Introduction
A body of research has focused on how men's attitudes toward their body influence exercise-related behavior (3,6,39,40,45). Men who experience dissatisfaction with their body are likely to spend more time exercising and to attend a gym more regularly (31); however, the motivational orientations of such gym goers (i.e., the nature of the rationales behind engaging in exercise at the gym) remain largely under investigated. The current research integrates men's body attitudes and motivation from a self-determination theory perspective (11) to assess the relationship with gym attendance. In addition, a recent theoretical development in self-determination theory incorporates implicit, nonconscious motivation, which can be measured by an implicit association test [IAT (16)]. Incorporating both explicit and implicit motivation measures can contribute to theory by examining the extent to which men who attend the gym regularly do so due to impulsive, automatic motivation or reflective, conscious motivation. This is the first study, to the authors' knowledge, to combine men's body attitudes with explicit and implicit measures of motivation.
A panoply of research outlines men's desire to become more muscular and lower their body fat (18,40,43). Up to 95% of college-aged males report being unhappy with their body appearance, which may lead to body dysmorphia (32). To better understand men's attitudes toward their body and how they influence exercise and dietary behaviors, several scales have been developed. The male body attitudes scale [MBAS (46)] is one such scale that reflects dimensions of male body dissatisfaction, based on theoretical and empirical literature (7). The MBAS outlines 3 dimensions related to muscularity, body fat, and height and has been validated in recent research (Tylka et al., 2005). Although the majority of research has focused on the classification of body dissatisfaction (3,6), less is known about the relationship between motivation and attitudes and their relation to gym attendance.
Research has shown that attitudes alone are unlikely to lead directly to behavior (13) and may be formed consistent with the qualities of an individual's motivation toward engaging in that behavior (19). Accordingly, researchers have included measures of motivation to complement attitudinal constructs (4). Self-determination theory [SDT (10,11)] is a meta-theory of human motivation that has been applied to a range of health-related behaviors, such as physical activity and exercise (1). Self-determination theory also emphasizes the role of an individual's cognitions on the quality of motivation, which is separated into autonomous and controlled forms of motivation. Individuals engaging in behavior through a sense of volition or choice are autonomously motivated and are likely to feel a sense of intrinsic enjoyment or satisfaction when carrying out that behavior (8). Autonomously motivated individuals are likely to persist with gym attendance without external contingencies such as rewards or pressure. By contrast, individuals experiencing controlled motivation perform behaviors for the attainment of external rewards (e.g., money, recognition) or to avoid feelings related to self-esteem such as guilt or shame (29). For instance, males may feel guilty for missing or skipping gym sessions and fear the outcomes (e.g., gaining weight, losing physique). The majority of research using SDT has emphasized the need to support autonomy and facilitate autonomous motivation to engage and persist in health behaviors (12,20,33). However, while autonomous motivation is considered important in behavioral engagement and persistence, controlled motivation may continue to influence behavior when external or self-esteem-related contingencies remain. For instance, individuals who feel ashamed of their body may attend a gym in order to see physical results; as long as the perception (shame) regarding their body persists, so too will the rationales for gym attendance (29).
A further premise of SDT relates to individual differences in dispositional motivational orientations. These orientations reflect relatively enduring and distal influences across a wide range of behaviors and are outlined in the general causality orientations scale [GCOS (9)]. For example, when receiving a promotion at work, an individual might think to ask how much money they will make in their new role, reflecting a control orientation, or if the new role will be challenging or enjoyable, reflecting an autonomy orientation (10). Recent research has identified that these orientations influence behavior at both explicit and implicit levels (25,26). Although several attempts have been made to measure implicit motivation in relation to behavior (25,27), the IAT (16) has increasingly been used. A reaction time-based task, the motivation IAT paradigm, suggests that individuals who hold autonomy orientations will respond quicker to the pairing of self (e.g., “me”) and autonomous (e.g., “freely”) words than the pairing between self and controlled (e.g., “forced”) words. Conversely, individuals who have controlled orientation at the implicit level will sort the latter pairing (self and controlled) quicker. Through a number of studies, Keatley et al. (9,21,25) have found that implicitly measured motivation predicts engagement and performance across a range of health behaviors, including physical activity. The current research extends these findings by investigating the role of implicit motivation alongside other variables related to physical activity (e.g., gym attendance) such as body attitudes.
