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The mediating role of behavioural automaticity and intention on past to future bootcamp attendance

The mediating role of behavioural automaticity and intention on past to future bootcamp attendance AUSTRALIAN PSYCHOLOGIST https://doi.org/10.1080/00050067.2023.2210759 ORIGINAL ARTICLE The mediating role of behavioural automaticity and intention on past to future bootcamp attendance a,b a,e a,c,d,e a,b,d Sabryna Sas , Daniel J. Phipps , Martin S. Hagger and Kyra Hamilton a b School of Applied Psychology, Griffith University, Mt Gravatt, Australia; Menzies Health Institute Queensland, Griffith University, Gold c d Coast, Australia; Department of Psychological Sciences, University of California, Merced, United States of America; Health Sciences Research Institute, University of California, Merced, United States of America; Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland ABSTRACT ARTICLE HISTORY Received 27 July 2022 Objective: The aim of the current study was to test whether behavioural automaticity and Accepted 26 April 2023 intention mediated the effects of past behaviour on a particular type of vigorous physical exercise: bootcamp attendance. KEYWORDS Methods: A community sample (N = 69) who had previously attended a bootcamp class was Habit; dual process theory; recruited from Queensland, Australia. Participants were asked to complete measures of their social-cognition; physical previous bootcamp attendance, behavioural automaticity, and intention to attend bootcamps activity (Time 1). One month later (Time 2), participants were asked to report their bootcamp attendance and behavioural automaticity in the previous month. Data were fitted to a Partial Least Squares-SEM model. Results: Past behaviour predicted both intention and behavioural automaticity. However, while behavioural automaticity significantly predicted prospectively measured behaviour and mediated the past-future behaviour relationship, there was no significant relationship between intention and bootcamp attendance. Past behaviour still predicted future behaviour beyond both behavioural automaticity and intention. Conclusions: Current results support dual process and habit theory in that behavioural automa- ticity accounts for a portion of the residual effect of past behaviour on future behaviour which is not accounted for by intentional processes. The lack of significant effect by intention may also support these theories, as bootcamp classes likely occur in a stable context (e.g., at a prescribed time and in a regular location), encouraging habitual responding over considered decision-making. KEY POINTS What is already known about this topic: (1) Engaging in regular physical activity, especially vigorous intensity exercise, provides benefits to health and wellbeing. (2) Extending social cognition theories, dual-process models posit that behaviour is enacted predominately through deliberative or automatic pathways, depending on contextual and situational factors. (3) A common hypothesis in dual process and habit theory is that automaticity is likely to exhibit strong effects when the behaviour occurs in stable contexts. What this topic adds: (1) This research tests the effects of behavioural automaticity and intention on physical activity in a seldom examined yet common type of exercise, bootcamp attendance. (2) Behavioural automaticity mediated the relationship between past behaviour and future bootcamp attendance, but the intention did not predict bootcamp attendance. (3) Given the stable context of bootcamp classes (i.e., at a prescribed time and place), current findings support dual process and habit theory that behaviours more likely to be stable are more likely to be enacted automatically rather than deliberatively. The beneficial effects of engaging in regular physical to moderate physical activity, provides additional ben- activity are well established, including enhanced psy- efits including improvements in body composition, chological well-being, prevention of heart disease, and decreased resting blood pressure, and enhanced glu- improved cognitive functioning (Haskell et al., 2009). cose control (Burgomaster et al., 2008; Swain, 2006). Participation in vigorous intensity exercise, compared Despite the well-known benefits, a large proportion of CONTACT Kyra Hamilton kyra.hamilton@griffith.edu.au © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 S. SAS ET AL. the Australian population fail to meet the recom- health behaviours (Brown et al., 2020; Hamilton et al., mended guideline of 150 min of moderate intensity 2020; Phipps et al., 2020). However, as posited by or 75 min of vigorous intensity physical activity per dual process and habit theory, it is likely that while week (Australian Government Department of Health both behavioural automaticity and intention appear Population Health Division, 2019). One popular as significant determinants of behaviour in group- method of engaging in vigorous physical activity is level correlational data, only one process likely “wins- the attendance of bootcamp style workout classes. out” in determining behaviour in any given situation. Bootcamp-style workouts are vigorous-intensity, Thus, theorists have attempted to investigate the highly structured group exercise programs following situations and contexts in which behaviour is likely a military training approach (Thompson, 2007). In addi- to be elicited by either habitual, automatic respond- tion to health benefits, group exercise programs ing, or intentional, reasoned decisions. involve exposure and contact with like-minded indivi- A key factor which may encourage automatic duals, where frequent association with health-focused responding over considered decision-making is the sta- individuals leads to more engagement in health beha- bility of the context in which behaviour is enacted. viours, such as exercise (Swain, 2006). Specifically, theorists have hypothesised behavioural To date, a plethora of research has employed social automaticity to likely exhibit strong effects when the