Desire Thinking as a Predictor of Gambling Bruce A. Fernie, Gabriele Caselli, Lucia Giustina, Gilda Donato, Antonella Marcotriggiani, Marcantonio M. Spada PII: DOI: Reference:
S0306-4603(14)00011-2 doi: 10.1016/j.addbeh.2014.01.010 AB 4150
To appear in:
Addictive Behaviors
Please cite this article as: Fernie, B.A., Caselli, G., Giustina, L., Donato, G., Marcotriggiani, A. & Spada, M.M., Desire Thinking as a Predictor of Gambling, Addictive Behaviors (2014), doi: 10.1016/j.addbeh.2014.01.010
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Desire thinking as a predictor of gambling
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Desire Thinking as a Predictor of Gambling Word count: 3,741 (all sections included)
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Short Communication
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Bruce A. Fernie King’s College London, Institute of Psychiatry, Department of Psychology, London, UK CASCAID, South London & Maudsley NHS Foundation Trust, UK Gabriele Caselli Studi Cognitivi, Italy
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Lucia Giustina Servizio Tossicodipendendenze, AUSL, Parma, Italy
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Gilda Donato Studi Cognitivi, Italy Antonella Marcotriggiani Studi Cognitivi, Italy
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Marcantonio M. Spada London South Bank University, UK
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Acknowledgments We are grateful to Gaia Ghigliani, Martina Pigionatti and Raffaella Rossin of the University of Pavia for their support in a preliminary pilot study about desire thinking and gambling. A further acknowledgment goes to Sandra Sassaroli and Giovanni M. Ruggiero, who have made this study possible. Author Notes Correspondence should be addressed to: Marcantonio M. Spada, Department of Psychology, Faculty of Arts and Human Sciences, London South Bank University, United Kingdom. Tel. +44 (0)20 7815 7815, e-mail
[email protected].
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Abstract Desire thinking is a voluntary cognitive process involving verbal and imaginal elaboration of a desired target. A desired target can relate to an object, an internal state or an
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activity, such as gambling. This study investigated the role of desire thinking in gambling in a
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cohort of participants recruited from community and clinical settings. Ninety five individuals completed a battery of self-report measures consisting of the Hospital Anxiety and Depression Scale (HADS), the Gambling Craving Scale (GCS), the Desire Thinking
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Questionnaire (DTQ) and the South Oaks Gambling Screen (SOGS). Correlation analyses revealed that gender, educational level, recruitment source, anxiety and depression, craving
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and desire thinking were correlated with gambling. A hierarchical multiple regression analysis revealed that both recruitment source and desire thinking were the only independent
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predictors of gambling when controlling for all other study variables, including craving.
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These findings are discussed in the light of Metacognitive Therapy (MCT).
Key words: desire thinking; gambling; metacognition; metacognitive beliefs; metacognitive therapy; negative emotion.
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1. Introduction Problem gambling can result in financial, interpersonal, legal and vocational costs to the gambler, his/her family and wider society. It has been conceptualised as an addictive
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behaviour that exists on a continuum representing a range of severity (Potenza, 2006). The
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prevalence of problem gambling varies across countries and cultures, with Italian rates estimated at 2.3% for youths and 2.2% for adults (Bastiani et al., 2013).
Craving has been implicated in the maintenance of problematic gambling (Young &
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Wohl, 2009) and has been defined as a powerful subjective experience that motivates individuals to seek out and achieve a desired target (Marlatt, 1987). The Elaborated Intrusion
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(EI) theory (Kavanagh, Andrade, & May, 2004) posits that the duration, frequency and intensity of craving results from a combination of conditioned and voluntary cognitive
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processes. The EI theory purports that internal and external triggers activate automatic associations relating to the absence of a desired target, resulting in a felt sense of deprivation.
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When these associations intrude into consciousness they induce craving – such craving is hypothesized to become perseverative due to a higher order cognitive process that activates
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elaborations of these intrusions. This cognitive process has been termed ‘desire thinking’. Desire thinking is conceptualised as a voluntary cognitive process that orients an
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individual towards images, information and memories of subjectively positive, target-related experiences. These targets can pertain to an activity, an object or an internal state (Kavanagh et al., 2004; Salkovskis & Reynolds, 1994). Desire thinking appears to have two broad domains (Caselli & Spada, 2011): verbal perseveration and imaginal prefiguration. Verbal perseveration concerns the repetitive engagement in verbal thoughts about a desired target and imaginal prefiguration refers to the tendency to prefigure images about desire-related content and experiences. Research has implicated desire thinking in addictive behaviours. For example, studies have suggested that verbal perseveration is a significant predictor of nicotine dependence independently of negative emotion and smoking urges (Caselli, Nikcevic, Fiore, Mezzaluna, & Spada, 2012). Further research has shown that alcohol-dependent drinkers and problem drinkers engage in higher levels of imaginal prefiguration than social drinkers, and
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that alcohol-dependent drinkers report significantly higher levels of verbal perseveration than both problem and social drinkers (Caselli, Ferla, Mezzaluna, Rovetto, & Spada, 2012). Research has also demonstrated that desire thinking is distinct from craving and can induce
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craving (Caselli, Soliani, & Spada, 2013; Caselli & Spada, 2011).