To conceptualize the patterns of effects of explicit and implicit measures on behavior, several dual-process or dual-system models have been proposed (21,44). It is important to measure implicit and explicit measures together to fully investigate the patterns of effects between the 2 measures in predicting behavior (35–38). Both implicit and explicit measures may act synergistically or antagonistically to predict behavior (36). For instance, an additive pattern suggests that both systems affect behavior independently; multiplicative patterns suggest the 2 measures interact to affect behavior; and double dissociative patterns suggest that implicit processes predict unplanned behaviors, whereas explicit processes better predict planned behaviors (36). Only by taking into account both implicit and explicit measures together can we understand which patterns are supported. In particular, Strack and Deutsch (44) developed the reflective-impulsive model (RIM), which attempts to comprehensively and parsimoniously account for the role of reflective and impulsive processes that influence behavior. In the RIM, the reflective system is related to deliberative, planned behaviors, leading to intentions for future states and goals. The impulsive system, by contrast, comprises processes that arise from the reflective system or perceptual inputs and is underpinned by associative networks. To this extent, explicit, self-report measures are proposed to provide an account of the reflective system, whereas implicit measures, such as the IAT, are well positioned to provide an account of the associative networks.
The aim of the present study was to investigate the influence of men's body attitudes alongside implicit and explicit motivation on gym attendance. We measured these influences while controlling for body mass index (BMI). From this framework, a number of hypotheses were derived. Based on previous research into men's body attitudes and its effects on behavior (7), we hypothesized that men with negative body attitudes would report greater gym attendance (H1). We also hypothesized that explicit measures of motivation at the proximal (i.e., perceived locus of causality [PLOC]) and distal (i.e., GCOS) levels would predict gym attendance (H2). Specifically, autonomous motivation would predict attending the gym for reasons of choice and enjoyment, whereas controlled motivation would reflect gym attendance due to extrinsic reasons or for reasons related to self-esteem. This hypothesis was based on previous literature showing the relationship between types of motivation and physical activity behaviors (5,30). Last, we hypothesized that implicit motivation would predict gym attendance (H3) similar to explicit measures. This hypothesis is based on previous research showing the relation between implicit autonomous motivation and physical activity (1,24).
Methods
Experimental Approach to the Problem
The current study was a cross-sectional study using online resources to measure participants' body attitudes and motivation types. The variables and types of measure were carefully selected based on their precedence in the literature as well as their suitability for answering the research questions.
Subjects
A total of 100 male participants age range of 18–68 years with the median range of 27, (Mage = 30.40, SD = 11.10) participated in the study, with an average BMI for the sample of 25.83 (SD = 6.62). The majority of the sample (57.3%) endorsed health and fitness as their primary reason for attending a gym or fitness centre, following by appearance (16.7%), amateur body building (16.7%), training or competing (8.3%), and other (1%). Participants reported an average gym or fitness centre attendance of 2.46 (SD = 1.71) sessions per week, typically lasting 1.06 (SD = 0.742) hours. We used the Borg Scale (2) to measure typical gym or fitness centre exertion, multiplying scores by 10 to approximate heart beats per minute during routines (M = 124.1 [fairly light to somewhat hard]; SD = 87.1; median = 130). All participant data were entered into analyses, except for one participant who did not provide data for gym attendance (N = 99). Ethical approval was granted by the Curtin University Human Research Ethics Committee. Individuals were eligible to participate in the study if they were male, were fluent English speakers, and attended a gym or fitness centre frequently. Written informed consent was provided by all participants.
Procedure
Data Collection
Participants were recruited online, where they were provided with study information and indicated their consent to participate by clicking the “I agree” button before advancing to the questionnaire. The order of presentation of the measures was randomized, such that participants received either the IAT before or after the questionnaires. The order of scales in the questionnaire was also randomized. Participants progressed through the questionnaires at their own pace, which lasted approximately 25 minutes. Completion of the IAT took approximately 5 minutes. All participants were given a $2 USD inconvenience allowance for participating. Although the IAT was administered online, it is set-up to download and run using the participants' own operating system; therefore, there were no issues relating to lag or internet speeds.
