Impulsivity Predictors of Problem Gambling and Impaired Control

Transcription

1 DOI /s Impulsivity Predictors of Problem Gambling and Impaired Control John Haw 1 # Springer Science+Business Media New York 2015 Abstract Previous studies of the relationship between impulsivity and problem gambling have produced inconsistent results due to sampling issues and the measures utilised. The current study assessed five facets of impulsivity as predictors of both gambling impaired control and problem gambling in a random sample of 309 regular EGM players. The measures included the UPPS-P for impulsivity, the PGSI for problem gambling and the impaired control scale from the SLUGS. This sample comprised 173 women (56 %) with a mean age of years and 146 men with a mean age of years. Results revealed that lack of perseverance was not a predictor of either gambling measure and that negative urgency was the strongest predictor of both impaired control and problem gambling. The negative urgency result is discussed in the context of the pathways models and the emotionally vulnerable problem gambler. It was also found that for the PGSI, positive urgency, lack of premeditation and sensation seeking were weaker, but still significant predictors. This result is discussed with regard to the use of the PGSI in predicting gambling behaviour and problems in the wording of the positive urgency facet of the UPPS-P. Keywords Impulsivity. Problem gambling. Impaired control Impulsivity has been broadly defined as a Btendency to act spontaneously and without deliberation^ ([1], p. 13). It is considered to play an important role in the initiation of behaviour and to be a personality-based risk factor for a range of mental health disorders including substance abuse, pathological gambling, bulimia nervosa, attention deficit/ hyperactivity disorder, borderline and anti-social personality disorders [2 5]. In total, the DSM-IV had 18 separate disorders that included criteria related to a broad definition of impulsivity [6] and this underlines the importance of defining and measuring the trait. With the types of the behaviours associated with impulsivity being so varied there have been a number of different scales developed that attempt to measure the overall construct [2]. * John Haw john.haw@scu.edu.au 1 School of Business and Tourism, Southern Cross University, Bilinga, Queensland 4225, Australia

2 The most recent is the UPPS which was derived through factor analysis of 20 scales drawn from nine well-validated self-report measures of impulsivity [7]. The four facets of impulsivity that make up the UPPS are negative Urgency (strong impulses particularly under conditions of negative affect), a lack of Premeditation (low tendency to think and reflect on consequences), lack of Perseverance (inability to remain focussed on some tasks) and Sensation seeking (openness to exciting experiences). The UPPS has recently gained considerable attention in research and is reported to have good reliability and validity. The scale conceptualises impulsivity as a broad behaviour and the individual constructs that comprise the UPPS are considered independent or orthogonal predictors of different types of behaviours that may be classified broadly as impulsivity. The UPPS was later expanded to include another facet of impulsivity labelled Positive urgency (UPPS-P). This facet represents strong impulses under conditions of positive affect and it was emphasised that this domain is also an independent, separate construct and not merely the opposite end of the same spectrum for negative urgency [8]. Although positive urgency is considered a separate construct, Berg et al. [2] performed a meta-analytic review of the psychopathological correlates of the five facets of the UPPS-P and found a similar pattern between negative and positive urgency. Both types of urgency had also generated the largest effect sizes across 115 studies with more than 40,000 participants. Berg et al. questioned the distinctiveness of these two urgency facets and suggested that the experience of strong emotion, regardless of valence, may be the key factor in understanding the relationship between impulsivity and psychopathology. The Berg et al. meta-analysis, however, did not include analyses of impulse control disorders as these have not been assessed with the UPPS with only pathological gambling receiving limited attention from research. Logically, it could be argued that impulse control disorders would be the class of disorders best predicted by a valid measure of impulsivity. With regard to studies of problem and pathological gambling, impulsivity has been the most robust of any personality characteristic associated with this these disorders [5]. The terms problem and pathological gambling are often used interchangeably, but pathological gambling tends to require professional diagnosis and is defined by the DSM as clinically persistent and maladaptive gambling behaviour [9]. It is an older term that conceptualises severe gambling problems as a mental illness. Problem gambling is often considered a precursor to pathological gambling [10] and some scales such as the South Oaks Gambling Screen (SOGS) label the lower categories in terms of problem gambling and the most severe category as probable pathological gambler in recognition of the hierarchy and the need for further, qualified diagnosis. However, this concept represents what Svetieva and Walker [11] call an Baddiction-based^ view. More recently a Bproblem-centred^ view, derived from public health models, has dominated [11] and this has seen the term problem gambling gain significance as a more relevant description of the level of gambling related harm experienced by the individual and community. Scales such as the Problem Gambling Severity Index (PGSI) still have a hierarchy from non-problem to problem gambling but this is considered a more accurate reflection of the problem gambling continuum rather than the traditional description of a pathological gambler as a discrete entity [12]. Prior to the DSM 5 pathological gambling was classified as an impulse control disorder and impulsivity has been considered within models of pathological gambling since the early 1970 s [13]. However research assessing the relationship between impulsivity and pathological gambling did not gain momentum until the 1990 s. This research was largely cross-sectional, utilising gamblers in treatment and paper and pencil tests similar to that of the UPPS [14 17]. Since then experimental and longitudinal studies have also implicated impulsivity as an important risk factor for problem gambling [5].