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The difference between automated and conditioned intrusions, and higher order cognitive processing that leads to the elaboration and perservation of thoughts about the desired-target, and thus an intensification of craving states (Caselli, Soliani & Spada, 2013),
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can be conceptualised as analogous to the mechanism proposed to be central to the development and maintenance of psychological disorder hypothesized by Metacognitive
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Therapy [MCT; (Wells, 2009)]. MCT views psychological disorder as resulting from the activation of perseverative cognitive processes (such as worry and rumination) and attentional
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strategies in response to inner events, such as thoughts, emotions, memories and physiological states. MCT refers to these perseverative cognitive processes and attentional
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strategies as components of a Cognitive Attentional Syndrome (CAS), a concept fundamental to building clinical formulations from this perspective.
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CAS configurations are hypothesized to be governed by explicit (often verbal, conscious rules for processing) and implicit (not consciously accessible) metacognitions
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(Wells, 2009). Metacognitions can be defined as “stable knowledge or beliefs about one’s own cognitive system, and knowledge about factors that affect the functioning of the system; the regulation and awareness of the current state of cognition, and appraisal of the significance of thought and memories” (p. 302; Wells, 1995). In MCT, metacognitions have been divided into two broad sets of beliefs (Wells, 2009): (1) negative beliefs concerning the significance, controllability and danger of particular types of inner events, e.g. “It is bad to think thought X” or “I need to control thought X”; and (2) positive beliefs about coping strategies that impact on inner events such as “worrying will help me get things sorted out in my mind” or “brooding will help me solve the problem”.
Research has implicated
metacognitions in both desire thinking (Caselli & Spada, 2010) and problem gambling
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(Lindberg, Fernie, & Spada, 2011). The latter study found that metacognitions independently predicted problem gambling when controlling for negative emotion. From a MCT standpoint craving would be conceptualised as an inner event, whereas
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desire thinking would be conceptualised as perseverative cognitive process activated in
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response to this event. This would suggest that desire thinking should predict addictive behaviour over and above craving because MCT posits that perseverative cognitive processes are more important than activating events in explaining psychological disorder. Accordingly,
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in this study, we hypothesized that desire thinking would predict gambling when controlling for craving.
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2. Method 2.1 Participants
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The sample consisted of 95 (76 male; 19 female) individuals who gamble and was recruited from community (n=47) and clinical (n=48; Servizio Tossicodipendenze, AUSL,
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Parma, Italy) settings. The two recruitment sources strategy was used in order to obtain data from individuals throughout the continuum of gambling. Participants had attained a range of
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educational levels: 3.2% had finished their education after ‘basic school’, 16.8% had reached ‘medium school’, 28.4% had finished formal education after ‘high school’, 44.2% had studied
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to degree level, 5.3% had received postgraduate education, and 2.1% had achieved doctorates. Inclusion criteria were: (1) to be engaging in gambling at least once per week; (2) to be 18 years of age or above; (3) to consent to participate in the study; and (4) to understand spoken and written Italian. The mean age of the sample was 41.6 years (SD=13.5; range=21-68). 2.2 Self-report Measures Hospital Anxiety and Depression Scale [HADS (Zigmond & Snaith, 1983)] The HADS consists of 14 items designed to assess anxiety and depression. The anxiety sub-factor (7 items) consists of items like “I get a sort of frightened feeling as if something horrible is about to happen”. The depression factor (7 items) consists of items like “I feel as if I am slowed down”. Higher scores indicate higher levels of anxiety and depression. The majority of studies examining the factor structure of HADS, in both clinical
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and general populations, have identified and confirmed the two dimensions outlined (Mykletun, Stordal, & Dahl, 2001). The HADS possesses good psychometric properties (Mykletun et al., 2001; Zigmond & Snaith, 1983) with both anxiety and depression sub-
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factors having been shown to independently predict gambling behaviour (Lindberg, Fernie &
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Spada, 2011). Gambling Craving Scale [GCS (Young & Wohl, 2009)]
The GCS consists of 9 items to assess craving for gambling. The anticipation sub-
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factor (3 items) consists of items like “Gambling would be fun right now”, the desire subfactor (3 items) consists of items like “I have an urge to gamble” and the relief sub-factor (3
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items) consists of items like “Gambling would make me less depressed”. The measure utilises a 7-point Likert-type response format that requires respondents to indicate the extent of their
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agreement to the items (from total disagreement to total agreement). Higher scores indicate higher levels of craving for gambling. The GACS has been shown to possess good
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psychometric properties (Young & Wohl, 2009). Desire Thinking Questionnaire [DTQ (Gabriele Caselli & Spada, 2011)]
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The DTQ consists of 10 items designed to assess desire thinking which can be scored according to two sub-factors (verbal perseveration and imaginal prefiguration) or as a total.