Measures
The revised MBAS (MBAS-R) (The original MBAS 12 (46)—was also tested in the regression models, and a similar pattern of results were found. In keeping with the developments in the literature, we report the revised version in the current article; alternative results using the original MBAS are available from the first author, on request) incorporates some revisions to the original MBAS by Tylka et al. (46), measuring men's attitudes toward their body fat and muscularity. As we were interested in men's attitudes toward their body that could be targeted by attending a gym or fitness centre, we included only the body fat and muscularity subscales of the MBAS-R (We initially included MBAS-height; however, removal of the predictor did not substantially change the results). Participants responded to a series of statements regarding body fat (e.g., seeing my reflection [e.g., in a mirror or window] makes me feel badly about my body fat) and muscularity (e.g., I think my arms should be more muscular) on a 6-point scale from I (never) to 6 (always). Cronbach's α values for the subscale scores for the total muscle (MBASMusc) and body fat (MBASBF) were 0.87 and 0.89, respectively.
The PLOC was adapted to apply to motivation related to attending the gym or fitness centre to exercise and work out. Participants evaluated a series of statements reflective of their underlying motivational regulations (e.g., “I feel under pressure to exercise or work out regularly from people I know well”) using a scale from 1 (“not true at all”) to 4 (“very true”). Weighted means were calculated for the resulting PLOC scores according to previous research to create scales for autonomous motivation (i.e., 2 × intrinsic motivation + identified regulation; Cronbach's α = 0.83) and controlled motivation (i.e., 2 × extrinsic regulation + introjected regulation; Cronbach's α = 0.61) (Although there is some debate regarding the structure of SDT and whether it is on a continuum (24), the current article opted for the calculations shown here, in order to be parsimonious with existing literature in the area).
The GCOS (9) measures individuals' general or dispositional motivation orientations, comprising a series of vignettes and associated responses reflective of autonomous and controlled motivational orientations. An example vignette refers to receiving a new position at a company; participants indicate how likely they will respond by thinking, “Will I make more at this position?” (i.e., control orientation; Cronbach's α = 0.88) or “I wonder if the new work will be interesting?” (i.e., autonomy orientation; Cronbach's = 0.71). Participants rate the likelihood of responding in these ways on a 7-point Likert-type scale from 1 (“very unlikely”) to 7 (“very likely”). There were 12 vignettes in total, each with 2 statements, one pertaining to autonomy orientation, the other pertaining to control orientation.
Implicit autonomous and controlled motivation were measured with the motivational IAT (25,27,36). Words relating to autonomous motivation (i.e., label: autonomous; stimuli: choice, free, spontaneous, willing, authentic) and controlled motivation (label: controlled; stimuli: pressured, restricted, forced, should, controlled) have previously been used to show distinct representations of the 2 motivation orientations. Participants were given information on what the forms of motivation were, emphasizing the differences between them. Words relating to “self” (I, me, my, mine, self) and “others” (they, them, their, theirs, others) were also adopted from previous research in the area (25,27,36). The category others was described to participants as reflecting “not-self,” to prevent comparison with a generalized social comparison group. The standard 5-step IAT was used, in which blocks 1, 2, and 4 comprised 20 practice trials, and blocks 3 and 5 comprised 60 trials (i.e., 20 practices, 40 tests). The critical blocks were counterbalanced. The improved scoring algorithm (17) was used to calculate the implicit motivation D-score, with positive scores reflecting an implicit bias to autonomous and self word pairings. All participants' motivation-IAT (M-IAT) data met the inclusion criteria, as detailed in the improved scoring algorithm (17).
Gym-attending behavior was measured by asking participants to indicate the average number of times they attended gym for a work-out or exercise session in a typical week. This was used as the outcome variable.
Results
Initial data screening for kurtosis and skewness indicated that data could be considered normally distributed. Indicators showed no issue with multicollinearity in the data set. Descriptive statistics and zero-order correlations between study variables are shown in Table 1. Participants' average gym sessions per week correlated significantly with PLOC autonomous motivation (r = 0.52, p < 0.001) and controlled motivation (r = 0.31, p < 0.001). Male body attitudes related to muscle (r = 0.21, p = 0.03) and body fat (r = 0.22, p = 0.03) were also significantly correlated. Finally, implicit motivation was not correlated with average gym sessions per week (r = −0.14, p = 0.18).