cognition theories to investigate the mechanisms and behaviour occurs frequently or in stable contexts determinants of health-related behaviours (McEachan (Ouellette & Wood, 1998). For example, at the same et al., 2011), including physical activity behaviours time of the day or week, in the same place, or with the (Hagger et al., 2002). A key hypothesis of such theories is same people. This is likely explained as, while intention that behaviour is the result of reasoned, conscious inten- may be useful in eliciting initial occurrences of tions, which themselves are formed on the basis of beliefs a behaviour, frequent co-exposure to stable cues and stemming from previous experiences (Ajzen, 1991; Brown the target behaviour likely encourages both the forma- et al., 2020). Such a proposition has support in the litera- tion and activation of cue-behaviour scripts. This is sup- ture, as intention is consistently shown to predict ported by evidence, as scholars have observed weaker a modest portion of variance in health behaviours includ- effects of intention and stronger effects of automatic ing physical activity (Hagger et al., 2002; Hamilton et al., processes when behaviours were performed frequently 2021, 2022; McEachan et al., 2011; Phipps, Hannan, et al., or in a stable context (Danner et al., 2008; Norman & 2021; Phipps et al., 2022). However, meta-analytic studies Cooper, 2011; Ouellette & Wood, 1998). Regarding phy- have also found past behaviour to have a significant sical activity, such a pattern of effects may suggest that residual effect on future behaviour beyond that of inten- while previous findings have shown modest effects of tion (Ouellette & Wood, 1998; Zhang et al., 2019), implying both behavioural automaticity and intention on beha- that the effect of past behaviour on future behaviour is viour (Gardner et al., 2011; Hagger, 2018), certain forms not totally modelled through changes in beliefs, and of physical activity may be more likely to fall under the subsequently, intentions. Instead, the persistent residual control of behavioural automaticity than others. effects of past behaviour may indicate the presence of Specifically, in the current study, we aim to investigate additional pathways to behaviour which are not the effects of behavioural automaticity and intention on accounted for by conscious, reasoned decision-making. a physical activity behaviour that is likely considered Such findings have contributed to the rise of dual-process stable: bootcamp attendance. That is, bootcamp classes models which implicate both deliberate and automatic often occur at a set time and in a regular place over the processes in explaining behaviour (Strack & Deutsch, period of the course, rather than at a flexible time or in 2004). a varied place of the individuals choosing. Based on this A key construct that attempts to measure such reasoning, therefore, bootcamp attendance may theo- processes is behavioural automaticity, a core element retically be determined by automatic processes rather of the wider habit construct conceptualised as the than considered decision-making. extent to which individuals enact their behaviour The aim of the current study is to test whether automatically, i.e., without conscious input (Gardner, behavioural automaticity and intention mediate the 2012; Verplanken & Orbell, 2003). To date, the beha- effects of past behaviour on future behaviour in vioural automaticity construct has shown notable a particular type of physical activity behaviour: the success in predicting physical activity with modest- attendance of bootcamp classes. Based upon the the- sized effects (Gardner et al., 2011) and has been ories of social cognition, dual-process models and shown to mediate the past behaviour-future beha- habit theory (Ajzen, 1991; Verplanken & Orbell, 2003), viour relationship alongside intention on a variety of we expect that both intention and behavioural AUSTRALIAN PSYCHOLOGIST 3 automaticity will significantly mediate the effects of the past 4 weeks. In general, to what extent did you attend past behaviour on future bootcamp attendance. bootcamp?”. Item three, “Think about the entire past 4 weeks and count, how many times did you attend boot- camp?”, required a numerical response Method Participants Procedure Participants (N = 69, 76.8% female, mean age = 35.84, This study was granted ethical clearance by the Griffith SD = 11.47) were bootcamp attendees from various University Human Research Ethics Board (GU ref no: locations across Queensland, Australia. Participants 2015/55). A prospective-correlational design was used were recruited if they had attended bootcamp sessions with two data collection time points separated by 4 in the 4-weeks prior to the study, and if they were 18 weeks. Participants were recruited via posts on social years of age or older. media sites, emails to first-year psychology students, through the researchers attending bootcamp locations and providing paper-based surveys, and distributing Measures flyers at various bootcamp locations. At bootcamp loca- The survey included items measuring demographics, tions, consent forms were signed by bootcamp leaders intention, behavioural automaticity, and past beha- to indicate permission to attend sessions for data collec- viour (Time 1), and 1 month later (Time 2), behavioural tion. After providing informed consent, participants automaticity and bootcamp attendance were mea- completed an online or paper-based survey measuring sured. Measures were adapted from validated scales intention, behavioural automaticity, past behaviour, and and have been demonstrated to represent reliable bootcamp attendance items (Time 1). Four weeks later, measures of the intended constructs (Ajzen, 2006). consenting participants completed the Time 2 survey. This survey was provided in-field, online, or over the Intention phone. A unique code identifier was used to match Intention was measured at Time 1 using three items on participant responses on the two surveys. Estimated a 7-point likert scale (1 = Strongly disagree, to 7 = Strongly completion