3 Problem gambling is defined differently to pathological gambling and definitions of problem gambling incorporate experiences of impaired control over gambling and the harmful consequences that may arise from this. For example Neal, Delfabbro and O Neil ([18], p. i) defined problem gambling as B.difficulties in limiting money and/or time spent on gambling which leads to adverse consequences for the gambler, others, or the community^. The most common measures of problem gambling, the Problem Gambling Severity Index (PGSI) and the SOGS, focus almost exclusively on measuring the adverse consequences of gambling rather than the impaired control aspect of this definition. Of the facets of impulsivity that have been tested with problem gambling, one of the most consistent predictors is impulsivity under conditions of negative affect [19,20]. Although emotions are not typically considered in the conceptualisation of personality traits, Blaszczynski and Nower [4] included emotions in two of the three hypothesised pathways to problem gambling. One of these pathways included impulsivity as a mediator of the relationship between negative emotion and problem gambling and both Clarke [19] and Haw [20] found support for this. With regard to the UPPS scale, negative urgency is the facet that measures impulsivity under conditions of negative affect. A meta-analysis conducted by MacLaren et al. [21] investigated impulsivity traits as measured by the UPPS as predictors of problem gambling and found a significant effect for both negative urgency and lack of premeditation, but no substantial effect for either lack of perseverance or sensation seeking. There were insufficient studies of positive urgency and problem gambling to be included in the meta-analysis, however, since then, a British study by Michalczuk et al. [22] has investigated the issue. This study compared 30 pathological gamblers with 30 healthy controls on all facets of the UPPS-P. The groups were controlled for age, gender, years of education and verbal IQ and Michalczuk et al. found that pathological gamblers scored higher than the controls on negative urgency, positive urgency, lack of premeditation and lack of perseverance, but not sensation seeking. More recently, Blain et al. [23] investigated the relationship between the UPPS-P and problem gambling in a community sample of 200 Australian gamblers. They controlled for gender and age and reported that only negative and positive urgency were predictive of problem gambling scores as measured by the PGSI. Lack of perseverance and sensation seeking were weakly correlated with problem gambling (negatively for lack of perseverance) but failed to achieve significance in the regression model. Lack of premeditation was shown to not have any relationship with problem gambling. When considering the MacLaren et al. meta-analysis, the Michalczuk et al. [22] group comparison and the Blain et al. correlational study, it does appears that negative urgency is the most consistent predictor of problem gambling. Sensation seeking and lack of premeditation have had mixed results and lack of perseverance is typically not associated with problem gambling. Positive urgency, has had support in the few recent studies that have tested this and it appears to be recording similar effect sizes to negative urgency [22,23]. Most studies of problem gambling that included positive urgency were not particularly robust with regard to specific sampling issues. Initial support for positive urgency in a sample of gamblers was provided by Cyders et al. [8] and Cyders and Smith [24]. In both studies the participants were undergraduate students who are not typically representative of gamblers or problem gamblers [25]. In particular, these types of samples tend to be at lower risks for gambling problems and this leads to positively skewed distributions for the problem gambling variable along with issues of generalisability.

