Introduction

Dancing has substantial benefits for physical and mental health such as decreased depression, anxiety and increased physical and psychological wellbeing [1–4]. At the same time, little is known about the psychological underpinnings of excessive dancing. Although there are theoretical reasons to assume that excessive dancing may have harmful effects for the individual, there have been very few studies that have studied the effects of excessive dance activity, and none that have described the psychological factors and consequences associated with dance addiction.

Given the lack of empirical research in dance addiction, exercise addiction is arguably similar and could be considered as conceptually resembling dance addiction for several reasons. For example, Pierce et al. [5] found that dancers score higher on the Exercise Addiction Scale compared to endurance and non-endurance athletes. Furthermore, both activities require stamina and physical fitness, and for this reason, dance is often classified as a form of exercise [6].

Exercise dependence has been described as a craving for leisure-time physical activity, resulting in uncontrollable excessive exercise behaviour [7–10]. Griffiths [11] suggested that—like most behavioural addictions—exercise addiction manifests in six general symptoms: salience, mood modification, tolerance, withdrawal symptoms, conflict and relapse [12]. Apart from the decline in mental health, exercise dependence and/or addiction is associated with body image distortions [7,13]. The implications of such findings are considerable given that body image disturbances mediate the development of eating disorders [14,15]. Confirming these assumptions, research suggests strong links between exercise addiction and eating disorders [16,17]. Furthermore, it appears that exercise dependence where running is the primary activity is associated with a rigid and inflexible personality pattern [18]. These data suggest that excessive exercising is linked to psychopathology and is a potentially self-harming behaviour.

There are data from several empirical studies suggesting that psychopathology may be present in dance addiction similar to that in exercise addiction. For example, Pierce and Daleng [19] conducted a study with 10 elite ballet dancers and found that dancers rated thinner bodies as ideal and significantly more desirable than their actual body image despite being in the ‘ideal’ BMI range. Furthermore, dancers often continue to dance despite discomfort, “because of the embedded subculture in dance that embraces injury, pain, and tolerance” (p. 105) [20]. In a recent study, Targhetta et al. [21] assessed addiction to the Argentine tango. They found that almost half (45%) of the participants met the DSM-IV criteria of abuse, although a substantially lower prevalence rate (7%) was found when Goodman’s [22] more conservative criteria and higher threshold for addiction were taken into account. Nevertheless, the mental health of ‘addicts’ and ‘non-addicts’ was not assessed and compared in this study, nor has it been investigated by other previous studies. Furthermore, the empirical investigation of motivation underlying excessive dancing is also lacking in the field.

The present study proposes that excessive social dancing is associated with detriments to mental health. More specifically, the study aimed (i) to identify subgroups of dancers regarding addiction tendencies and (ii) to explore which factors account for the elevated risk of dance addiction. In the latter of these aims, demographic variables, (increased) dance activity, psychiatric distress, (decreased) wellbeing, borderline personality disorder symptoms, body shape dissatisfaction and eating disorder symptoms were expected to be contributing factors of dance addiction. Finally, the study aimed (iii) to explore the motivations underlying excessive dancing.

Methods

Results

Latent profile analysis and factor mixture modelling

A latent profile analysis was performed on the items of DAI to identify risk groups of dance addiction. Two to six class solutions were estimated. Table 1 demonstrates that the AIC, BIC and sample-size adjusted BIC continued to decrease as more latent classes were added. Based on AIC, BIC and SSABIC, the four-latent-class solution was noted. After inspection of entropy, the four-class solution reached the maximum level, but the five-class solution also provided an adequate level of entropy (0.84). Based on the LMR test, the five-class solution was accepted.

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Table 1. Fit indices for the latent class and CFA factor mixture modeling.

https://doi.org/10.1371/journal.pone.0125988.t001

In addition to LPA, CFA mixture modeling was also performed with the same class indicators (the seven items of DAI). Table 1 presents the fit indices and entropy. The two-class solution yielded the lowest AIC, BIC and SSABIC values. However, entropy was unacceptably low (0.42). Therefore, the three-class solution was opted for where AIC and BIC values were the second-lowest, and SSABIC values and entropy (0.56) were also acceptable. Nevertheless, when comparing the five-class LPA solution and the three-class CFA mixture modeling solution, both the fit indices (AIC, BIC and SSABIC) and entropy was better for the five-class solution. Therefore the three-class solution of the CFA mixture model was rejected in favour of the five-class solution obtained via LPA.