for survey 1 and 2 was 16 and 6 min, respec- agree). These questions measured participant’s self- tively. Upon completion of both time points, participants reported intention to attend bootcamp for the subse- were given the opportunity to enter a prize draw to win quent 4 weeks. The three items include (1) it is likely one of the four $25 Coles/Myer gift card vouchers. that I will attend bootcamp in the next 4 weeks, (2) I intend to attend bootcamp in the next 4 weeks, (3) Data analysis I plan to attend bootcamp in the next 4 weeks. The data were analysed as a linear partial least squares Behavioural automaticity structural equation model using WarpPLS 8.0. Behavioural automaticity was measured at Time 1 and Standard errors were created using the stable method Time 2 using the 4-item self-report behaviour automa- (Kock, 2014). Only cases with complete data were used ticity index (Gardner et al., 2012; Verplanken & Orbell, in the analysis. The Tenenhaus’ GoF index (GoF), the 2003) on a 7-point likert scale (1 = Strongly disagree, to R-squared contribution ratio (RSCR), and the average 7 = Strongly agree). The four items include (1) attending block variance inflation factor (AVIF) were used to bootcamp is something I do automatically, (2) attend- assess model fit. Values exceeding 0.36 for the GoF ing bootcamp is something I do without having to indicate good quality for studies with large effect sizes. consciously remember, (3) attending bootcamp is Values greater than 0.90 for RSCR and less than 3.3 for something I do without thinking, and (4) attending the AVCIF also indicate good fit and model quality. bootcamp is something I start doing before I realise Analysis using Gpower 3.1. indicated a minimum I am doing it. required sample of 55 to achieve a power of .80, assuming modest effect sizes for the regression coeffi - Bootcamp attendance cients (f =.15; Cohen, 1988). Participant attendance at bootcamp was measured at Time 1 and Time 2 with three items. Items one and two were Results measured on a 7-point likert scale (1 = Never, to 7 = Always). Items included (1) “Think about the past 4 weeks. In general, While the final sample consisted of 69 participants, how often did you attend bootcamp?”, and (2) “Think about data were collected for 159 participants at baseline, 4 S. SAS ET AL. Table 1. Means, standard deviations, reliability, and zero-order intercorrelations for all variables addressing bootcamp attendance. 1. 2. 3. 4. 5. 6. 7. 1. Age - 2. Gender −.188 - 3. Past behaviour (Time 1) .124 .016 - 4. Behavioural automaticity (Time 1) .241* .021 .638** - 5. Behavioural automaticity (Time 2) .243* −.038 .477** .609** - 6. Intention (Time 1) .108 .021 .556** .541** .274* - 7. Bootcamp attendance (Time 2) .151 −.032 .559** .429** .698** .288* - Mean 35.84 - 5.61 5.49 5.11 6.46 4.83 Standard deviation 11.47 - 1.61 1.69 2.05 1.26 1.95 Reliability - - .94 .94 .98 .93 .94 *p < .050; **p < .010. with 90 failing to complete the Time 2 survey, 1 month later. 2 bootcamp attendance (β=-.101, p = .124, f = .025) and Participants who successfully completed the Time 2 survey thus did not mediate the relationship between past did not significantly differ from those who did not with behaviour and bootcamp attendance (β=-.053, p = .194, 2 2 regard to age (t(156) = 1.37, p = .173), gender (χ (1) = 2.38, f = .033). Behavioural automaticity significantly mediated p = .123), marital status (χ (4) = 4.55, p = .235), employment the effect of past behaviour on prospectively measured 2 2 2 status (χ (1)= .266, p = .966), income (χ (1) = 2.46, p = .652), bootcamp attendance at Time 2 (β = .198, p < .001, f = or ethnicity (χ (1)= .195, p = .659). However, there was .121), as Time 1 behavioural automaticity significantly a modest difference between those who completed the predicted Time 2 behavioural automaticity (β = .609, p Time 2 survey and those who did not on the Time 1 study < .001, f = .371), and Time 2 behavioural automaticity variables (Wilks’ Lambda =.916, F(3,153) = 4.68, p = .004, in turn predicted prospectively measured bootcamp η = .084; Past Behaviour F(1,155) = 12.26, p = .001, attendance at Time 2 2 2 η = .073; T1 Behavioural Automaticity F(1,155) = 4.64, (β = .520, p < .001, f = .361). p = .033, η = .029; Intention F(1,155) = 8.93, p = .003, η = .054). Correlations, internal consistency, and Discussion descriptive statistics for all variables are presented in Table 1. In the current study, we aimed to investigate the med- Regarding the model, analysis revealed good fit to iating role of intention and behavioural automaticity data (GoF = .613, RSCR =.993, AFVIF = 2.29, see Figure 1). on the relationship between past and future behaviour All items are loaded significantly onto their respective in a community sample of bootcamp fitness class latent variables (all β’s > .628, all p’s <.001). Past behaviour attendees. Automaticity mediated the relationship predicted intention (β = .526, p < .001, f = .277), Time 1 between past behaviour and future bootcamp atten- behavioural automaticity (β=.626, p < .001, f = .392), and dance. However, while past behaviour predicted boot- Time 2 bootcamp attendance (β = .403, p < .001, f = .246). camp attendance, intention did not have a significant In contrast to expectations, intention did not predict Time effect on future bootcamp attendance. Figure 1. The tested model predicting bootcamp attendance from behavioural automaticity and intention. Note. Paths are presented with standardised parameter estimates. *p < .05, **p < .01, ***p < .001. AUSTRALIAN PSYCHOLOGIST 5 The significant effect of past behaviour on beha- park each time) and timing (e.g., at the same time each vioural automaticity and of behavioural automaticity week) of bootcamp classes likely encouraged more on subsequent behaviour is in line with previous the- automatic responding and habit development. ory that constructs that measure automatic, habitual Past behaviour also predicted bootcamp attendance processes, like behavioural automaticity, account, at directly and importantly beyond the effects of beha- least partially, for