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Table 1.: Means and zero-order correlation matrix for motivation measures, male body attitude measures, and average gym sessions per week.*
Hierarchical regression analyses were conducted to assess the unique contribution of predictors to gym attendance. Body mass index was entered in the first step. In the second step, motivation (i.e., PLOCAut, PLOCCon, GCOSAut, GCOSCon, and M-IAT) and male body attitudes (i.e., MBASBF and MBASmus) were entered. Standardized beta coefficients and statistics related to the regression analysis are included in Table 2. Body mass index did not significantly predict gym attendance in the first step: adjusted R2 (Adj. R2) = −0.01, p = 0.97, F(1,87) = 0.002, p = 0.97. The inclusion of the predictor variables on gym attendance in the second step led to a significant increase in variance accounted for: Adj. R2 = 0.35, p < 0.001; F(8,87) = 6.87, p < 0.001; ΔR2 = 0.41, p < 0.001, with the BMI remaining a nonsignificant predictor (β = −0.09, p = 0.45). Average gym sessions per week were significantly predicted by MBASBF (β = 0.32, p = 0.01) but not MBASmus, providing partial support for H1. The PLOCAut significantly predicted average number of gym sessions per week (β = 0.56, p < 0.001), although prediction by PLOCCon was nonsignificant (β = −0.07, p = 0.51); GCOS variables were similarly nonsignificant, indicating partial support for H2. Implicitly measured motivation significantly and negatively predicted average number of gym sessions per week (β = −0.21, p = 0.03), supporting hypothesis H3 (Interaction terms between explicit generalized measures of motivation (GCOS) and the implicit measure of motivation were entered into the third step of the regression model, in additional analyses. These, however, were not significant predictors of behavior and are therefore omitted. Full analyses are available from the correspondent author, on request).
Table 2.: Results of hierarchical regression analyses showing the contribution of explicit and implicit motivational and body attitudinal measures.*
Discussion
The aim of the current study was to investigate the effects of men's attitudes toward their body alongside their implicit and explicit motivation in relation to the number of times they attend a gym, per week. The research adopted a dual-systems framework to conceptualize the patterns of prediction between men's body attitudes, alongside explicit and implicit measures of motivation. A series of hypotheses based on previous literature in the area were systematically tested. The first hypothesis (H1) related to the effect of negative body attitudes toward muscle mass and body fat, as measured by the MBAS. The current research provided partial support for this hypothesis, indicating that men with higher negative views toward their body fat also reported greater average gym attendance per week. Considering that BMI (our control variable) was not a significant predictor of gym attendance, it may mean that individuals attend the gym due to subjective perceptions of body weight (as measured by the MBAS), rather than actual body weight (as measured by the BMI). Given that attitudes toward muscle did not significantly predict gym attendance, it may be that the current sample was more motivated to attend the gym because of perceptions of body fat, rather than muscle mass. It should be noted, however, that participants in the current sample were slightly overweight in terms of their BMI. Notwithstanding these limitations, the present results may suggest that males with higher BMI place more emphasis on weight loss than muscle gain, which is an important consideration for health and exercise professionals in terms of focusing interventions, in that individuals with higher BMI may be more focused on weight-related issues than muscle.
The second hypothesis (H2) was related to the role of explicit motivation types on gym attendance. In the present study, context-specific autonomous motivation significantly predicted higher gym attendance per week, supporting the link between autonomous motivation to engage in physical activity and continued, persistent physical activity behavior (20). This means that individuals who choose to attend the gym with a sense of volition and choice are more likely to attend more often. Although controlled motivation was significantly correlated with gym sessions per week, it was not a significant predictor of gym attendance in our regression analyses, and therefore the hypothesis was not fully supported. It should be noted that controlled motivation (PLOC) was a relatively low alpha level in the current study; however, the scale has been widely used and supported in the literature, and it is not uncommon for research using these scales to report lower reliability for controlled motivation (14,41).
