Predictors of Relapse in Problem Gambling: A Prospective Cohort Study

Transcription

1 Predictors of Relapse in Problem Gambling: A Prospective Cohort Study David P. Smith, Malcolm W. Battersby, Rene G. Pols, Peter W. Harvey, Jane E. Oakes & Michael F. Baigent Journal of Gambling Studies e-issn DOI /s

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3 DOI /s ORIGINAL PAPER Predictors of Relapse in Problem Gambling: A Prospective Cohort Study David P. Smith Malcolm W. Battersby Rene G. Pols Peter W. Harvey Jane E. Oakes Michael F. Baigent Ó Springer Science+Business Media New York 2013 Abstract To explore the variation of predictors of relapse in treatment and support seeking gamblers. A prospective cohort study with 158 treatment and support seeking problem gamblers in South Australia. Key measures were selected using a consensus process with international experts in problem gambling and related addictions. The outcome measures were Victorian Gambling Screen (VGS) and behaviours related to gambling. Potential predictors were gambling related cognitions and urge, emotional disturbance, social support, sensation seeking traits, and levels of work and social functioning. Mean age of participants was 44 years (SD = years) and 85 (54 %) were male. Median time for participants enrolment in the study was 8.38 months (IQR = 2.57 months). Patterns of completed measures for points in time included 116 (73.4 %) with at least a 3 month follow-up. Using generalised mixed-effects regression models we found gambling related urge was significantly associated with relapse in problem gambling as measured by VGS (OR 1.29; 95 % CI ) and gambling behaviours (OR 1.16; 95 % CI ). Gambling related cognitions were also significantly associated with VGS (OR 1.06; 95 % CI ). There is consistent association between urge to gamble and relapse in problem gambling but estimates for other D. P. Smith (&) M. W. Battersby R. G. Pols P. W. Harvey J. E. Oakes M. F. Baigent Flinders Human Behaviour and Health Research Unit, Department of Psychiatry, Flinders University, GPO Box 2100, Adelaide, SA 2001, Australia david.smith@flinders.edu.au M. W. Battersby malcolm.battersby@flinders.edu.au R. G. Pols rene.pols@flinders.edu.au P. W. Harvey peter.harvey@flinders.edu.au J. E. Oakes jane.oakes@flinders.edu.au M. F. Baigent michael.baigent@flinders.edu.au

4 potential predictors may have been attenuated because of methodological limitations. This study also highlighted the challenges presented from a cohort study of treatment and support seeking problem gamblers. Keywords Problem gambling Relapse Predictors Cohort study Introduction Problem gambling is a serious public health concern at an international level (e.g. Germany, Holland, Spain, Switzerland, Australia, United States, Britain and Hong Kong) and occurs more frequently in younger populations (Becona 1996; Bondolfi et al. 2000; Delfabbro 2009; Shaffer and Hall 2001; Wardle et al. 2007; Wong and Ernest 2003). Comorbid mental disorders such as depression and anxiety are common in both treatment and non-treatment seeking problem gamblers (Lorains et al. 2011). The availability of treatments for gambling disorders has increased in the past 10 years and are similar to those in other addictions, for example brief interventions (Hodgins 2009), psychotherapies (Gooding and Tarrier 2009), and peer-support (Oei and Gordon 2008). However, a discernible threat to treatment efficacies exist due to high rates of relapse (Hodgins et al. 2007; Hodgins and el-guebaly 2004) as in other addictive behaviours and psychological disorders [e.g. alcohol use, anxiety disorder, and obsessive compulsive disorder (Brandon et al. 2007)]. In the effort to identify predictors of relapse, a range of outcome measures have been used to quantify the behaviour. In two studies conducted in The Netherlands, relapsers were classified as either those that responded in the affirmative to Do you think that you have a gambling problem again? (Goudriaan et al. 2007) or as the presence of any gambling behaviour in a one year follow-up time period (Wilde et al. 2013). In a United States study that examined factors associated with length of gambling abstinence attempt, relapsers were defined as either delayed if they were current PG (pathological gambling) and had at least one sustained period of gambling abstinence C3 months, or as immediate according to PG s who had never remained abstinent for a period [2 weeks (Daughters et al. 2005). In a Canadian study on retrospective and prospective precipitants to relapse, relapse was defined as gambling after 2 weeks of abstinence where all participants in the study had a goal of abstinence (Hodgins and el-guebaly 2004). In Spain, an investigation of relapse following behavioural treatment for slot-machine gambling defined relapsers as those that had more than two isolated episodes of gambling in the 12 months follow-up or a total expense higher than a week of gambling before the treatment (Echeburua et al. 2001). These studies highlight the disparity in operationalizing relapse and the challenge to homogenizing a gambling related taxonomy at a transnational level. On the flip side of the relapse equation only a few studies have examined risk factors that may precipitate relapse and these are mostly limited to one or two predictor variables (Ledgerwood and Petry 2006). A broad range of precipitants to relapse in gambling have been proposed from psychological, psychobiological, social, and environmental domains (Ledgerwood and Petry 2006). The studies that have investigated specific factors include neurocognitive indicators of disinhibition and decision making (Goudriaan et al. 2007), affective states and stress reactivity (Daughters et al. 2005), satisfaction with treatment,