the residual effects of past behaviour vioural automaticity and intention. While some residual on future behaviour that are not accounted for by effect of past behaviour would be expected, for exam- intention (Ouellette & Wood, 1998). Repeated occur- ple, due to shared method bias in the behaviour mea- rences of past behaviour are a key determinant of sure, the moderate strength of this effect raises more automatic responding, leading to habit develop- potentially important questions for the tested model. ment (Gardner & Lally, 2018), especially in cases where Specifically, seminal papers in the habit field theorised the behaviour occurs frequently and in a stable con- that the majority of the variance which past behaviour text. Further, once developed, habit encourages future accounted for in the future behaviour beyond intention behavioural occurrences upon encountering relevant was due to habitual responding (Ouellette & Wood, stimuli or cues, triggering behaviour rapidly and auto- 1998). Yet, the strength of the observed residual effect matically by activating behavioural scripts or encoura- of past behaviour in the current study suggests this is ging habitual decisions in favour of undertaking the not the case. Alternatively, this residual effect of past behaviour (Gardner, Rebar, et al., 2020; Hagger, 2020). behaviour on bootcamp attendance may reflect the This effect is again likely to be particularly strong in effect of alternative constructs on bootcamp atten- stable contexts such as bootcamp attendance, as cues dance that were not measured in the current study. such as the consistent timing or location of bootcamp For example, it is possible factors such as implicit beliefs, classes may become triggers for scripts promoting self-regulation, or individual difference factors may actual bootcamp attendance. affect behaviour beyond the effect of behavioural auto- In contrast to our hypotheses and reasoned action maticity and intention (Adriaanse et al., 2014; Hagger & theories (Ajzen, 1991), intention did not mediate the Hamilton, 2021; Hamilton et al., 2018; Phipps et al., relationship between past and future behaviour. We 2020) or even moderate whether an individual responds observed a significant effect of past behaviour on automatically or as a result of a considered decision- intention in line with current theories of social cogni- making (Gardner, Lally, et al., 2020; Phipps, Hagger, tion and reasoned action. That is, past occurrences of et al., 2021; Sas et al., 2022). a behaviour likely affect the social-cognitive beliefs The current study has numerous strengths includ- which underlie intentions, for example, by allowing ing the investigation of a novel population group in for evaluations of the behaviour and increasing the bootcamp attendees, use of a prospective design, likelihood that behaviour is viewed as under one’s and adoption of theory to base hypotheses. Despite control. However, intention did not significantly pre- these strengths, several limitations should be high- dict future behaviour, despite the constructs theoreti- lighted. First, the current study used self-reported cal prominence as the most proximal predictor of measures of bootcamp attendance, and thus mea- behaviour (Ajzen, 1991). While this finding may be surements may be subject to recall and social desir- unexpected from social cognition theories and pre- ability bias. Future research may consider requiring vious evidence in broader physical activity research bootcamp leaders to complete class attendance lists (Hagger et al., 2002), a plausible explanation for this and comparing this with self-reported attendance to lack of significant effect may lie in dual process and provide more accurate data. As an additional benefit, habit theories. Specifically, there are findings of weaker such observation-based behavioural measures would effects of intention as compared to habit in highly also serve to minimise any potential biases caused by stable contexts (Norman & Cooper, 2011; Ouellette & participants declining to complete follow-up surveys. Wood, 1998) and that when a behaviour becomes This was an issue in the current research, where sufficiently habitual, intention is no longer an impor- higher than desirable attrition between the Time 1 tant factor in eliciting a behavioural response and Time 2 measurement points presented a notable (Chatzisarantis & Hagger, 2007; Gardner et al., 2011). limitation and resulted in a modest sample size for Thus, as the current study recruited only those who the final analysis. This is partially addressed through had recently attended a bootcamp class, it is likely the use of PLS-SEM, which is generally accepted to intention played an important role in the initial deci- provide accurate results even in smaller sample sizes sion to engage in these classes, but once attendance (Willaby et al., 2015). However, it is none-the-less began, the stable context (e.g., in the same gym or a concern which should be addressed in the future 6 S. SAS ET AL. ORCID research, for example, through the use of a larger sample and objective or observational measures. Sabryna Sas http://orcid.org/0000-0002-6537-0964 Lastly, due to the correlational nature of this study, Daniel J. Phipps http://orcid.org/0000-0002-0217-0578 the interpretation of the effects reported is based Martin S. Hagger http://orcid.org/0000-0002-2685-1546 solely upon theory. While this is an inherent limita- Kyra Hamilton http://orcid.org/0000-0001-9975-685X tion of correlational research, it may be of particular importance in researching constructs that are likely Data availability statement developed through stable contexts, like behavioural automaticity, where the direction of effect is difficult Data, outputs and supplementary materials are available to establish. Additional research including longitudi- at https://osf.io/uqjxk/ nal and cross-lagged panel designs is needed in order to confirm the hypothesised directions of effects. 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The mediating role of behavioural automaticity and intention on past to future bootcamp attendance