Our final hypothesis (H3) related to implicit motivation, which was found to be a significant negative predictor of gym attendance. In the current study, higher implicit controlled motivation (i.e., indicated by negative D-scores) was predictive of gym attendance as opposed to implicit autonomous motivation. These results indicate that unplanned gym attendance may be predicted by implicit processes. In the present study, it is plausible that unplanned opportunities to attend the gym are what the implicit measure is predicting, rather than habitual responses. The reason for this is that the explicit measure of controlled motivation was significantly correlated with gym attendance behavior but did not show significant independent association with gym attendance. Therefore, when planning and reflecting on reasons to attend the gym (i.e., indicated by the PLOC), individuals are likely to be influenced more by explicit autonomous motivations, when individuals do not plan or form intentions to attend the gym (i.e., a time during the day in which attending a gym becomes suddenly possible (37,38)), implicit controlled motivation may be more predictive of gym attendance.
The present research takes a novel approach in combining SDT with men's attitudes toward their physical appearance for predicting self-reported gym attendance. Although there is a large focus on body and muscle dissatisfaction, contemporary theories of motivation have, to our knowledge, not yet been applied to further understand the influence of differing types of motivation (i.e., controlled or autonomous) and body attitudes on gym attendance. The comprehensive testing of the hypotheses through hierarchical regression allowed the influence of motivational variables on gym attendance to be observed while controlling for BMI. The measurement of motivation at the implicit level can be considered a strength of the present study, in light of recent developments in SDT. Although the M-IAT has been supported in various applications throughout the literature, there remains a general lack of consensus regarding which implicit test best represents influences from the impulsive system (22). Future research should seek to corroborate the present trend in the literature by including other implicit measures, such as the single-category IAT (23) or the go/no-go association task (34). These measures allow for autonomous and controlled motivation to be measured separately, which may clarify the antagonistic patterns of prediction between autonomous and controlled motivation types. Furthermore, inclusion of explicit measures of habitual behavior, such as the behavioral self-report automaticity indices (15), may also be used to establish support for automatic or habitual gym attendance, alongside implicit measures.
The present study carries some limitations that should be noted. Firstly, the sample average BMI was slightly overweight, which may have influenced the responses on measures of body attitude. The cross-sectional design can also be considered a limitation; although the study was sufficiently powered, a prospective-correlational or longitudinal design that establishes the effect of motivation on gym attendance over time may be a useful avenue for future research. The self-reported nature of the scales should also be taken into consideration when interpreting these results. Further research may endeavor to incorporate more objective measurements of behavior (e.g., data from personal exercise-tracking devices, gym, or fitness centre access logs). Lastly, as autonomous motivation is facilitated by the support of psychological needs such as competence and relatedness, the influence of others (e.g., personal trainers, gym partners) on individual motivation at the gym or fitness centre is an important area for further research.
In terms of practical recommendations emerging from the current research, findings may help to guide health and exercise professionals (e.g., personal trainers, coaches) and inform interventions by highlighting the roles of men's body attitudes and different motivation types in influencing gym attendance. Men with negative body attitudes may still exhibit autonomous forms of motivation in relation to gym attendance. Therefore, providing autonomy support (which underpins autonomous motivation), and minimizing extrinsic reasons for gym attendance, may be of importance to establishing long-term, positive health behavior change (28,42). Given the poorer psychological and health outcomes associated with forms of controlled motivation (29), trainers and coaches should shift focus from external appearance to more intrinsic elements of exercise in the gym or fitness centre. The role of implicit, nonconscious processes should also be taken into account. Given that implicit controlled motivation may influence spontaneous gym attendance, the promotion of establishing regular routines, or planning attendance in advance, may be advantageous (5).
Practical Applications
Gym attendance for men may not always be about increasing muscle mass (i.e., the muscular ideal), but as was the case in this study, can also be driven by the desire to lose weight. Both autonomous motivation and implicit controlled motivation positively predict gym attendance, this suggests that health practitioners should encourage autonomous forms of motivation while maintaining awareness of the effects of implicit controlled motivation, that is, unplanned attendance potentially due to feelings of shame or guilt about their body size and shape.
References
1. Banting LK, Dimmock JA, Grove JR. The impact of automatically activated motivation on exercise-related outcomes. J Sport Exerc Psychol 33: 569–585, 2011.
