of Experimental Social Psychology*, 45, 313.
https://doi.org/10.1016/j.jesp.2008.09.012

Sriram, N., & Greenwald, A. G. (2009). The Brief Implicit Association Test. *Experimental Psychology*, 56, 283–294. https://doi.org/10.1027/1618-3169.56.4.283

Tanner-Smith, E., & Tipton, E. (2014). Robust variance estimation with dependent effect sizes: Practical considerations and a software tutorial in Stata and SPSS. *Research Synthesis Methods*, 5, 13–30.
Tipton, E. (2015). Small sample adjustments for robust variance estimation with meta-regression. *Psychological Methods*, 20, 375–393.

Tremblay, M. S., Aubert, S., Barnes, J. D., Saunders, T. J., Carson, V., Latimer-Cheung, A. E., ... on behalf of SBRN Terminology Consensus Project Participants. (2017). Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. *International Journal of Behavioral Nutrition and Physical Activity*, 14, 75. https://doi.org/10.1186/s12966-017-0525-8

van Hees, V. T., Gorzelniak, L., Dean León, E. C., Eder, M., Pias, M., Taherian, S., ...
Brage, S. (2013). Separating movement and gravity components in an acceleration
signal and implications for the assessment of human daily physical activity. *PloS
One*, 8, e61691. https://doi.org/10.1371/journal.pone.0061691

Warburton, D. E., Charlesworth, S., Ivey, A., Nettlefold, L., & Bredin, S. S. (2010). A systematic review of the evidence for Canada's Physical Activity Guidelines for Adults. *The International Journal of Behavioral Nutrition and Physical Activity*, 7, 39.

https://doi.org/10.1186/1479-5868-7-39

Wendel-Vos, G. C. W., Schuit, A. J., Saris, W. H. M., & Kromhout, D. (2003).
Reproducibility and relative validity of the short questionnaire to assess health-
enhancing physical activity. *Journal of Clinical Epidemiology*, *56*, 1163–1169.

Yun, L., & Berry, T. R. (2018). Examining implicit cognitions in the evaluation of a
community-wide physical activity program. *Evaluation and Program Planning*, *69*,
10–17. https://doi.org/10.1016/j.evalprogplan.2018.04.001

Zelinsky, N. A., & Shadish, W. (2016). A demonstration of how to do a meta-analysis that combines single-case designs with between-groups experiments: The effects of choice making on challenging behaviors performed by people with disabilities.

*Developmental Neurorehabilitation*, 21(4), 266-278.

doi:10.3109/17518423.2015.1100690
Zenko, Z., & Ekkekakis, P. (2019). Internal consistency and validity of measures of automatic exercise associations. *Psychology of Sport and Exercise, 43*, 4-15.
doi:10.1016/j.psychsport.2018.12.005
¹We used the term implicit here knowingly that there is no consensus yet in the literature regarding terminology. The term “implicit” can also refer to other terms like: type- 1 processes, associative, automatic, impulsive; while the term “explicit” refers to: type-2 processes, propositional, controlled, reflective. Although we acknowledge the controversy, this debate of terminology is not within the scope of this review and will not be further discussed.
<table><caption>Table 1. Studies identified in the systematic review</caption><thead><tr><th>Study</th><th>Participants</th><th>Design<sup>*</sup></th><th>Implicit attitudes</th><th>Physical activity</th><th>Direct correlation</th><th>Additive correlational pattern<sup>°</sup></th></tr></thead><tbody><tr><td>1 Antoniewicz &amp; Brand 2014<sup>*</sup></td><td>N = 72 graduate sport and exercise students<br>M<sub>age</sub> = 26 ± 9 years<br>43% female<br>M<sub>PA</sub> = 305 ± 190 min of weekly exercise</td><td>Cross-sectional</td><td>Task: Affective Misattribution Procedure (AMP, Payne et al., 2005)<br>Stimuli: photographs, grey rectangles (controls);<br>Primes: fitness center scenarios<br>Scoring procedure: difference between the proportion of ideographs evaluated positively after each type of prime (Payne et al., 2005)<br>Reliability: Internal consistency not reported</td><td>Measure: Self-report questionnaire (constructed for the study)<br>Score: Total time (min) of exercise per week<br>Type: MVPA</td><td>r = .01 (N = 72)</td><td>Not examined, not a study objective</td></tr><tr><td>2 Antoniewicz &amp; Brand, 2016<sup>*</sup></td><td>N = 88 exercise program attendants<br>M<sub>age</sub> = 25 ± 7 years<br>51% female</td><td>Association 1: Cross-sectional<br>Association 2: Prospective (14 weeks)</td><td>Task: Pictorial Brief Implicit Association Test (BIAT, Sriram and Greenwald, 2009)<br>Stimuli: pictures and