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AUSTRALIAN PSYCHOLOGIST https://doi.org/10.1080/00050067.2023.2210759 ORIGINAL ARTICLE The mediating role of behavioural automaticity and intention on past to future bootcamp attendance a,b a,e a,c,d,e a,b,d Sabryna Sas , Daniel J. Phipps , Martin S. Hagger and Kyra Hamilton a b School of Applied Psychology, Griffith University, Mt Gravatt, Australia; Menzies Health Institute Queensland, Griffith University, Gold c d Coast, Australia; Department of Psychological Sciences, University of California, Merced, United States of America; Health Sciences Research Institute, University of California, Merced, United States of America; Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland ABSTRACT ARTICLE HISTORY Received 27 July 2022 Objective: The aim of the current study was to test whether behavioural automaticity and Accepted 26 April 2023 intention mediated the effects of past behaviour on a particular type of vigorous physical exercise: bootcamp attendance. KEYWORDS Methods: A community sample (N = 69) who had previously attended a bootcamp class was Habit; dual process theory; recruited from Queensland, Australia. Participants were asked to complete measures of their social-cognition; physical previous bootcamp attendance, behavioural automaticity, and intention to attend bootcamps activity (Time 1). One month later (Time 2), participants were asked to report their bootcamp attendance and behavioural automaticity in the previous month. Data were fitted to a Partial Least Squares-SEM model. Results: Past behaviour predicted both intention and behavioural automaticity. However, while behavioural automaticity significantly predicted prospectively measured behaviour and mediated the past-future behaviour relationship, there was no significant relationship between intention and bootcamp attendance. Past behaviour still predicted future behaviour beyond both behavioural automaticity and intention. Conclusions: Current results support dual process and habit theory in that behavioural automa- ticity accounts for a portion of the residual effect of past behaviour on future behaviour which is not accounted for by intentional processes. The lack of significant effect by intention may also support these theories, as bootcamp classes likely occur in a stable context (e.g., at a prescribed time and in a regular location), encouraging habitual responding over considered decision-making. KEY POINTS What is already known about this topic: (1) Engaging in regular physical activity, especially vigorous intensity exercise, provides benefits to health and wellbeing. (2) Extending social cognition theories, dual-process models posit that behaviour is enacted predominately through deliberative or automatic pathways, depending on contextual and situational factors. (3) A common hypothesis in dual process and habit theory is that automaticity is likely to exhibit strong effects when the behaviour occurs in stable contexts. What this topic adds: (1) This research tests the effects of behavioural automaticity and intention on physical activity in a seldom examined yet common type of exercise, bootcamp attendance. (2) Behavioural automaticity mediated the relationship between past behaviour and future bootcamp attendance, but the intention did not predict bootcamp attendance. (3) Given the stable context of bootcamp classes (i.e., at a prescribed time and place), current findings support dual process and habit theory that behaviours more likely to be stable are more likely to be enacted automatically rather than deliberatively. The beneficial effects of engaging in regular physical to moderate physical activity, provides additional ben- activity are well established, including enhanced psy- efits including improvements in body composition, chological well-being, prevention of heart disease, and decreased resting blood pressure, and enhanced glu- improved cognitive functioning (Haskell et al., 2009). cose control (Burgomaster et al., 2008; Swain, 2006). Participation in vigorous intensity exercise, compared Despite the well-known benefits, a large proportion of CONTACT Kyra Hamilton kyra.hamilton@griffith.edu.au © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 S. SAS ET AL. the Australian population fail to meet the recom- health behaviours (Brown et al., 2020; Hamilton et al., mended guideline of 150 min of moderate intensity 2020; Phipps et al., 2020). However, as posited by or 75 min of vigorous intensity physical activity per dual process and habit theory, it is likely that while week (Australian Government Department of Health both behavioural automaticity and intention appear Population Health Division, 2019). One popular as significant determinants of behaviour in group- method of engaging in vigorous physical activity is level correlational data, only one process likely “wins- the attendance of bootcamp style workout classes. out” in determining behaviour in any given situation. Bootcamp-style workouts are vigorous-intensity, Thus, theorists have attempted to investigate the highly structured group exercise programs following situations and contexts in which behaviour is likely a military training approach (Thompson, 2007). In addi- to be elicited by either habitual, automatic respond- tion to health benefits, group exercise programs ing, or intentional, reasoned decisions. involve exposure and contact with like-minded indivi- A key factor which may encourage automatic duals, where frequent association with health-focused responding over considered decision-making is the sta- individuals leads to more engagement in health beha- bility of the context in which behaviour is enacted. viours, such as exercise (Swain, 2006). Specifically, theorists have hypothesised behavioural To date, a plethora of research has employed social automaticity to likely exhibit strong effects when the cognition theories to investigate the mechanisms and behaviour occurs frequently or in stable contexts determinants of health-related behaviours (McEachan (Ouellette & Wood, 1998). For example, at the same et al., 2011), including physical activity behaviours time of the day or week, in the same place, or with the (Hagger et al., 2002). A key hypothesis of such theories is same people. This is likely explained as, while intention that behaviour is the result of reasoned, conscious inten- may be useful in eliciting initial occurrences of tions, which themselves are formed on the basis of beliefs a behaviour, frequent co-exposure to stable cues and stemming from previous experiences (Ajzen, 1991; Brown the target behaviour likely encourages both the forma- et al., 2020). Such a proposition has support in the litera- tion and activation of cue-behaviour scripts. This is sup- ture, as intention is consistently shown to predict ported by evidence, as scholars have observed weaker a modest portion of variance in health behaviours includ- effects of intention and stronger effects of automatic ing physical activity (Hagger et al., 2002; Hamilton et al., processes when behaviours were performed frequently 2021, 2022; McEachan et al., 2011; Phipps, Hannan, et al., or in a stable context (Danner et al., 2008; Norman & 2021; Phipps et al., 2022). However, meta-analytic studies Cooper, 2011; Ouellette & Wood, 1998). Regarding phy- have also found past behaviour to have a significant sical activity, such a pattern of effects may suggest that residual effect on future behaviour beyond that of inten- while previous findings have shown modest effects of tion (Ouellette & Wood, 1998; Zhang et al., 2019), implying both behavioural automaticity and intention on beha- that the effect of past behaviour on future behaviour is viour (Gardner et al., 2011; Hagger, 2018), certain forms not totally modelled through changes in beliefs, and of physical activity may be more likely to fall under the subsequently, intentions. Instead, the persistent residual control of behavioural automaticity than others. effects of past behaviour may indicate the presence of Specifically, in the current study, we aim to investigate additional pathways to behaviour which are not the effects of behavioural automaticity and intention on accounted for by conscious, reasoned decision-making. a physical activity behaviour that is likely considered Such findings have contributed to the rise of dual-process stable: bootcamp attendance. That is, bootcamp classes models which implicate both deliberate and automatic often occur at a set time and in a regular place over the processes in explaining behaviour (Strack & Deutsch, period of the course, rather than at a flexible time or in 2004). a varied place of the individuals choosing. Based on this A key construct that attempts to measure such reasoning, therefore, bootcamp attendance may theo- processes is behavioural automaticity, a core element retically be determined by automatic processes rather of the wider habit construct conceptualised as the than considered decision-making. extent to which individuals enact their behaviour The aim of the current study is to test whether automatically, i.e., without conscious input (Gardner, behavioural automaticity and intention mediate the 2012; Verplanken & Orbell, 2003). To date, the beha- effects of past behaviour on future behaviour in vioural automaticity construct has shown notable a particular type of physical activity behaviour: the success in predicting physical activity with modest- attendance of bootcamp classes. Based upon the the- sized effects (Gardner et al., 2011) and has been ories of social cognition, dual-process models and shown to mediate the past behaviour-future beha- habit theory (Ajzen, 1991; Verplanken & Orbell, 2003), viour relationship alongside intention on a variety of we expect that both intention and behavioural AUSTRALIAN PSYCHOLOGIST 3 automaticity will significantly mediate the effects of the past 4 weeks. In general, to what extent did you attend past behaviour on future bootcamp attendance. bootcamp?”. Item three, “Think about the entire past 4 weeks and count, how many times did you attend boot- camp?”, required a numerical response Method Participants Procedure Participants (N = 69, 76.8% female, mean age = 35.84, This study was granted ethical clearance by the Griffith SD = 11.47) were bootcamp attendees from various University Human Research Ethics Board (GU ref no: locations across Queensland, Australia. Participants 2015/55). A prospective-correlational design was used were recruited if they had attended bootcamp sessions with two data collection time points separated by 4 in the 4-weeks prior to the study, and if they were 18 weeks. Participants were recruited via posts on social years of age or older. media sites, emails to first-year psychology students, through the researchers attending bootcamp locations and providing paper-based surveys, and distributing Measures flyers at various bootcamp locations. At bootcamp loca- The survey included items measuring demographics, tions, consent forms were signed by bootcamp leaders intention, behavioural automaticity, and past beha- to indicate permission to attend sessions for data collec- viour (Time 1), and 1 month later (Time 2), behavioural tion. After providing informed consent, participants automaticity and bootcamp attendance were mea- completed an online or paper-based survey measuring sured. Measures were adapted from validated scales intention, behavioural automaticity, past behaviour, and and have been demonstrated to represent reliable bootcamp attendance items (Time 1). Four weeks later, measures of the intended constructs (Ajzen, 2006). consenting participants completed the Time 2 survey. This survey was provided in-field, online, or over the Intention phone. A unique code identifier was used to match Intention was measured at Time 1 using three items on participant responses on the two surveys. Estimated a 7-point likert scale (1 = Strongly disagree, to 7 = Strongly completion for survey 1 and 2 was 16 and 6 min, respec- agree). These questions measured participant’s self- tively. Upon completion of both time points, participants