The features of each class are presented in Fig 1. The first class (low-risk) represented those dancers that scored below the average on all dimensions of problematic behaviour. The second class (medium-risk without social conflicts) and the third class (medium-risk with social conflicts) of dancers represented mild problematic behaviour. The fourth class (at-risk with no social conflict) was close to problematic behaviour, although participants reported no conflict with the social environment. Finally, the fifth class (at-risk with social conflict) represented a high risk of problematic behaviour. In this latter group, all factors showed an elevated level of all problematic dimensions and encompassed 11.4% of the sample. Group differences between classes on DAI were all significant (see Table 2).

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Fig 1. Dance Addiction Inventory item means per DAI class.

https://doi.org/10.1371/journal.pone.0125988.g001

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Table 2. Latent class differences regarding demographic variables, mental health, body image and eating disorder symptoms.

https://doi.org/10.1371/journal.pone.0125988.t002

Differences between the latent classes on the given addiction dimensions were mostly significant (p<0.05) (Salience: Class 1 vs. 2, 4, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5, Conflicts with social environment: Class 1 vs. 2, 3, 4, 5; Class 2 vs. 5; Class 3 vs. 5, Class 4 vs. 5, Mood enhancement: Class 1 vs. 2, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5, Tolerance: Class 1 vs. 2, 4, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5, Withdrawal symptoms: Class 1 vs. 2, 3, 4, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5, Relapse: Class 1 vs. 2, 4, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5 and Craving: Class 1 vs. 2, 4, 5; Class 2 vs. 3, 4, 5; Class 3 vs. 4, 5.

Class differences

Table 2 depicts group differences on the measured variables between latent classes. Class 5 has high values on Intensity and Global Severity Index (both compared to Class 1 and 2), MSI-BPD (compared to Class 1), and SCOFF (compared to Classes 1, 2 and 3), but they are similar to the other groups in terms of demographic variables and BMI. The other problematic group (Class 4) has increased values on Intensity and GSI (both compared to Class 1) and MSI-BPD (compared to Classes 1 and 2). Latent class differences (where Wald χ² p<0.1) are depicted on Fig 2.

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Fig 2. Latent class means across variables.

https://doi.org/10.1371/journal.pone.0125988.g002

Regression models

In order to account for possible overlap among variables, regression using the stepwise method was conducted. Adding variables in three separate blocks (Block1: Intensity, Block 2: SCOFF, GSI, BPD, Block 3: DMI factors) resulted in five separate models with DAI scores as the dependent variable (see Table 3). Each model has significantly better predictive potential than the previous one. Intensity was a constant predictor of DAI variance. The predictive power of BPD decreased across the models as more variables were added. Although GSI was entered, it did not have any predictive effect on DAI in any of the models. The final model (Model 5) incorporated DMI-Escapism, Intensity, DMI Mood Enhancement, SCOFF and BPD as predictor variables (in decreasing effect) and explained 41% of DAI total variance.

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Table 3. Regression model coefficients.

https://doi.org/10.1371/journal.pone.0125988.t003

Discussion

The purpose of the present study was to explore the effect of dance addiction on mental health and wellbeing. To the authors’ knowledge, this is the first study to explore the psychopathology and motivation behind dance addiction. Based on the criteria of addiction by Griffiths [11], five distinct latent classes were established. Classes 1, 2 and 3 encompassed low to moderate risk dancers. About one-quarter of the sample reported high values on all seven criteria of addiction but reported no conflict with the social environment (Class 4). Finally, 11% of dancers belong to the fifth (i.e., most problematic) class scoring high on all addiction symptoms. This is in line with previous research into muscle dysmorphia [40], although no previous study has explored the latent profile of problematic exercisers.

The results also indicated that dance addiction was associated with mild psychopathology, especially with elevated number of eating disorder symptoms, and to a lesser extent, borderline personality traits. Again, this result is in line with previous studies reporting elevated psychopathological factors behind problematic exercisers [6,15,41]. Escapism as a motivational factor (and to a lesser extent Mood Enhancement) is an especially strong indicator of dance addiction, even after accounting for the overlap with other possible mediators such as distress. The role of Escapism has already been much reported in other types of behavioural addiction such as gambling and video gaming [42–44]. Here, escapism as a motivational factor refers to dancing in order to avoid feeling empty or as a mechanism to deal with everyday problems. This provides evidence that to a minority of individuals, dance addiction may be a maladaptive coping mechanism. These results provide support for the notion that excessive dancing constitutes a potential addiction problem for a minority of individuals.