emoticons<br>Categories: "exercise" and "nonsports" activities (pictures); "good" and "bad" (emoticons)<br>Scoring procedure: D-Score (Greenwald, Nosek, &amp; Banaji, 2003)<br>Reliability: Internal consistency not reported</td><td>Measure (association 1): self-report questionnaire (constructed for the study)<br>Score: Total time (min) of exercise per week<br>Association 2 (prospective): Measure (association 2): instructor-recorded session attendance for exercise program<br>Score: exercise adherence to a 14-week exercise program<br>Type: MVPA (association 1 and 2)</td><td>r = .03 (N = 86)<br>r = .16 (N = 88)</td><td>Not examined, not a study objective</td></tr></tbody></table>
<table><tr><td>3 Berry et al., 2011*</td><td>N = 53<br>undergraduate<br>university students<br>M<sub>age</sub> = 22 ± 5 years<br>74% female</td><td>Cross-sectional</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998)<br>Stimuli: words<br>Categories: "exerciser", "couch potato", "good", and "bad"<br>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)<br>Reliability: Internal consistency not reported</td><td>Measure: self-report questionnaire (The Godin Leisure Time Exercise Questionnaire, GLTEQ; Godin & Shephard, 1985)<br>Score: frequency and intensity of MVPA bouts 15 min or longer expressed in METs of a typical week<br>Type: MVPA</td><td>r = .38<br>(N = 53)</td><td>Not examined, not a study objective</td></tr><tr><td>4 Berry, 2016*</td><td>N = 155 university students (enrolled in first year psychology class)<br>M<sub>age</sub> = 19 ± 2 years<br>70% female</td><td>Cross-sectional</td><td>Task: Two Go/NoGo Association Task (GNAT; Nosek & Banaji, 2001)<br>Stimuli: words<br>Categories: "exercise"; generic, "good" (affective [fun] and instrumental [fit]), and "bad" (affective [boring], and instrumental [unfit])<br>Scoring procedure: difference between M<sub>RT</sub> of trials with "exercise" + "good" as a category and M<sub>RT</sub> of trials with "exercise" + "bad" as a category<br>Reliability: ICCs ranged from .72 to .85</td><td>Measure: self-report questionnaire (GLTEQ; Godin & Shepard, 1985)<br>Score: frequency and intensity of MVPA bouts 15 min or longer expressed in METs of a typical week<br>Type: MVPA</td><td>GNAT affective valence: r = .05<br>GNAT instrumental valence: r = .25<br>(all N's = 131)</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>5</td><td>Bluemke et al., 2010 *</td><td>N = 94 university students<br/>M<sub>age</sub> = 23 ± 3 years<br/>50% female</td><td>Cross-sectional</td><td>Task: Evaluative priming procedure (Eves, Scott, Hoppe, &amp; French, 2007)<br/>Stimuli: words<br/>Primes: exercise-specific or generic verbs and adjectives<br/>Scoring procedure: M<sub>RT</sub> to categorize positive and negative words before exercise prime, standardized (divided) by pooled SD<sub>RT</sub><br/>Reliability: not reported</td><td>Measure: self-report questionnaire (constructed for the study)<br/>Score: Total time (min) of weekly exercise for a typical week<br/>Type: MVPA</td><td>Spearman rho = .33 (N = 85)</td><td>Not examined, not a study objective</td></tr><tr><td>6</td><td>Brand &amp; Schweizer, 2015 *</td><td>N = 74 persons recruited at a university campus<br/>Men: M<sub>age</sub> = 23 ± 4 years<br/>Women: 26 ± 4 years<br/>41% female<br/>M<sub>PA</sub> = 101 ± 118 min of weekly exercise</td><td>Cross-sectional</td><td>Task: Evaluative priming procedure (Bluemke et al., 2010)<br/>Stimuli: words<br/>Primes: exercise-specific or generic verbs and adjectives<br/>Scoring procedure: M<sub>RT</sub> to categorize positive and negative words before exercise prime<br/>Reliability: not reported</td><td>Measure: self-report questionnaire (constructed for the study)<br/>Score: Total time (min) of exercise only (i.e., sportive activities) for a typical week<br/>Type: MVPA</td><td>r = .21 (N = 74)</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>7</td><td>Brand & Antoniewicz, 2016</td><td>N = 44 fitness club exercisers<br>M<sub>age</sub> = 41 ± 14 years<br>41% female<br>M<sub>PA</sub> = 1.7 visits to a fitness club per week over 14 weeks</td><td>Retrospective (14 weeks)<br>Task: Single Target Implicit Association Test (ST-IAT; Bluemke & Friese, 2008)<br>Stimuli: photographs and emoticons<br>Categories: "exercise" (photographs); "good" and "bad" (emoticons)<br>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)<br>Reliability: not reported</td><td>Measure: recording of club's check-in