reported intention to attend bootcamp for the subse- were given the opportunity to enter a prize draw to win quent 4 weeks. The three items include (1) it is likely one of the four $25 Coles/Myer gift card vouchers. that I will attend bootcamp in the next 4 weeks, (2) I intend to attend bootcamp in the next 4 weeks, (3) Data analysis I plan to attend bootcamp in the next 4 weeks. The data were analysed as a linear partial least squares Behavioural automaticity structural equation model using WarpPLS 8.0. Behavioural automaticity was measured at Time 1 and Standard errors were created using the stable method Time 2 using the 4-item self-report behaviour automa- (Kock, 2014). Only cases with complete data were used ticity index (Gardner et al., 2012; Verplanken & Orbell, in the analysis. The Tenenhaus’ GoF index (GoF), the 2003) on a 7-point likert scale (1 = Strongly disagree, to R-squared contribution ratio (RSCR), and the average 7 = Strongly agree). The four items include (1) attending block variance inflation factor (AVIF) were used to bootcamp is something I do automatically, (2) attend- assess model fit. Values exceeding 0.36 for the GoF ing bootcamp is something I do without having to indicate good quality for studies with large effect sizes. consciously remember, (3) attending bootcamp is Values greater than 0.90 for RSCR and less than 3.3 for something I do without thinking, and (4) attending the AVCIF also indicate good fit and model quality. bootcamp is something I start doing before I realise Analysis using Gpower 3.1. indicated a minimum I am doing it. required sample of 55 to achieve a power of .80, assuming modest effect sizes for the regression coeffi - Bootcamp attendance cients (f =.15; Cohen, 1988). Participant attendance at bootcamp was measured at Time 1 and Time 2 with three items. Items one and two were Results measured on a 7-point likert scale (1 = Never, to 7 = Always). Items included (1) “Think about the past 4 weeks. In general, While the final sample consisted of 69 participants, how often did you attend bootcamp?”, and (2) “Think about data were collected for 159 participants at baseline, 4 S. SAS ET AL. Table 1. Means, standard deviations, reliability, and zero-order intercorrelations for all variables addressing bootcamp attendance. 1. 2. 3. 4. 5. 6. 7. 1. Age - 2. Gender −.188 - 3. Past behaviour (Time 1) .124 .016 - 4. Behavioural automaticity (Time 1) .241* .021 .638** - 5. Behavioural automaticity (Time 2) .243* −.038 .477** .609** - 6. Intention (Time 1) .108 .021 .556** .541** .274* - 7. Bootcamp attendance (Time 2) .151 −.032 .559** .429** .698** .288* - Mean 35.84 - 5.61 5.49 5.11 6.46 4.83 Standard deviation 11.47 - 1.61 1.69 2.05 1.26 1.95 Reliability - - .94 .94 .98 .93 .94 *p < .050; **p < .010. with 90 failing to complete the Time 2 survey, 1 month later. 2 bootcamp attendance (β=-.101, p = .124, f = .025) and Participants who successfully completed the Time 2 survey thus did not mediate the relationship between past did not significantly differ from those who did not with behaviour and bootcamp attendance (β=-.053, p = .194, 2 2 regard to age (t(156) = 1.37, p = .173), gender (χ (1) = 2.38, f = .033). Behavioural automaticity significantly mediated p = .123), marital status (χ (4) = 4.55, p = .235), employment the effect of past behaviour on prospectively measured 2 2 2 status (χ (1)= .266, p = .966), income (χ (1) = 2.46, p = .652), bootcamp attendance at Time 2 (β = .198, p < .001, f = or ethnicity (χ (1)= .195, p = .659). However, there was .121), as Time 1 behavioural automaticity significantly a modest difference between those who completed the predicted Time 2 behavioural automaticity (β = .609, p Time 2 survey and those who did not on the Time 1 study < .001, f = .371), and Time 2 behavioural automaticity variables (Wilks’ Lambda =.916, F(3,153) = 4.68, p = .004, in turn predicted prospectively measured bootcamp η = .084; Past Behaviour F(1,155) = 12.26, p = .001, attendance at Time 2 2 2 η = .073; T1 Behavioural Automaticity F(1,155) = 4.64, (β = .520, p < .001, f = .361). p = .033, η = .029; Intention F(1,155) = 8.93, p = .003, η = .054). Correlations, internal consistency, and Discussion descriptive statistics for all variables are presented in Table 1. In the current study, we aimed to investigate the med- Regarding the model, analysis revealed good fit to iating role of intention and behavioural automaticity data (GoF = .613, RSCR =.993, AFVIF = 2.29, see Figure 1). on the relationship between past and future behaviour All items are loaded significantly onto their respective in a community sample of bootcamp fitness class latent variables (all β’s > .628, all p’s <.001). Past behaviour attendees. Automaticity mediated the relationship predicted intention (β = .526, p < .001, f = .277), Time 1 between past behaviour and future bootcamp atten- behavioural automaticity (β=.626, p < .001, f = .392), and dance. However, while past behaviour predicted boot- Time 2 bootcamp attendance (β = .403, p < .001, f = .246). camp attendance, intention did not have a significant In contrast to expectations, intention did not predict Time effect on future bootcamp attendance. Figure 1. The tested model predicting bootcamp attendance from behavioural automaticity and intention. Note. Paths are presented with standardised parameter estimates. *p < .05, **p < .01, ***p < .001. AUSTRALIAN PSYCHOLOGIST 5 The significant effect of past behaviour on beha- park each time) and timing (e.g., at the same time each vioural automaticity and of behavioural automaticity week) of bootcamp classes likely encouraged more on subsequent behaviour is in line with previous the- automatic responding and habit development. ory that constructs that measure automatic, habitual Past behaviour also predicted bootcamp attendance processes, like behavioural automaticity, account, at directly and importantly beyond the effects of beha- least partially, for the residual effects of past behaviour vioural automaticity and intention. While some residual on future behaviour that are not accounted for by