4 The Blain et al. [23] study appeared to improve on the seminal studies of positive urgency by recruiting a sample of 267 gamblers from the community. The sample was recruited online but may not be representative of the problem gambling population as it included 32 undergraduate psychology students and the majority (77 %) of the total sample was aged under 49 years. The criterion for being identified as a gambler was gambling at least once in the past 6 months and the most popular gambling form was lottery tickets with a mean frequency of 1.87 times per year. Hence, the sample appeared to contain low frequency gamblers who predominantly participated in a relatively non-problematic form of gambling and would be considered at minimal risk for developing problems [26]. Surprisingly, 11 % of the sample met the criteria for problem gambling and a further 26 % were at moderate risk for problem gambling according to the PGSI classification. There were problems with the skewness of the distributions and Blain et al. reported a very high correlation between positive and negative urgency of rs=0.78. It was due to multicollinearity that Blain et al. needed to separate negative and positive urgency and analyse two regression models. Other studies [24,27,28] have also found correlations consistently appearing above r=0.60 between positive and negative urgency which supports Berg et al. [2] questioning the distinctiveness of these two impulsivity facets. The other issue for gambling research recruiting community samples is the measure of problem gambling used. Instruments such as the SOGS and PGSI are widely reported but Svetieva and Walker [11] argued that the items in both are still rooted in the addiction-based view of problem gambling and fail to adequately measure gambling related harm. Furthermore neither were designed as measures for research interested in predicting gambling behaviour. Rather, these instruments have a history of use in measuring gambling related harm in the community. Consequently, these scales classify participants into discrete categories, which serves a purpose for the treatment centres and public policy, but when left in their original continuous form for ungrouped data analysis result in severely positively skewed distributions. These distributions can be impossible to transform [23] and subsequently diminish the statistical validity of the analyses undertaken. The problem is exacerbated by the recruitment of gamblers from non-problematic forms of gambling. The Productivity Commission [26] noted that playing electronic gaming machines (EGMs) had between a 7 to 17 times greater risk of problem gambling compared to playing lotteries. This risk factor was up to 2 times for gambling on racing and casino games compared to lotteries. Previous studies have tended to recruit participants with varying level of exposure to the problematic forms of gambling. This variance between studies in recruitment practices may explain the variance in results with regard to the specific impulsivity predictors of problem gambling. To address the conceptual and methodological issues of problem gambling scales, Blaszczynski et al. [10] developed a three-item measure of impaired control as part of the Sydney Laval Universities Gambling Screen (SLUGS). Defining impaired control as difficulties in resisting the urge to gamble and/or spending more time and money than intended, Blaszczynski et al. [10] found that recreational gamblers commonly experienced impaired control. This concurs with the findings of O Connor and Dickerson [29] and the analysis of prevalence data by Productivity Commission [26]. Most importantly, however the concept and definition of impaired control focuses on difficulties in self-regulation. This appears to align itself better with the concept of impulsivity than the harmful consequences arising from gambling as measured by the SOGS and PGSI. For example, the PGSI asks gamblers if they went back another day to try to win money and did they ever borrow money or sell things in order to gamble. These items appear to lack the spontaneity and the absence of deliberation that is associated with definitions of impulsivity.