database<br>r = .23 (N = 44)<br>Score: number of visits in a fitness club over a 14-week period<br>Type: MVPA</td><td>Not examined, not a study objective</td></tr><tr><td>8</td><td>Calitri et al., 2009</td><td>N = 125 British university students<br>M<sub>age</sub> = 23 ± 6 years<br>72% female</td><td>Retrospective (1 week)<br>Task: Extrinsic Affective Simon Task (EAST, De Houwer, 2003)<br>Stimuli: words<br>Primes: "exercise", control, "positive", and "negative"<br>Scoring procedure: Difference of M<sub>RT</sub> of trials with "exercise" + "negative" as a category and M<sub>RT</sub> of trials with "exercise" + "positive" as a category<br>Reliability: Split-half adjusted r = .40</td><td>Measure: interview (7-day PAR interview; Sallis et al., 1985)<br>r = .22 (N = 98)<br>Score: type, frequency, intensity and duration of 7 days for PA bouts longer than 10 min, expressed in METs per week<br>Type: MVPA</td><td>Additive pattern examined<br>Result: Implicit attitudes were significantly associated with physical activity (β = .19) after controlling for attentional bias (β = .25), explicit instrumental (β = -.13) and affective (β = .25) attitudes and intentions (β = .10) toward physical activity</td></tr></table>
<table><tr><td>9</td><td>Chevance<br>Caudroit et<br>al., 2018</td><td>N = 76 persons with<br>obesity<br>M<sub>age</sub> = 56 ± 12<br>years<br>65% female<br>M<sub>BMI</sub> = 39 ± 7 kg/m<sup>2</sup><br>M<sub>PA</sub> = 17 ± 16 min<br>of weekly MVPA</td><td>Prospective<br>(4 months)<br>Task: Single Category Implicit<br>Association Test (SC-IAT; Karpinski<br>& Steinman, 2006)<br>Stimuli: words<br>Categories: “physical activity”,<br>“positive”, and “negative”<br>Scoring procedure: DW-<br>Score (Chevance et al., 2017;<br>Richetin et al., 2015)<br>Reliability: not reported</td><td>Measure: Tri-axial<br>accelerometer (van Hees et<br>al., 2013)<br>Score: Time (min) of MVPA<br>over 7 days<br>Type: MVPA</td><td>r = .15<br>(N = 76)</td><td>Additive pattern<br>examined<br>Result: Implicit attitudes<br>were significantly<br>associated with<br>physical activity (β =<br>.21) after controlling for<br>age (β = -.45), BMI (β =<br>-.33), past physical<br>activity (β = .25),<br>intentions toward<br>physical activity (β = -<br>.09), intentions to limit<br>sedentary behavior (β =<br>.14), and implicit<br>attitudes toward<br>sedentary behavior (β =<br>-.18)</td></tr></table>
<table><tr><td>10</td><td>Chevance et al., 2019</td><td>N = 79 person with respiratory diseases<br/>M<sub>age</sub> = 62 ± 6 years<br/>47% female<br/>M<sub>BMI</sub> = 31 ± 7 kg/m<sup>2</sup></td><td>Cross-sectional</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998)<br/>Stimuli: words<br/>Categories: “physical activity”, “sedentary behavior”, “positive”, and “negative”<br/>Scoring procedure: DW-Score (Chevance et al., 2017; Richetin et al., 2015)<br/>Reliability: Split-half r = .82</td><td>Measure: self-report questionnaire (GLTEQ; Godin & Shepard, 1985)<br/>Score: frequency and intensity of MVPA bouts 15 min or longer over the previous week-end (compilation score)<br/>Type: total PA</td><td>r = .02<br/>(N = 78)</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>11</td><td>Chevance, Caudroit et al., 2017</td><td>Sample I:<br/>N = 94 adults from the general population<br/>M<sub>age</sub> = 35 ± 9 years<br/>65% female<br/>M<sub>PA</sub> = 317 min of weekly total PA<br/>Sample II:<br/>N = 59 obese adults<br/>M<sub>age</sub> = 51 ± 12 years<br/>74% female<br/>M<sub>PA</sub> = 195 min of weekly total PA</td><td>Cross-sectional</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998)<br/>Stimuli: pictures and words<br/>Categories: "physical activity" and "physical inactivity" (pictures); words: "positive" and "negative"<br/>Scoring procedure: D-Score<br/>(Greenwald, Nosek, & Banaji, 2003)<br/>Reliability: not reported</td><td>Measure: self-report questionnaire (Global Physical Activity Questionnaire; Bull, Maslin, & Armstrong, 2009)<br/>Score: Time (min) per week of total PA (planned exercise, commuting, activities at work, household and leisure time activities) across a typical week<br/>Type: total PA</td><td>Sample I<br/>(adults from the general population):<br/>r = .14<br/>(N = 94)<br/>Sample II<br/>(obese adults): r = .24<br/>(N = 59)</td><td>Additive pattern examined<br/>Result: (sample I)<br/>Implicit attitudes were not significantly associated with physical activity (β not reported) after controlling for explicit attitudes (β = .25), perceived behavioral control (β = .46), social norms and intentions (β not reported)<br/>(sample II)<br/>Implicit attitudes were significantly associated with physical activity (β = .25) after controlling for explicit attitudes (β = .38), perceived behavioral control, social norms and intentions (β not reported)</td></tr></table>