2. Borg GAV. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14: 377–381, 1982.
3. Cafri G, Thompson JK. Measuring male body image: A review of the current methodology. Psychol Men Mascul 5: 18, 2004.
4. Chan DKC, Hagger MS. Transcontextual development of motivation in sport injury prevention among elite athletes. J Sport Exerc Psychol 34: 661, 2012.
5. Chatzisarantis NLD, Hagger MS. Effects of an intervention based on self-determination theory on self-reported leisure-time physical activity participation. Psychol Health 24: 29–48, 2009.
6. Choi P, Pope H, Olivardia R. Muscle dysmorphia: A new syndrome in weightlifters. Br J Sports Med 36: 375–376, 2002.
7. Cohane GH, Pope HG. Body image in boys: A review of the literature. Int J Eat Disord 29: 373–379, 2001.
8. Deci EL, Ryan RM. Intrinsic Motivation and Self-Determination in Human Behaviour. New York, NY: Plenum, 1985.
9. Deci EL, Ryan RM. The general causality orientations scale: Self-determination in personality. J Res Pers 19: 109–134, 1985.
10. Deci EL, Ryan RM. Handbook of Self-Determination Research. Rochester, NY: University of Rochester Press, 2002. pp. 470.
11. Deci EL, Ryan RM. Self-determination theory: A macrotheory of human motivation, development, and health. Can Psychol 49: 182–185, 2008.
12. Dimmock JA, Banting LK. The influence of implicit cognitive processes on physical activity: How the theory of planned behaviour and self-determination theory can provide a platform for our understanding. Int Rev Sport Exerc Psychol 2: 3–22, 2009.
13. Fishbein M, Ajzen I. The influence of attitudes on behavior. In: The Handbook of Attitudes. Mahwah, NJ: Erlbaum. 2005. pp. 173–222.
14. Gagne M. Autonomy support and need satisfaction in the motivation and well-being of gymnasts. J Appl Soc Psychol 15: 372–390, 2003.
15. Gardner B, Abraham C, Lally P. Towards parsimony in habit measurement: Testing the convergent and predictive validity of an automaticity subscale of the self-report habit index. Int J Behav Nutr Phys Act 9: 102, 2012.
16. Greenwald AG, McGhee D, Schwartz J. Measuring individual differences in implicit cognition: The implicit association test. J Pers Soc Psychol 74: 1464–1480, 1998.
17. Greenwald AG, Nosek BA, Banaji MR. Understanding and using the implicit association test: I. An improved scoring algorithm. J Pers Soc Psychol 85: 197–216, 2003.
18. Grieve FG, Helmick A. The influence of men's self-objectification on the drive for muscularity: Self-esteem, body satisfaction and muscle dysmorphia. Int J Mens Health 7: 288–298, 2008.
19. Hagger MS, Chatzisarantis NL. Self-determination theory and the psychology of exercise. Int Rev Sport Exerc Psychol 1: 79–103, 2008.
20. Hagger MS, Chatzisarantis NLD. Integrating the theory of planned behaviour and self-determination theory in health behaviour: A meta-analysis. Br J Health Psychol 14: 275–302, 2009.
21. Hofmann W, Gawronski B, Gschwendner T, Le H. A meta-analysis on the correlation between the implicit association test and explicit self-report measures. Pers Soc Psychol Bull 31: 1369–1385, 2005.
22. Jung KH, Lee J-H. Implicit and explicit attitude dissociation in spontaneous deceptive behavior. Acta Psychol 132: 62–67, 2009.
23. Karpinski A, Steinman RB. The single category implicit association test as a measure of implicit social cognition. J Pers Soc Psychol 91: 16, 2006.
24. Keatley DA, Clarke DD, Hagger MS. Investigating the predictive validity of implicit and explicit measures of motivation on condom use, physical activity and healthy eating. Psychol Health 27: 550–569, 2011.
25. Keatley DA, Clarke DD, Hagger MS. Investigating the predictive validity of implicit and explicit measures of motivation in problem-solving behavioural tasks. Br J Soc Psychol 52: 510–524, 2012.
26. Keatley DA, Clarke DD, Hagger MS. The predictive validity of implicit measures of self-determined motivation across health-related behaviours. Br J Health Psychol 18: 2–17, 2013.