5 The UPPS-P has gone some way to clarifying and consolidating the disparate measures of impulsivity. However, the results for studies on positive urgency are less convincing and studies that test the UPPS-P on gamblers are not achieving consistent results with the exception of negative urgency. This may be due to the samples recruited and/or the measures of problem gambling utilised. The aim of the current study is to assess the relationship between the UPPS-P and problem gambling on a community sample of regular EGM players. The intention is to include a specific measure of gambling impaired control (SLUGS) along with a more common measure of gambling related harm (PGSI). Method Participants A total of 309 participants across Australia were recruited via random digit telephone dialling. Of these, 173 (56 %) were women and 146 (44 %) were men. The mean age of participants was years (SD=16.01) and ranged from 18 to 87 years. The women in the sample were slightly older than the men with a mean age of years compared to years. The median frequency of EGM play per year was 52 (range=26 365) and the median expenditure on EGMs per year was $1040 (range=5 60,000). Materials The 59-item UPPS-P [7,8], the 9-item PGSI [30] and the 3-item impaired control scale from the SLUGS [10] were administered via telephone interview. The SLUGS and PGSI were framed within the past 12 months and the UPPS-P was a lifetime measure. The SLUGS response options were altered from the original visual analogue scale [10] to the 4-point scale never to almost always. This is the same response scale as used in the PGSI as all the problem gambling questions were asked in the same section of the survey. The SLUGS subscale recorded the lowest reliability figure with a Cronbach s alpha of This figure is acceptable given there was only 3-items in this scale and the reduction in the number of response options compared to the original SLUGS. Also, the size of the reliability coefficient is not considered problematic due to the large sample size in the current study and the associated increased power. The lowest reliability coefficient for the other scales was 0.83 for lack of perseverance. Procedure The full UPPS-P and the SLUGS subscale were included in a survey of regular gamblers as part of a larger study that included the PGSI. Prior to the survey commencement, ethics approval was obtained from the university ethics committee. A market research company with experience in problem gambling research undertook recruitment using a Computer Assisted Telephone Interview (CATI) procedure for all states in Australia. Participants were required to be over 18 years of age and were asked for their frequency of gambling, in the past 12 months, across a variety of gambling forms. From the original 620 recruited, a sub-sample of 309 EGM

6 players who gambled a minimum frequency of 26 times per year (once fortnightly on average) was extracted for the final data set. Results Before assessing the UPPS-P as a predictor of problem gambling some basic grouped and ungrouped analyses were undertaken as part of the data screening process and to also allow comparisons with other published studies. Each participant s total score for the PGSI was calculated and categorised according to the instrument instructions [30]. Table 1 provides the frequencies for each category across both genders. The mean PGSI scores were 3.26 for men (SD=4.62) and 3.14 for women (SD=4.98) which were not significantly different (p=0.82). There was also no significant difference (p= 0.36) on the impaired control scale of the SLUGS between men (M=2.45, SD=2.15) and women (M=2.67, SD=2.07). It s worth noting that 62 % of participants reported experiencing harm from their gambling in the past 12 months and 82 % reported experiencing some impaired control (ie. scored at least 1 on either scale). Table 2 provides the mean impulsivity scores for each gender. An independent samples t- test revealed that there were significant differences between men and women in lack of perseverance, sensation seeking, and positive urgency with the male mean score significantly greater than the female mean score. Table 3 provides the correlations between all variables for men (below the diagonal) and women. Overall, there was a similar pattern between men and women in the relationship amongst variables although there was a tendency for the correlations to be stronger for men than women. The PGSI and SLUGS correlated highly with each other, as did negative and positive urgency for both genders. Regression Analysis Inspection of histograms for the impulsivity, problem gambling and age variables showed that negative urgency, sensation seeking, age and the SLUGS were normally distributed. There was some slight positive skewness for lack of perseverance and lack of premeditation and this positive skewness was increased for positive urgency. However, the PGSI was the most positively skewed and this was reflected in the skewness figure which was considerably higher than positive urgency (2.32 vs. 1.51). This pattern was more pronounced with kurtosis figures as the PGSI recorded a kurtosis figure almost 3 times larger than the next largest figure which was for lack of premeditation (5.76 vs. 1.98). With only seven predictor variables the effects of non-normality are minimised Table 1 PGSI classification frequencies for men and women PGSI category Men Women Total Non-problem 46 (34 %) 72 (42 %) 118 (38 %) Low-Risk 36 (27 %) 39 (23 %) 75 (24 %) Moderate Risk 35 (26 %) 39 (23 %) 74 (24 %) Problem Gambling 19 (14 %) 23 (13 %) 42 (14 %)