<table><tr><td>12</td><td>Chevance, Héraud et al., 2017a<br/>*<br/>N = 119 person with respiratory diseases<br/>M<sub>age</sub> = 62 ± 9 years<br/>62% female<br/>M<sub>BMI</sub> = 29 ± 7 kg/m<sup>2</sup></td><td>Prospective (6 months)</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998)<br/>Stimuli: words<br/>Categories: “physical activity”, “sedentary behavior”, “positive”, and “negative”<br/>Scoring procedure: DW-Score (Chevance et al., 2017b; Richetin et al., 2015)<br/>Reliability: Split-half r = .97</td><td>Measure: self-report questionnaire (Phone-Fitt Questionnaire; Gill, Jones, Zou, & Speechley, 2008)<br/>Score: type, frequency and duration for recreational activities over a for a typical week (compilation score)<br/>Type: MVPA</td><td>r = .32<br/>(N = 54)</td><td>Additive pattern examined<br/>Result: Implicit attitudes were significantly associated with physical activity (β = .29) after controlling for exercise tolerance (β = .43) and intentions toward physical activity (β = .10)</td></tr><tr><td>13</td><td>Conroy et al., 2010<br/>N = 201 university students<br/>M<sub>age</sub> = 19 years<br/>72% female<br/>M<sub>PA</sub> = 9406 ± 3757 average daily steps across 7 days</td><td>Prospective (1 week)</td><td>Task: Single Category Implicit Association Test (SC-IAT; Karpinski & Steinman, 2006)<br/>Stimuli: words<br/>Categories: “physical activity”, “good”, and “bad”<br/>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)</td><td>Measure: pedometer<br/>Score: average daily step count of 7 days<br/>Type: total PA</td><td>r = .14<br/>(N = 201)</td><td>Additive pattern examined<br/>Result: Implicit attitudes were significantly associated with physical activity (β = .15) after controlling for sex (β = -.10), exercise self-efficacy (β = -.20), barriers efficacy (β = .37), outcome expectancies (β = -.08), intentions (β = .24) and perceived behavioral control (β = -.10) toward physical activity</td></tr></table>
<table><tr><td>14</td><td>Denman &amp; Baldwin, 2015</td><td>N = 84 participants from the general population<br/>M<sub>age</sub> = 34 ± 11 years<br/>68% female<br/>M<sub>BMI</sub> = 30 ± 9 kg/m<sup>2</sup></td><td>Cross-sectional</td><td>Task: Single Category Implicit Association Test (SC-IAT; Karpinski &amp; Steinman, 2006)<br/>Stimuli: words<br/>Categories: “physical activity”, “bad”, and “good”<br/>Scoring procedure: D-Score (Greenwald, Nosek, &amp; Banaji, 2003)<br/>Reliability: not reported</td><td>Measure: self-report questionnaire (7-day, physical activity recall interview; Blair et al., 1985)<br/>Score: Total time (min) of exercise per week<br/>Type: MVPA</td><td>r = -.03 (N = 84)</td><td>Additive pattern examined<br/>Result: neither implicit (β = 2.56) nor explicit attitudes (β = -.93) were significantly associated with exercise minutes</td></tr><tr><td>15</td><td>Endrighi et al., 2016 *</td><td>N = 100 endometrial cancer survivors<br/>M<sub>age</sub> = 57 ± 11 years<br/>100% female<br/>M<sub>BMI</sub> = 34 ± 9 kg/m<sup>2</sup></td><td>Cross-sectional</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, &amp; Schwartz, 1998)<br/>Stimuli: words<br/>Categories: “exercise” and “physical inactivity”, “good”, and “bad”<br/>Scoring procedure: D-Score (Greenwald, Nosek, &amp; Banaji, 2003)<br/>Reliability: Split-half adjusted r = .81</td><td>Measure: accelerometer, self-report time spent exercising per session and per day<br/>Score: composite of time (minutes) spent exercising<br/>Type: MVPA</td><td>Not reported and not provided</td><td>Additive pattern examined<br/>Result: Implicit attitudes were not significantly associated with physical activity after controlling for exercise self-efficacy (β not reported)</td></tr></table>