5 alcohol consumption at pre-treatment, neuroticism (Echeburua et al. 2001), and gambling related cognitions and urge (Hodgins and el-guebaly 2004; Oei and Gordon 2008). The findings are generally mixed and associations have been modest or null. In substance use disorders there is an established evidence-base to conceptualizing and preventing relapse (Brandon et al. 2007). A significant influence in this field has been Marlatt s cognitive-behavioural model of relapse (Marlatt and Gordon 1985). This model has underpinned the development of relapse prevention (RP) interventions in addiction treatment centers across the world including those for problem gambling (Brandon et al. 2007; Donovan and Witkiewitz 2012). Since inception, it has evolved from a linear configuration to a non-linear dynamic system where the complex interplay between multiple risk factors is integral to predicting a relapse event (Witkiewitz and Marlatt 2004). The risk factors are clustered as distal or stable factors and proximal or time-varying factors. Examples of distal factors in alcohol use disorder include comorbid conditions and severity of alcoholism. Proximal factors can involve psychological disturbance such as stress, rapidly shrinking social support, and craving (Donovan 1996). It has been proposed that distal factors may predict those individuals that are vulnerable to relapse whilst proximal factors may account for more temporal characteristics of a relapse event (Shiffman 1989). In problem gambling, data are lacking to clarify the association between potential predictors and relapse and if associations are consistent across time. A better understanding of distal and proximal factors associated with relapse in treatment seeking problem gamblers will help predict those vulnerable to relapse, improve relapse prevention strategies, and reduce the risk of relapse. In this study, we aimed to identify potential predictors of relapse using an expert consensus process and then based on these findings to address the research question: among a population of treatment and support seeking problem gamblers what are the demographic, behavioural, clinical and social characteristics that may consistently predict relapse in problem gambling across time Methods The study involved a two-staged process to identify predictors of relapse in problem gamblers. Firstly, a Delphi inquiry was used to obtain expert consensus on definitions, potential elements and predictors of relapse. This process is briefly described below with full details available at (Battersby et al. 2010). Secondly, these findings were used to empirically test potential predictors using a prospective cohort design and comprise the main focus of this paper. Delphi Due to a paucity of research literature into relapse in problem gambling we utilised a Delphi study to identify and quantitatively measure uncertainty on definitions, elements, and predictors of relapse. The Delphi is an iterative process that provides the opportunity for individual experts to change their opinions based on feedback of summary measures from preceding rounds (Mullen 2003). In this study the Delphi was conducted in four rounds. Round one involved a project advisory group comprising six expert clinicians (4 psychiatrists, 1 social worker and 1 psychologist) with at least 15 years clinical experience each in problem gambling and substance use disorders. Of these, four were widely published international researchers in problem gambling and related disorders. Tasks included

6 creating definitions of relapse in problem gambling and listing potential elements and predictors of relapse. A list of international experts in gambling disorders and related addictions was also generated by the advisory group to invite for participation in subsequent rounds. Round two comprised the entire list of elements and predictors and alternative definitions of relapse sent to potential participants for rating. There was a response rate of 46 % with twenty two participants returning completed questionnaires. In round three a revised questionnaire was developed to include median scores from the preceding round and sent to participants who were asked to once again rate each item in light of the new information. The principal investigator then convened a meeting of an expert panel of six international researchers who attended the Auckland 2008 International Think Tank on Gambling at the Auckland University of Technology to discuss findings from the Delphi iterations. Final predictors selected for the observational study were required to be measurable in terms of validated self-rating scales and parsimony where there was item overlap within domains of psychological, psychobiological, and social and environmental factors. Each measure is described in the following section. Cohort Setting and Participants The study took place in Adelaide, South Australia from March 2008 to March Adelaide has a population of 1.2 million and is the capital city of South Australia (total population 1.6 million) where problem gambling is mainly a result of the widespread availability of 12,688 live gaming machines in venues in nearly all towns and cities across the state (Government of South Australia: Consumer and Business Services 2012). Participants were adults who, at baseline, were either seeking or engaged in treatment or support for problem gambling. Recruitment was initiated by research staff contacting gambling help services in South Australia with information about the study. All services contacted agreed to participate. Participant numbers recruited by agency were influenced by factors that included service size and duration of agency engagement with the study. Participating services were the Statewide Gambling Therapy Service (SGTS) that provides individual cognitive behavioural therapy in outpatient settings; Pokies Anonymous (PA), a self-help peer support organisation based on similar principles to Alcoholics Anonymous; Gambling Helpline (GHL), a free 24-h counselling, information and referral service; Offenders Aid and Rehabilitation Service (OARS), a support organisation for people affected by problem gambling who have been, or are at risk of entering the criminal justice system in South Australia; and Relationships Australia South Australia (RASA) which provides specialised counselling services for problem gamblers. The study received approval from the Southern Adelaide Health Service/Flinders University Human Research Ethics Committee. Participants were given an information statement regarding the study and asked to provide written informed consent before data collection began. Design and Procedure A prospective cohort design was used to investigate predictors of relapse in problem gamblers, which followed participants over a 6 12 month time period. Participants were recruited in the period March to September Baseline measures were collected