7 Table 2 Mean impulsivity scores (SD) for men and women Impulsivity facet Men Women p Negative urgency (9.13) (8.68) 0.60 Lack of premeditation (6.27) (6.08)) 0.56 Lack of perseverance (5.50) (4.81) 0.04 Sensation seeking (8.98) (8.19) 0.00 Positive urgency (10.95) (8.20) 0.00 with such a large sample, however, the violations of normality for the PGSI warrant caution interpreting results for this variable. Of greater concern for regression analysis is the presence of multivariate outliers and multicollinearity and these were tested separately for impaired control and problem gambling. Impaired Control (SLUGS) With the SLUGS impaired control subscale as the criterion variable and age, gender and the UPPS-P as the predictor variables, Mahalanobis distances were calculated and three multivariate outliers were identified. These were deleted leaving 306 in the final sample. Inspection of the standardised residual scatterplot showed that the assumptions of normality, linearity and homoscedasticity were met although there was some minor heteroscedasticity present. The shape of this heteroscedasticity suggested the model was less accurate at predicting higher scores on the SLUGS. Multicollinearity was not an issue with the lowest tolerance figure being 0.52 for negative urgency and an accompanying VIF of With gender and age entered at Step 1 and the UPPS-P at Step 2, the regression results revealed that age and gender explained a significant 11 % of the variance in gambling impaired control as measured by the SLUGS with R 2 =0.11, adjusted R 2 =0.11, F (2303)=18.95, p< The addition of the UPPS-P facets explained a further 17 % of the variance in impaired control with R 2 =0.28, adjusted R 2 =0.26, F (7298)=16.29, p< As shown in Table 4, only age and negative urgency were significant predictors of gambling impaired control for EGM players. Table 3 Pearson s correlation between variables for each gender (men below the diagonal) Age PGSI SLUGS NegU LPremd LPersev SS PosU Age PGSI SLUGS NegU LPremd LPersev SS PosU All values of r 0.18 significant at the p<0.05 level

8 Table 4 Hierarchical regression results for impulsivity predictors of impaired control (n=306) B(95%CI) SEB β sr 2 t p Step 1 Constant 4.62 (3.54, 5.71) Age 0.04 ( 0.06, 0.03) Gender 0.31 ( 0.14,0.76) Step 2 Constant 1.56 ( 0.44, 3.56) Age 0.03 ( 0.05, 0.01) Gender 0.24 ( 0.20, 0.69) NegU 0.09 (0.05, 0.12) LPremd 0.01 ( 0.056, 0.03) LPersev 0.02 ( 0.03,0.06) SS 0.02 ( 0.04, 0.01) PosU 0.02 ( 0.01, 0.05) Problem Gambling (PGSI) With the PGSI as the criterion, Mahalanobis distance figures resulted in the deletion of one case leaving a sample of 308. The standardised residual scatter plot showed the assumption of linearity was met but there was heteroscedasticity and a violation of normality. The square root transformation of the PGSI variable improved the heteroscedasticity to an acceptable level but a square root transformation of positive urgency was also required to improve normality. Multicollinearity was not an issue with the lowest tolerance figure being 0.53 for negative urgency and an accompanying VIF of Following the same process as the SLUGS, with gender and age entered at Step 1 and the UPPS-P at Step 2, the regression results revealed that age and gender explained a significant 14 % of the variance in gambling related harm as measured by the PGSI with R 2 =0.14, adjusted R 2 =0.14, F (2, 305)=24.87, p< The addition of the UPPS-P facets explained a further 21 % of the variance in impaired control with R 2 =0.35, adjusted R 2 =0.34, F (7300)= 23.20, p< As shown in Table 5, age, negative urgency, lack of premeditation, sensation seeking and positive urgency were all significant predictors of problem gambling for EGM players. Discussion The aim of the current study was to assess the relationship between the impulsivity facets of the UPPS-P and problem gambling with a random sample of regular EGM players. When using the gambling impaired control scale from the SLUGS as the criterion variable, the only impulsivity predictor was negative urgency. When using the PGSI as the measure of problem gambling, negative urgency, along with lack of premeditation, sensation seeking and positive urgency were all significant predictors. In both models age and gender were controlled and, in both models, age was also a significant negative predictor of scores on both the SLUGS and the PGSI.