27. Levesque C, Brown KW. Mindfulness as a moderator of the effect of implicit motivational self-concept on day-to-day behavioral motivation. Motiv Emot 31: 284–299, 2007.
28. Markland DA, Ryan RM, Tobin VJ, Rollnick S. Motivational interviewing and self–determination theory. J Soc Clin Psychol 24: 811–831, 2005.
29. McLachlan S, McLachlan S, Keatley DA, Stiff C, Hagger MS. Shame: A Self-Determination Perspective, in Psychology of Neuroticism and Shame. Jackson RG., ed. Hauppauge, NY: Nova Science, 2010. pp. 1–14.
30. Moreno MJ, Parra RN, González-Cutre CD. Influence of autonomy support, social goals and relatedness on amotivation in physical education classes. Psicothema 20: 636–641, 2008.
31. Mosley PE. Bigorexia: Bodybuilding and muscle dysmorphia. Eur Eat Disord Rev 17: 191–198, 2009.
32. Murray SB, Rieger E, Hildebrandt T, Karlov L, Russell J, Boon E, Dawson RT, Touyz SW. A comparison of eating, exercise, shape, and weight related symptomatology in males with muscle dysmorphia and anorexia nervosa. Body Image 9: 193–200, 2012.
33. Ng JY, Ntoumanis N, Thogersen-Ntoumani C, Deci EL, Ryan RM, Duda JL, Williams GC. Self-determination theory applied to health contexts: A meta-analysis. Perspect Psychol Sci 7: 325–340, 2012.
34. Nosek BA, Banaji MR. The go/no-go association task. Soc Cogn 19: 625–666, 2001.
35. Perugini M, Conner M, O'Gorman R. Automatic activation of individual differences: A test of the gatekeeper model in the domain of spontaneous helping. Eur J Pers 25: 465–476, 2011.
36. Perugini M, Richetin J, Zogmaister C. Prediction of behaviour. In: Gawronski B., Payne K., eds. Social Cognition: Measurement, Theory, and Applictions. London, United Kingdom: The Guilford Press, 2010.
37. Rebar A, Loftus AM, Hagger MS. Habits predict physical activity on days when intentions are weak. J Sport Exerc Psychol 36: 157–165, 2014.
38. Rebar A, Loftus AM, Hagger MS. Conscious control and the non-conscious regulation of health behaviour. Front Hum Neurosci 9: 122, 2015.
39. Robert CA, Munroe-Chandler KJ, Gammage KL. The relationship between the drive for muscularity and muscle dysmorphia in male and female weight trainers. J Strength Cond Res 23: 1656–1662, 2009.
40. Segura-García C, Ammendolia A, Procopio L, Papaianni MC, Sinopoli F, Bianco C, De Fazio P, Capranica L. Body uneasiness, eating disorders, and muscle dysmorphia in individuals who overexercise. J Strength Cond Res 24: 3098–3104, 2010.
41. Sheldon KM, Ryan RM, Deci EL, Kasser T. The independent effects of goal contents and motives on well-being: It's both what you pursue and why you pursue it. Social Psychol Bull 30: 475–486, 2004.
42. Silva MN, Markland D, Miderico CS, Vierira PN, Castro MM, Coutinho SR, Santos TX, Matos MG, Sardinha LB, Teixeria PJ. A randomized controlled trial to evaluate self-determination theory for exercise adherence and weight control: Rationale and intervention description. BMC Public Health 8: 234, 2008.
43. Skemp KM, Mikat RP, Schenck KP, Kramer NA. Muscle dysmorphia: Risk may be influenced by goals of the weightlifter. J Strength Cond Res 27: 2427–2432, 2013.
44. Strack F, Deutsch R. Reflective and impulsive determinants of social behavior. Pers Soc Psychol Rev 8: 220–247, 2004.
45. Thompson JK, et al. Exacting Beauty: Theory, Assessment, and Treatment of Body Image Disturbance. American Psychological Association, 1999.
46. Tylka TL, Bergeron D, Schwartz JP. Development and psychometric evaluation of the Male Body Attitudes Scale (MBAS). Body Image 2: 161–175, 2005.