<table><tr><td>16</td><td>Escriva-Boulley &amp; Boiché (unpublished)<br/>*</td><td>N = 107 adults<br/>M<sub>age</sub> = 42.9 years<br/>75% females<br/>M<sub>PA</sub> = 149 min</td><td>Cross-sectional</td><td>Task: Implicit Association Test (IAT; Greenwald, McGhee, &amp; Schwartz, 1998)<br/>Stimuli: words<br/>Categories: "physical activity", "sedentary behavior", "positive", and "negative"<br/>Scoring procedure: DW-Score (Chevance et al., 2017b; Richetin et al., 2015)</td><td>Measure: self-report questionnaire (Bélanger-Gravel &amp; Godin, 2010)<br/>Score: frequency and duration of all PA sessions reported in minutes per week<br/>Type: MVPA</td><td>r = .12<br/>(N = 107)</td><td>Not examined, not a study objective</td></tr><tr><td>17</td><td>Eves et al., 2007<br/>*</td><td>N = 188 Royal Air Force trainees<br/>M<sub>age</sub> = 20 ± 4 years<br/>22% female</td><td>Association 1: Retrospective (1 week)<br/>Association 2: Cross-sectional</td><td>Task: Evaluative priming task (Fazio, 2001)<br/>Stimuli: words<br/>Primes: exercise (moderate [walking] and vigorous [running] intensity), control, "good", "bad", "happy", and "sad"<br/>Scoring procedure: unclear<br/>Reliability: not reported</td><td>Measure (association 1): self-report questionnaire (7-day, physical activity recall interview; Blair et al., 1985).<br/>Score: frequency of physical activity of 7 days<br/>Type: MVPA<br/>Measure (association 2): pedometer<br/>Score: average daily step count of 7 days<br/>Type: total PA</td><td>Not reported and not provided</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>18</td><td>Gerber et al., 2018</td><td>N = 101 patients showing a psychiatric disorder directly recruited from psychiatric clinics<br/>M<sub>age</sub> = 40 ± 12 years<br/>49% female<br/>M<sub>PA</sub> = 226 min of weekly MVPA</td><td>Prospective (1 week)<br/>Task: Single Target Implicit Association Test (ST-IAT; Bluemke & Fries, 2008)<br/>Stimuli: photographs and emoticons<br/>Categories: “exercise” (photographs); “good” and “bad” (photographs)<br/>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)<br/>Reliability: not reported</td><td>Measure (associations 1-3): interview using the SIMPAQ (Rosenbaum & Ward, 2016)<br/>Score: Time (min) per week of “walking”, “exercise activities”, and “other physical activities”<br/>Measure (associations 4 & 5): accelerometer<br/>Score: Time (min) of 7 days of light physical activity and moderate- to-vigorous physical activity.<br/>Type: MVPA (associations 2 and 5); LTPA (association 4); “other” (associations 1 and 3)</td><td>Association 1 (interview, walking): r = -.10<br/>Association 2 (interview, exercise activities): r = .14<br/>Association 3 (interview, other PA): r = .03<br/>Association 4 (accelerometer, LTPA): r = -.07<br/>Association 5 (accelerometer, MVPA): r = .09<br/>(all N's = 101)</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>19</td><td>Hyde et al., 2012</td><td>N = 164 university students (enrolled in undergraduate kinesiology course)<br/>46% female<br/>M<sub>PA</sub> = 7763 average daily steps across 7 days</td><td>Associations 1 &amp; 2: Retrospective (1 week)<br/>Associations 3 &amp; 4: Cross-sectional<br/>Associations 5 &amp; 6: Prospective (1 week)</td><td>Task: Single Category Implicit Association Test (SC-IAT; Karpinski &amp; Steinman, 2006)<br/>Stimuli: words<br/>Categories: “physical activity”, “good”, and “bad”<br/>Scoring procedure: D-Score (Greenwald, Nosek, &amp; Banaji, 2003)<br/>Reliability: Split-half r = .73</td><td>Measure (associations 2 &amp; 6): pedometer<br/>Score: average daily step count of 7 days<br/>Type: total PA<br/>Measure (associations 1, 3, 4 &amp; 5): self-report questionnaire (International Physical Activity Questionnaire - Short Form; Booth, 2000)<br/>Score: frequency, intensity and duration of 7 days expressed in METs<br/>Type: MVPA</td><td>Association 1 (self-reported): r = .01<br/>Association 2 (pedometer): r = -.08<br/>Association 3 (self-reported): r = -.12<br/>Association 4 (self-reported): r = .05<br/>Association 5 (self-reported): r = -.13<br/>Association 6 (pedometer): r = -.07<br/>(all N's = 164)</td><td>Not examined, not a study objective</td></tr></table>