effect of past behaviour would be expected, for exam- intention (Ouellette & Wood, 1998). Repeated occur- ple, due to shared method bias in the behaviour mea- rences of past behaviour are a key determinant of sure, the moderate strength of this effect raises more automatic responding, leading to habit develop- potentially important questions for the tested model. ment (Gardner & Lally, 2018), especially in cases where Specifically, seminal papers in the habit field theorised the behaviour occurs frequently and in a stable con- that the majority of the variance which past behaviour text. Further, once developed, habit encourages future accounted for in the future behaviour beyond intention behavioural occurrences upon encountering relevant was due to habitual responding (Ouellette & Wood, stimuli or cues, triggering behaviour rapidly and auto- 1998). Yet, the strength of the observed residual effect matically by activating behavioural scripts or encoura- of past behaviour in the current study suggests this is ging habitual decisions in favour of undertaking the not the case. Alternatively, this residual effect of past behaviour (Gardner, Rebar, et al., 2020; Hagger, 2020). behaviour on bootcamp attendance may reflect the This effect is again likely to be particularly strong in effect of alternative constructs on bootcamp atten- stable contexts such as bootcamp attendance, as cues dance that were not measured in the current study. such as the consistent timing or location of bootcamp For example, it is possible factors such as implicit beliefs, classes may become triggers for scripts promoting self-regulation, or individual difference factors may actual bootcamp attendance. affect behaviour beyond the effect of behavioural auto- In contrast to our hypotheses and reasoned action maticity and intention (Adriaanse et al., 2014; Hagger & theories (Ajzen, 1991), intention did not mediate the Hamilton, 2021; Hamilton et al., 2018; Phipps et al., relationship between past and future behaviour. We 2020) or even moderate whether an individual responds observed a significant effect of past behaviour on automatically or as a result of a considered decision- intention in line with current theories of social cogni- making (Gardner, Lally, et al., 2020; Phipps, Hagger, tion and reasoned action. That is, past occurrences of et al., 2021; Sas et al., 2022). a behaviour likely affect the social-cognitive beliefs The current study has numerous strengths includ- which underlie intentions, for example, by allowing ing the investigation of a novel population group in for evaluations of the behaviour and increasing the bootcamp attendees, use of a prospective design, likelihood that behaviour is viewed as under one’s and adoption of theory to base hypotheses. Despite control. However, intention did not significantly pre- these strengths, several limitations should be high- dict future behaviour, despite the constructs theoreti- lighted. First, the current study used self-reported cal prominence as the most proximal predictor of measures of bootcamp attendance, and thus mea- behaviour (Ajzen, 1991). While this finding may be surements may be subject to recall and social desir- unexpected from social cognition theories and pre- ability bias. Future research may consider requiring vious evidence in broader physical activity research bootcamp leaders to complete class attendance lists (Hagger et al., 2002), a plausible explanation for this and comparing this with self-reported attendance to lack of significant effect may lie in dual process and provide more accurate data. As an additional benefit, habit theories. Specifically, there are findings of weaker such observation-based behavioural measures would effects of intention as compared to habit in highly also serve to minimise any potential biases caused by stable contexts (Norman & Cooper, 2011; Ouellette & participants declining to complete follow-up surveys. Wood, 1998) and that when a behaviour becomes This was an issue in the current research, where sufficiently habitual, intention is no longer an impor- higher than desirable attrition between the Time 1 tant factor in eliciting a behavioural response and Time 2 measurement points presented a notable (Chatzisarantis & Hagger, 2007; Gardner et al., 2011). limitation and resulted in a modest sample size for Thus, as the current study recruited only those who the final analysis. This is partially addressed through had recently attended a bootcamp class, it is likely the use of PLS-SEM, which is generally accepted to intention played an important role in the initial deci- provide accurate results even in smaller sample sizes sion to engage in these classes, but once attendance (Willaby et al., 2015). However, it is none-the-less began, the stable context (e.g., in the same gym or a concern which should be addressed in the future 6 S. SAS ET AL. ORCID research, for example, through the use of a larger sample and objective or observational measures. Sabryna Sas http://orcid.org/0000-0002-6537-0964 Lastly, due to the correlational nature of this study, Daniel J. Phipps http://orcid.org/0000-0002-0217-0578 the interpretation of the effects reported is based Martin S. Hagger http://orcid.org/0000-0002-2685-1546 solely upon theory. While this is an inherent limita- Kyra Hamilton http://orcid.org/0000-0001-9975-685X tion of correlational research, it may be of particular importance in researching constructs that are likely Data availability statement developed through stable contexts, like behavioural automaticity, where the direction of effect is difficult Data, outputs and supplementary materials are available to establish. Additional research including longitudi- at https://osf.io/uqjxk/ nal and cross-lagged panel designs is needed in order to confirm the hypothesised directions of effects. 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Journal

Australian Psychologist – Taylor & Francis

Published: Jul 4, 2023

Keywords: Habit; dual process theory; social-cognition; physical activity

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