Although the fifth, problematic (high-risk) class is not a clinically diagnosed “addicted” group, it appears that the prevalence of high-risk problematic dancers (11%) is substantially lower than calculated from the DSM-IV criteria of abuse (45%) in a previous study examining tango dancers but slightly higher than the prevalence following Goodman’s criteria of addiction (7%) [21]. Furthermore, in the present study, external validating factors confirmed the existence of a problematic group of dancers. For example, in the problematic group (Class 5), the prevalence of eating disorders was about twice as great as in any other class. Moreover, given that SCOFF does not contain items of exercise as a purging behaviour, the prevalence of eating disorders among problematic dancers was probably underestimated in the current sample. Future studies should also assess whether eating disorder is primary or secondary to dance addiction, that is to say, whether the purpose of excessive dancing is weight-control and/or the motivation to perform leads to disturbances in eating patterns [45]. On the other hand, when mental health was approached with positively-worded items via the Mental Health Continuum, there were no group differences. This is due to the fact mental ‘healthiness’ (i.e., emotional, psychological, and social wellbeing) and mental illness (i.e., major depressive episode and addiction problems) constitute separate unipolar dimensions [46]. On the other hand, the participants comprised a highly educated sample, and high education is associated with lower happiness [47] that is not true for distress where high education is associated with fewer psychiatric symptoms [48]. Finally, it is also possible that the different classes captured different stages of developing addiction thus the differences between classes on the given variables.

Although distress correlates with dance addiction, this association disappeared when other measures were added to the regression model. This may indicate that distress is not directly associated with problematic dancing and that it may arise from other problematic factors such as having an eating disorder. One of the common elements between behavioural and chemical addiction is that they both involve the satisfying of short-term needs at the expense of longer-term negative effects [11,49,50]. From this perspective, excessive behaviours serve as a coping mechanism to regulate unpleasant feelings and cognitive affects [11]. Indeed, the fact that Escapism and Mood Modification above all other motivational factors were associated with dance addiction is in line with the theory of excessive behaviours as coping mechanisms against everyday distress.

This study is not without its limitations. The total population of dancers (and therefore the response rate and representativeness of the results) are unknown. Many individuals did not complete the questionnaire because it was either too long and/or questions may have been too personal and uncomforting. If the latter is true, then the number of dancers at risk of addiction was probably underestimated in the present sample. The data were also self-report and therefore subject to possible biases (e.g., social desirability bias, recall bias, etc.). Furthermore, and similarly to most other Internet-based recruitment methods, self-selection could also have led to possible bias.

Given the lack of research in the field, it is paramount to further clarify the concept of dance addiction. For example, it remains a question as to whether dance addiction is conceptually different from exercise addiction. Although the motivational factors are somewhat different from those underlying exercise dependence [32], the question to what extent do the two concepts overlap remains unanswered. An even more important task is to develop a new measure for dance addiction with a valid cut-off value. Such an instrument should pay close attention to the role of social conflicts as a result of excessive and/or problematic dancing. Given that dancing is a social activity, social conflicts may not arise when the person has only fellow dancers as partners or friends—therefore, the risky behaviour may remain somewhat hidden. Additional items should assess physical consequences of problematic dancing such as the number of injuries and the prevalence of dancing despite being injured. Items exploring negative consequences (such as tiredness at work) should also be added. Given the fact that “runners high” is a good predictor of compulsive running [18], it would be interesting to know whether a similar pattern is present in excessive dancing. Another question could examine the differences between amateur and professional dancers in terms of addiction tendency (although among professional dancers there may be a debate about whether their behaviour is dancing addiction or ‘workaholism’). Finally, it is not known whether the findings outlined here extend to other dance genres, therefore this could be the focus of future studies.

Dancing is very clearly a healthy activity for the majority of individuals therefore one should avoid overpathologizing the behavior [51]. However, excessive dancing may have problematic and/or harmful effects on the individual’s life just as any activity that has a rewarding value [52]. Although causality is yet to be established, clearly, dance addiction has the potential to be associated with mild psychopathology among a minority of individuals.

Supporting Information