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The verbal perseveration sub-factor consists of items like “I mentally repeat to myself that I need to practice the desired activity”. The imaginal prefiguration sub-factor includes items like “I imagine myself doing the desired activity”. The measure utilises a 4-point Likert-type response format that requires respondents to indicate the extent of their agreement to the items (e.g. “Almost never”, “Sometimes”, “Often”, “Almost always”). Higher scores indicate higher levels of desire thinking. The DTQ has been shown to possess a robust factor structure (Gabriele Caselli & Spada, 2011). South Oaks Gambling Screen [SOGS (Lesieur & Blume, 1987)] The SOGS consists of 20 items designed to assess gambling behaviour and the identification of individuals who are problem and pathological gamblers. SOGS total scores of 0 indicate “No problem with gambling”, and total scores of 1-4 indicate “Some problems
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with gambling”, whilst total scores of 5 above identify “Probable pathological gambling”. Although popular, this self-report measure has not been accepted without criticism (e.g. Battersby, Thomas, Tolchard, & Esterman, 2002). However, such criticisms have been
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addressed with the SOGS having been shown to meet the criterion of validity generalization
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(Gambino & Lesieur, 2006). 2.3 Procedure
Ethics approval was obtained from a university ethics board. Participants received the
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booklet containing the self-report measures by direct distribution and all took part on a voluntary and unpaid basis. Participants were informed both verbally and in writing that the
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study was an investigation into the relationship between negative emotion, craving, thinking styles and gambling. Participants were informed that confidentiality would be protected and
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that they could withdraw at any point. Subsequently, participants gave informed consent to take part in the study.
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Following a brief introduction to the study, the participants were instructed to complete the self-report measures. For the completion of the DTQ and the GCS, participants
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were asked to refer to gambling as the desired activity. All participants were debriefed following completion of the study.
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2.4 Data Analysis
The scores from the completed self-report measures were entered into a statistical
software package. Descriptive statistics and tests for normality of distribution were derived. A correlation analysis was then conducted to ascertain the relationships between the variables. Following this, a hierarchical multiple regression analysis, using variables that significantly correlated with gambling as predictor variables, was undertaken. Recruitment source (i.e. whether the participant was recruited from a clinical or community setting), gender and educational level variables were entered into the first step as predictor variables; in step 2, anxiety and depression (HADS) variables were added to the model; in step 3, craving (GCS) was also entered into the model; and finally, in step 4, desire thinking (DTQ) was added to the model.
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3. Results 3.1 Data Description Kolmogorov-Smirnov tests revealed that the distributions of age, educational level,
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anxiety and depression, craving, desire thinking and gambling scores were significantly
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different than normal. Table 1 shows the means, standard deviations and ranges for all the variables. Due to the non-normality of the data distribution, Spearman’s Rho correlation analyses were performed on the data (see Table 1) revealing that gender, educational level,
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recruitment source, anxiety and depression, craving and desire thinking were significantly correlated with gambling.
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3.2 Data Configuration
To ascertain whether the dataset was suitable for hierarchical multiple regression
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analysis, statistical assumptions were evaluated. Multicollinearity was deemed not to be problematic for the dataset for the following reasons: (1) the correlation matrix revealed no
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substantial correlations (r>.9); and (2) an inspection of the Tolerance Index (Ti) and Variable Inflation Factors (VIF) for all predictor variables did not reveal a T i value less than .2 or a
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VIF value under 5.0. Histograms and normality plots also suggested that the residuals were normally distributed. Plots of the regression standardized residuals against the regression predicted
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standardized
values
suggested
that
the
assumptions
of
linearity
and
homoscedasticity were met. Additionally, the Durbin-Watson test suggested that the assumption of independent errors is tenable. 3.3 Hierarchical Multiple Regression Analysis In order to test the experimental hypothesis that desire thinking would be a significant independent predictor of gambling, a hierarchical multiple regression analysis (see Table 2) was conducted using the study variables that were identified as significantly associated with gambling from the earlier correlation analysis. The analysis revealed that recruitment source was the only significant predictor of gambling when the study’s demographic variables were entered into the model. Steps two and three showed that the addition of the anxiety and depression variables and, in the third step, the craving variable did not significantly improve
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the explanatory power of the model. However, on the forth step, the addition of the desire thinking variable did. Indeed, the final model revealed that only recruitment source and desire thinking remained significant predictors of gambling when controlling for all other study
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variables.