9 Table 5 Hierarchical regression results for impulsivity predictors of problem gambling (n=308) B (95 % CI) SE B β sr 2 t p Step 1 Constant 3.09 (2.45,3.73) Age 0.03 ( 0.04, 0.02) Gender 0.06( 0.32,0.21) Step 2 Constant 1.42(0.57,2.28) Age 0.02( 0.03, 0.01) Gender 0.15( 0.40,0.11) NegU 0.05(0.03,0.07) LPremd 0.02(0.00,0.05) LPersev 0.00( 0.03,0.03) SS 0.02( 0.04,0.00) sqrtposu 0.19(0.02,0.36) The result for negative urgency has considerable support in the literature on problem gambling (e.g., [2,19 21,23]). Negative urgency measures strong impulses under condition of negative affect and this was shown in the current study to be the sole impulsivity predictor of impaired control and the strongest impulsivity predictor of problem gambling for a sample of regular EGM players. This result provides varying support for two of the three pathways in Blaszczynski and Nower s [4] model of problem gambling; the emotionally vulnerable problem gambler and the antisocial impulsivist. The emotionally vulnerable problem gambler is characterised by participation in gambling to help moderate negative affective states such as depression and anxiety. It is also characterised by high levels of impulsivity [4]. This type of gambler uses gambling to emotionally escape aversive affective states and this may be through the process of disassociation and narrowed attention. There is a tendency for this to occur more for older women and EGM players [4]. The current study controlled for form of gambling, age and gender and this may have helped highlight the relationship between the negative urgency facet of impulsivity and the gambling measures over studies that did not control these variables and included less problematic forms of gambling (e.g., [8,23]). Overall, the results of the present study confirm that the negative urgency impulsivity trait is one of the strongest predictors of regular EGM players experiencing problems self-regulating their gambling behaviour and also experiencing harm from it. The anti-social impulsivist type is a more complex pathway to problem gambling and can be associated with anti-social personality disorder, attention deficit problems, comorbid substance disorders and a lower level of general psychosocial functioning. The current study is more limited in its support for this path over the emotionally vulnerable gambler as more extensive psychological and clinical history measures are required. There were however a number of impulsivity facets related to the PGSI, including lack of premeditation, positive urgency and sensation seeking which may initially appear to lend support to Blaszczynski and Nower s[4] description of this pathway. However, these three impulsivity facets of the UPPS- P did have some issues in the analysis with the PGSI. With regard to the PGSI, four of the five impulsivity facets were significant predictors with only lack of perseverance failing to achieve significance in the final model. This result for lack