<table><tr><td>20</td><td>Muschalik et al., 2018 *</td><td>N = 340 university students</td><td>Associations 1-3: Retrospective (1 & 2 months)</td><td>Task: Single Category Implicit Association Test (SC-IAT; Karpinski & Steinman, 2006)</td><td>Measure: self-report questionnaire (Short Questionnaire to Assess Health-enhancing physical activity, Wendel-Vos et al., 2003)</td><td>Association 1: Additive pattern examined<br>r = .07<br>(N = 240)</td></tr><tr><td></td><td></td><td>M<sub>age</sub> = 20 years</td><td></td><td>Stimuli: words</td><td></td><td>Association 2: Result: Implicit attitudes non-significantly associated with</td></tr><tr><td></td><td></td><td>61% female</td><td>Associations 4-6: Cross-sectional</td><td>Categories: “physical activity”, “positive”, and “negative”</td><td>Score: frequency, intensity and duration of 7 days expressed in METs for total PA (commuting, activities at work, household, planned exercise and leisure time activities)</td><td>Association 3: physical activity (β = .11) after controlling for gender (β = .14), age (β = .11), perceived pros</td></tr><tr><td></td><td></td><td></td><td>Associations 7-9: Prospective (1, 2 and 3 months)</td><td>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)</td><td>Type: total PA</td><td>Association 4: (β = .07) and cons (β = -.06), social norms (β = -.07), social modeling</td></tr><tr><td></td><td></td><td></td><td></td><td>Reliability: Split-half r = .83</td><td></td><td>Association 5: (β = -.08), self-efficacy (β = .22), and intentions</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Association 6: (β = .17) toward physical activity</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Association 7: (β are reported here for PA measure at 3-month only)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Association 8: r = .10<br>(N = 128)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Association 9: r = .05<br>(N = 120)</td></tr></table>
<table><tr><td>21 Oliver & Kemps, 2018</td><td>N = 103 university students</td><td>Retrospective (1 week)</td><td>Task: Single Category Implicit Association Test (SC-IAT; Karpinski & Steinman, 2006)</td><td>Measure: pedometer which participants were to remove when exercising (i.e., working out at the gym, participating in a team sport or going for a run, as well as walking for the purpose of exercising)</td><td>r = -.28 (N = 103)</td><td>Additive pattern examined</td></tr><tr><td></td><td>M<sub>age</sub> = 27 ± 11 years</td><td></td><td>Stimuli: words</td><td>Categories: "activity", "I like", and "I dislike"</td><td></td><td>Result: Implicit attitudes were significantly associated with physical activity (β = -.27) after controlling for autonomous (β = .23) and controlled motivation (β = .31) toward physical activity</td></tr><tr><td></td><td>69% female</td><td></td><td>Scoring procedure: D-Score (Greenwald, Nosek, & Banaji, 2003)</td><td></td><td>Score: daily step count average of 7 days for incidental PA only</td><td></td></tr><tr><td></td><td>M<sub>BMI</sub> = 34 ± 9 kg/m<sup>2</sup></td><td></td><td>Reliability: not reported</td><td></td><td>Type: incidental PA</td><td></td></tr><tr><td></td><td>M<sub>PA</sub> = 6073 ± 2730 average daily steps across 7 days</td><td></td><td></td><td></td><td></td><td></td></tr></table>
<table><tr><td>22</td><td>Padin et al., 2017</td><td>N = 150 university students</td><td>Cross-sectional</td><td>Task: Personalized Single Category Implicit Association Test (SC-IAT; Karpinski & Steinman, 2006; Olson & Fazio, 2004)</td><td>Measure: Self-report questionnaire (International Physical Activity Questionnaire, IPAQ; long form; Booth, 2000)</td><td>Association 1: r = .12</td><td>Additive pattern examined</td></tr><tr><td></td><td></td><td>M<sub>age</sub> = 19 ± 2 years</td><td></td><td>Stimuli: words</td><td>Score (association 1): leisure time physical activity of the past 7 days transformed to METs/minutes per week (Ainsworth et al., 1993)</td><td>Association 2: r = .17</td><td>Result: (association 1) Implicit attitudes were not significantly associated with physical activity (b = 677) after controlling for explicit instrumental (b = -128) and affective attitudes (b = 27) toward physical activity;</td></tr><tr><td></td><td></td><td>60% female</td><td></td><td>Categories: “physical activity”, “I like”, and “I don’t like”</td><td>Score (association 2): average number of minutes spent engaging in a single bout of walking or moderate-to-vigorous PA during their leisure time</td><td>(all N's = 148)</td><td></td></tr><tr><td></td><td></td><td>M<sub>BMI</sub> = 24 ± 4 kg/m<sup>2</sup></td><td></td><td>Scoring procedure: D-Score without error penalty (Greenwald, Nosek, & Banaji, 2003; Richetin et al., 2015)</td><td></td><td></td><td>(association 2) Implicit attitudes were significantly associated with physical activity (b = 12.45) after controlling for explicit instrumental (b = -.70) and affective attitudes (b = .09) toward physical activity</td></tr><tr><td></td><td></td><td>M<sub>PA</sub> = 2100 METs min per week</td><td></td><td>Reliability: not reported</td><td>Type: MVPA</td><td></td><td></td></tr></table>