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4. Discussion
This study hypothesized that desire thinking would predict gambling independently of negative emotion and craving. However, despite finding that both negative emotion and
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craving were significantly associated with gambling, these variables became non-significant predictors of such behaviour when desire thinking was entered into the hierarchical multiple
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regression model. This result suggests that desire thinking is a stronger predictor of gambling than both negative emotion and craving. This finding also offers support to the MCT
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conceptualisation of psychological disorder, insofar as a perseverative cognitive process (i.e. desire thinking) is an independent predictor of gambling when controlling for craving (an
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intrusive experience relating to a desired target). The finding that the recruitment source was a significant predictor of gambling is as
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expected. We would imagine that individuals who engage in problematic gambling are more likely, than those who do not, to seek help and therefore be found in a clinical setting. What is
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interesting about this result, however, is that desire thinking remained an independent and significant predictor of gambling even when controlling for recruitment source. This suggests that desire thinking may be fundamental in explaining gambling across its continuum of severity. The clinical implications of this study are that modification of the frequency and pervasiveness of desire thinking in treatment may help to reduce problematic gambling. For assessment purposes, gathering information regarding not only the experience of craving, but also desire thinking, may be helpful. In terms of specific interventions to address levels of desire thinking, techniques specific to MCT aimed at disrupting perseverative cognitive processes and promoting adaptive reflexive cognitive responses such as Detached Mindfulness and the Attention Training Technique (Wells, 2009) may be effective. It is also
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plausible to assume that other third-wave CBT approaches related to MCT such as mindfulness (Riley, 2012; Toneatto, Vettese & Nguyen, 2007) may also prove beneficial in targeting desire thinking.
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This study is subject to several limitations that can be addressed by further research.
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Firstly, the self-report measures employed are vulnerable to measurement error through social desirability and self-report biases, context effects and poor recall. Secondly, this study employed a cross-sectional design and this prevents us from drawing conclusions about
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causality. Thirdly, the presence of concurrent psychological disorder (which could account for the observed associations between craving, desire thinking and gambling) was not
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assessed. However, controlling for negative emotion provides a degree of confidence in the specificity of the results. Fourthly, whilst educational attainment was controlled for, socio-
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economic status was not, nor was substance misuse or impulsivity (factors shown to be associated with pathological gambling (Petry, 2001). Fifthly, participants were directed to
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focus on gambling behaviour when completing the DTQ. This may have inflated endorsements of certain items that ask to what extent participants imagine themselves doing
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the desired activity. Finally, previous treatment for gambling with CBT was not recorded and this may have exposed participants to the identification and exploration of the desire thinking
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construct. However, this is unlikely as desire thinking is not directly addressed by standard CBT treatment protocols for problem gambling. Despite these limitations, we believe that findings from this study suggest that desire
thinking plays an important role in problematic gambling. Further research should be conducted to assess the effect of interventions aimed at disrupting the perseveration of desire thinking in modifying problematic gambling.
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Acknowledgements Author B.F. receives salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Research Unit at South
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London and Maudsley NHS Foundation Trust and King’s College London. The views
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expressed are those of the author and not necessarily those of the NHS, the NIHR or the
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Department of Health.
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Tables
IP
7.
8.
9.
.13†
.16
.19
.03
.10
.19
.22*†
.37**
.21*
.26*
.13
-.50**
-.31**†
-.35**
-.35**
-.22**
-.31**
-
-
.81**†
.73**
.58**
.45**
.54**
-
-
-
.72**†
.54**†
.43**†
.45**†
-
-
-
-
.64**
.56**
.57**
-
-
-
-
-
-
.64**
.42**
-
-
-
-
-
-
-
.68**
-
-
-
-
-
-
-
-
X
SD
Range of Scores
2.
3.
4.
41.55
13.45
47 (21-68)
.03
-.23
.14
-
-
1 (0-1)
-
-.09
3.38
.98
5 (1-6)
-
-
US
6.
TE D
T
Table 1: Means (X), Standard Deviations (SD), Ranges, and Two-tailed Spearman’s Rho Correlations of Study Variables.
-
-
1 (0-1)
-
5. SOGS
6.34
5.45
17 (0-17)
-
6. DTQ total
18.55
9.07
29 (10-39)
-
7. GCS total
22.09
10.06
48 (9-57)
8. HADS anxiety
6.82
3.81
17 (1-18)
9. HADS depression
5.68
3.85
15 (0-15)
4. Recruitment source
CR
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3. Education level
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2. Gender
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1. Age
5.
Note: n=95, unless † where n=94; the ‘recruitment source’ variable attests to whether participants were recruited from a community or clinical setting; *p