10 of perseverance and the significant result for negative urgency both have good support from previous research and match the results for the SLUGS. However, the result for sensation seeking, positive urgency and lack of premeditation are more difficult to align with past research and seem to only add to the inconsistent results of past studies. One explanation for the inconsistent results for the PGSI could be the measure itself. Svetieva and Walker [11] questioned the validity of the scale as a measure of harm and statistically the scale has shown to be problematic in analysis due to non-normality [23]. In the current analysis, the large proportion of zero scores required that all PGSI total scores be transformed by adding one and then calculating the square root. This improves the distribution and the chances of obtaining a significant result but reduces the interpretability of the result and warrants caution. Also, in the current study sensation seeking, positive urgency (which also underwent square root transformation) and lack of premeditation were not as strongly related to problem gambling as negative urgency, with each only adding around 1 % extra in terms of unique explained variance. Therefore their statistical significance in the model may be due to the high power of the current analysis. With two analyses on the same data set (PGSI and SLUGS), a Bonferroni correction of alpha to would see both lack of premeditation and positive urgency fail to achieve statistical significance. This correction would not affect the result for the SLUGS. Sensation seeking would still be statistically significant in the PGSI model, but the effect remains almost negligible. A further problem with the sensation seeking variable is that the result indicated a negative relationship with problem gambling. That is, higher scores on sensation seeking predicted lower scores on the PGSI. This suggests that sensation seeking is a protective factor for problem gambling. This assertion does not have support in any of the other studies of the UPPS and the problem gambling. Looking more closely at the items that comprise sensation seeking, it could be argued that these items measure more than just sensation seeking, but also the number of other activities participants engage in. For example, there are a number of items that ask if participants would enjoy a range of activities such as scuba diving, skiing and water skiing, flying an airplane, jumping out of an airplane, and fast paced sports and game. It could be argued that a gambler who is willing to engage in a range of activities has more options for spending their time than just gambling and they may also have a greater social support network through these activities. This may be the protective factor. However, this is speculative and given the lack of support for the current sensation seeking result in the literature it may also simply be a spurious finding. A closer inspection of the UPPS-P items also offers an explanation for why relationships appear stronger between the UPPS-P and the PGSI than they do between the UPPS-P and impaired control. Of particular interest to the current study was the positive urgency facet and claims that it may not be orthogonal to negative urgency [2]. In the current study, both negative and positive urgency were strongly correlated with both the PGSI and impaired control. However, for the impaired control model only negative urgency remained significant once gender, age and all other impulsivity facets were entered. Positive urgency did retain significance in the PGSI model which supports past studies (e.g., [23,24]) but its unique contribution was much weaker than that of negative urgency. One explanation for the strength of positive urgency as a significant predictor of the PGSI may be found in the wording of the items in the UPPS-P. The positive urgency scale appears to not be purely a measure of impulsivity but more a measure of the adverse consequences of impulsivity. It could be argued that ten of the fourteen items that make up the positive urgency scale are actually measuring the adverse consequences of impulsivity and this may inflate its relationship with the PGSI as the PGSI contains items that measure of the adverse consequences of gambling. For example, there is no adverse

11 consequence associated with the items BI am surprised at the things I do while in a great mood^ and BI tend to act without thinking when I am really excited^ but the bulk of the items that make up the positive urgency scale do include adverse consequences such as BWhen I am very happy, I can t seem to stop myself from doing things that can have bad consequences^, BWhen I am in a great mood I tend to get into situations that could cause me problems^ and BOthers are shocked or worried about the things I do when I am feeling very excited^. These aren t simply examples of acting impulsively when in a positive mood, but also include harmful or adverse consequences. There are also harmful consequences implied with the negative urgency items, but not to the same extent with perhaps half the items being considered double-barrelled in this way. The items that comprise the other measures of impulsivity also appear to be free of any adverse consequence in their wording and are more behavioural in nature. Cyders et al. [8] did use three experts in determining the items that comprise the positive urgency scale but perhaps more research is needed on these. In the current study positive urgency was not related to difficulties in self-regulation of gambling behaviour (impaired control) but it was positively related to gambling with harmful consequences (the PGSI). It may be that the harmful consequences part of the positive urgency scale has inflated this relationship. One of the strengths of the current study is the recruitment of a random sample of regular EGM gamblers who are at greatest risk for developing problems with their gambling. The study is also the only study of impulsivity to include a measure of gambling impaired control and this scale was shown to better meet the assumptions of inferential statistics than the more common measure of problem gambling. The results of both measures added further support to the findings of past studies that lack of perseverance is not related to gambling behaviour and that negative urgency is the strongest impulsivity predictor of gambling impaired control and harmful consequences. The difference between the results for each gambling measure highlighted some statistical issues with the PGSI model and also an issue with the wording for positive urgency. This may explain past results linking positive urgency to gambling related harm and future research should undertake detailed item analysis of the positive urgency scale. Compliance with Ethical Standards Conflict of Interest The author has received funding support and provided consultancies to organisations directly and indirectly benefiting from gambling, including Australian governments and industry operators. Informed Consent All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study References 1. Carver, C. S. (2005). Impulse and constraint: perspectives from personality psychology, converence with theory in other areas and potential for integration. Personality and Social Psychology Review, 9(4), Berg, J. M., Latzman, R. D., Bliwise, N. G., & Lilienfeld, S. O. (2015). Parsing the heterogeneity of impulsivity: a meta-anlytic review of the behavoral implications of the UPPS for psychopathology. Psychological Assessment. doi: /pas

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