<table><tr><td>23</td><td>Rebar et al.,<br>2015</td><td>N = 91 university<br>students<br>M<sub>age</sub> = 20 years<br>42% female</td><td>Prospective<br>(2 weeks)</td><td>Task: Single Category Implicit<br>Association Test (SC-IAT; Karpinski<br>& Steinman, 2006)<br>Stimuli: words<br>Categories: "physical activity",<br>"good", and "bad"<br>Scoring procedures: D-Score<br>(Greenwald, Nosek, & Banaji, 2003)<br>and IPE-Score (Rebar et al., 2015).<br>Reliability (D-Score): split-half r =<br>.73</td><td>Measure: Tri-axial<br>accelerometer over 13 days<br>Score: average activity counts<br>Type: total PA<br>D-Score: r =<br>.12<br>IPE-Score: r =<br>.25<br>(all N's = 91)</td><td>Not examined, not a<br>study objective</td></tr><tr><td>24</td><td>Rebar &<br>Conroy,<br>2013a<br>(unpublished)<br>*</td><td>N = 128 university<br>students<br>M<sub>age</sub> = 20 years<br>59% female</td><td>Prospective<br>(13 days)</td><td>Task: Single Category Implicit<br>Association Test (SC-IAT; Karpinski<br>& Steinman, 2006)<br>Stimuli: words<br>Categories: "physical activity",<br>"good", and "bad"<br>Scoring procedures: IPE-Score<br>(Rebar et al., 2015)<br>Reliability: not reported, not<br>applicable with this scoring<br>procedure</td><td>Measure: Tri-axial<br>accelerometer over 13 days<br>Score: average activity counts<br>Type: total PA<br>r = .13<br>(N = 128)</td><td>Not examined, not a<br>study objective</td></tr><tr><td>25</td><td>Rebar &<br>Conroy,<br>2013b<br>(unpublished)<br>*</td><td>N = 195 university<br>students<br>M<sub>age</sub> = 20 years<br>51% female</td><td>Prospective (7<br>days)</td><td>Task: Single Category Implicit<br>Association Test (SC-IAT; Karpinski<br>& Steinman, 2006)<br>Stimuli: words<br>Categories: "physical activity",<br>"good", and "bad"</td><td>Measure: pedometer over 7<br>days<br>Score: Average daily step<br>count<br>Type: total PA<br>r = .03<br>(N = 195)</td><td>Not examined, not a<br>study objective</td></tr></table>
<table><tr><td>26 Zenko & Ekkekakis, 2019</td><td>N = 95 (73% university students)<br/>M<sub>age</sub> = 25 years<br/>61% female<br/>M<sub>BMI</sub> = 25 kg/m<sup>2</sup><br/>M<sub>PA</sub> = 236 min per week</td><td>Retrospective (1 week)<br/>Tasks: Affective Misattribution Procedure (association 1);<br/>Evaluative Decision Task (associations 2, 3);<br/>Extrinsic Affective Simon Task (associations 4, 5);<br/>Go/NoGo Association Task (associations 6, 7);<br/>Personalized Single-Category Implicit Association Test (association 8);<br/>Single-Category Implicit Association Test (associations 9, 10)<br/>Please see the original article for a description of each stimuli, scoring procedures and reliability estimates.</td><td>Scoring procedures: IPE-Score (Rebar et al., 2015)<br/>Reliability: not reported, not applicable with this scoring procedure<br/>Measure: International Physical Activity Questionnaire (IPAQ-SF; Craig et al., 2003)<br/>Score: average 10 minutes bouts engaged in MVPA and walking exercise behavior during a usual week reported in minutes per week<br/>Type: MVPA</td><td>Association 1: r = -.16 (N = 82)<br/>Association 2: r = -.09 (N = 80)<br/>Association 3: r = -.14 (N = 80)<br/>Association 4: r = .03 (N = 81)<br/>Association 5: r = .05 (N = 81)<br/>Association 6: r = -.07 (N = 80)<br/>Association 7: r = .12 (N = 80)<br/>Association 8: r = -.08 (N = 92)<br/>Association 9: r = -.005 (N = 92)<br/>Association 10: r = -.14 (N = 87)</td><td>Not examined, not a study objective</td></tr></table>
**Note.** \*Correlation(s) not mentioned in the manuscript and provided from the authors; \+Designs are presented in accordance with the aim of this meta-analysis and in reference to the correlations reported between implicit attitudes and physical activity behavior (e.g., solely cross-sectional correlations were retrieved in experimental and interventional studies); \circAccording to Perugini, Richetin & Zogmaister (2005) an additive pattern is observed when an implicit measure explains a unique portion of variance of the dependent variable in addition to what is explained by an explicit measure; IA = Implicit Attitudes; PA = physical activity; MVPA = Moderate to vigorous PA; total PA = all type of PA including exercise, commuting, activities at work, household and leisure time activities; Incidental PA = PA without planned PA or exercise; other = other type of PA; BMI = body mass index; RT = reaction time.
<table><caption>Table 2. Summary of the potential moderators of the implicit attitudes and physical activity association (none were statistically significant)</caption><thead><tr><th>Moderator</th><th>k effect sizes</th><th>r (95 % CI)</th><th>Z</th><th>p-value</th></tr></thead><tbody><tr><td>Participants</td><td></td><td></td><td></td><td></td></tr><tr><td>General population</td><td>4</td><td>.10 (-.03, .24)</td><td>.91</td><td>.13</td></tr><tr><td>Patients</td><td>9</td><td>.08 (-.01, .16)</td><td>.93</td><td>.09</td></tr><tr><td>Students</td><td>42</td><td>.05 (.09, .08)</td><td>.98</td><td>.01</td></tr><tr><td>Age *°</td><td></td><td></td><td></td><td></td></tr><tr><td>Mean age > 30 years old</td><td>13</td><td>.08 (.00, .04)</td><td>.98</td><td>.02</td></tr><tr><td>Mean age < 30 years old</td><td>36</td><td>.07 (.00, .02)</td><td>.99</td><td>&lt; .001</td></tr><tr><td>Study design</td><td></td><td></td><td></td><td></td></tr><tr><td>Cross-sectional</td><td>19</td><td>.09 (.04, .15)</td><td>.99</td><td>&lt; .001</td></tr><tr><td>Prospective</td><td>18</td><td>.06 (.01, .11)</td><td>.98</td><td>.02</td></tr><tr><td>Retrospective</td><td>18</td><td>-.00 (-.06, .05)</td><td>.16</td><td>.70</td></tr><tr><td>PA measure (1)</td><td></td><td></td><td></td><td></td></tr><tr><td>Self-reported</td><td>43</td><td>.06 (.02, .09)</td><td>.99</td><td>&lt; .001</td></tr><tr><td>Objective</td><td>12</td><td>.04 (-.03, .11)</td><td>.82</td><td>.25</td></tr><tr><td>PA measure (2)</td><td></td><td></td><td></td><td></td></tr><tr><td>MVPA</td><td>32</td><td>.07 (.02, .11)</td><td>.99</td><td>&lt; .001</td></tr><tr><td>Other</td><td>23</td><td>.04 (-.01, .08)</td><td>.90</td><td>.14</td></tr><tr><td>Implicit measure (1)</td><td></td><td></td><td></td><td></td></tr><tr><td>Classification task</td><td>49</td><td>.05 (.02, .08)</td><td>.99</td><td>&lt; .01</td></tr><tr><td>Priming task</td><td>6</td><td></td><td></td><td></td></tr><tr><td>Implicit measure (2)</td><td></td><td></td><td></td><td></td></tr><tr><td>IAT or relative</td><td>42</td><td>.04 (.01, .09)</td><td>.98</td><td>.02</td></tr><tr><td>Other</td><td>13</td><td>.09 (.01, .04)</td><td>.98</td><td>&lt; .01</td></tr><tr><td>Study quality *</td><td></td><td></td><td></td><td></td></tr><tr><td>High (score ≥ .08/1)</td><td>12</td><td>.05 (-.02, .11)</td><td>.89</td><td>.15</td></tr><tr><td>Medium (.02 &lt; score &lt; .08/1)</td><td>35</td><td>.04 (.00, .02)</td><td>.95</td><td>.06</td></tr><tr><td>Low (score ≤ .02/1)</td><td>8</td><td>.17 (.07, .26)</td><td>.99</td><td>&lt; .01</td></tr><tr><td>Laboratory</td><td></td><td></td><td></td><td></td></tr><tr><td>Berry</td><td>3</td><td>.20 (.07, .34)</td><td>.99</td><td>&lt; .01</td></tr><tr><td>Boiché</td><td>6</td><td>.15 (.05, .27)</td><td>.99</td><td>&lt; .01</td></tr><tr><td>Conroy</td><td>11</td><td>.09 (.01, .16)</td><td>.98</td><td>.02</td></tr><tr><td>Brand</td><td>11</td><td>.02 (.00, .03)</td><td>.57</td><td>.45</td></tr><tr><td>Other</td><td>24</td><td>.02 (.00, .02)</td><td>.72</td><td>.34</td></tr><tr><td>Study objective = testing correlation</td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>37</td><td>.05 (-.00, .11)</td><td>.94</td><td>.08</td></tr><tr><td>No</td><td>18</td><td>.06 (.01, .09)</td><td>.99</td><td>&lt; .01</td></tr></tbody></table>

Note. * Age and study quality have been treated as continuous variables in the moderator analyses; they are reported here as categorical values to simplify the interpretation; ° There are missing values for the variable 'age'.
<figure><img src="image_2.png" /><figcaption>Figure 1. Flow diagram of inclusion process</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2. Forrest plot</figcaption></figure>

Note. AMP = Affective Misattribution Procedure; IAT = Implicit Association Test; BIAT = Brief Implicit Association Test; SC-IAT = Single Category Implicit Association Test; ST-IAT = Single Target Implicit Association Test; GNAT = Go No Go Association Test; EPP = Evaluative Priming Procedure; EAST = Extrinsic Affective Simon Task; EDT = Evaluative Decision Task; unpub = unpublished manuscript
<figure><img src="image_4.png" /><figcaption>Figure 3. Forrest plot of the sensitivity analyses</figcaption></figure>

**Note.** RVE = Robust Variance Estimation; AGG = aggregated meta-analysis; ML = multi-level meta-analysis; outlier 1 = Oliver & Kemps, 2018; outlier 2 = Berry et al., 2011; low qual = studies with a low quality score (≤ .02/1); k = effect sizes.