7 following consent to participate in the study. Follow-up time points were one and 3 months for all participants. Depending on the date of recruitment participants either completed a 6 month measure as their final follow-up, or a 6 month and a final 12 month measure by study completion in March To improve follow-up response rates, self-report questionnaires were administered by mail to reduce burden of visits to the study site (National Research Council 2010). Mailouts included an unconditional gift voucher of $10 with 3, 6, and 12 month questionnaires, and a gift voucher of $20 on return of the final 6 or 12 month questionnaire (Edwards et al. 2002). A follow-up letter was sent to participants 1 week after mailing out of each questionnaire. The purpose of this letter was to thank those who had returned their questionnaires and act as a reminder for those who had not. At 3 weeks a further letter was sent out to participants with unreturned questionnaires. A final follow-up occurred at 5 weeks with a telephone call for unreturned questionnaires to offer the mailing out of a further set of questionnaires if required (Dillman 2007). Outcome Measures Outcome status was represented by three unordered items (remission, relapse, and continuing to problem gamble). All participants were measured to be either in remission (non-problem gambling), or continuing to problem gamble at baseline, the vast majority being in the continuing category as they were treatment and support seeking. To assess predictors of relapse, participants needed to achieve at least some period of remission during the study so subsequent relapse predictors could be observed and measured. At follow-up time points of 1, 3, 6, and 12 months, outcome status of participants were classified as either in remission, continuing to problem gamble, or relapsed if the individual had returned to problem gambling following a remission period. To compare alternative measures of outcome derived from the Delphi process, assessments were conducted at each point using two approaches; Victorian Gambling Screen (VGS) self-harm subscale and self-reported gambling behaviours as quantified by the Delphi definition of relapse. For the purpose of this study, selection of the VGS was based on the scales following properties: (i) items on the self-harm subscale relate to the person s experiences in the previous 4 weeks, and therefore an enhanced sensitivity to relapse and temporal associations, (ii) representation of all domains of elements in the final Delphi list (behavioural, cognitive, and interpersonal factors), and (iii) a validated cut score indicative of problem gambling. Of other validated instruments that classify problem or probable pathological gambling, neither the Canadian Problem Gambling Index (CPGI) (Ferris and Wynne 2001) or the South Oaks Gambling Screen (SOGS) (Lesieur and Blume 1987) satisfied both criteria (i) and (ii). The VGS is a self-reported questionnaire measuring the extent to which a gambler s behaviour has caused harm to themselves, their family or the community in the previous 4 weeks. The harm to self sub-scale has been validated for use in Australia in community and clinical populations (Tolchard and Battersby 2010) where a cut score of 21 or higher is indicative of problem gambling. Concurrent validity indicates the scale correlates very highly with the SOGS (r = 0.97), but extends the score range. The VGS has also shown similar properties in construct validity as the CPGI on a number of problem gambling correlates (e.g. self-rating of problem ; wanted help ; and suicidal tendencies ) (McMillen and Wenzel 2006).

8 For gambling behaviours participants self-reported their gambling activities during the previous 4 weeks and were classified according to their aims of abstinence or controlled gambling. Three questions pertaining to outcome status were asked: (i) Are you currently aiming to be abstinent from gambling? (Yes/No); When answering No the person was asked: (ii) To what level are you aiming to limit your gambling? (Once a week/twice a week/ Three or more times a week/daily). (iii) How often did you gamble during the last 4 weeks? (Never/Once/Twice/Once a week/twice a week/three or more times a week). Participants who answered Yes to question (i) and reported gambling activity in that time period were categorised as having either relapsed or continuing to problem gamble (depending on their previous assessment). Participants answering No to question (i) and reporting gambling activity in question (iii) that exceeded their aims in question (ii) were also categorised as either having relapsed or continuing to problem gamble. Otherwise participants were categorised as in remission. Predictors A range of self-report measures were selected a priori on the basis of Delphi findings for potential predictor variables and were administered at all study time points. To assess internal consistency of each measure completed by participants at baseline, Cronbach alpha (a) coefficients were calculated and are reported in the following section. The experience of depression, state anxiety and stress was measured using the 21 item self-report Depression Anxiety Stress Scale (DASS-21) that has been validated against other depression and anxiety inventories (a = 0.96) (Lovibond and Lovibond 1995). Levels of trait anxiety were recorded using the 20 item self-report Trait Anxiety Inventory (TAI) Y-20 self-report measure shown to have good reliability measured by test retest coefficients and sound validity (a = 0.92) (Spielberger et al. 1983). Gambling Urge Scale (GUS) is a 6 item self-report questionnaire measuring the extent of gambling urge and has been validated in community, university and clinical samples of gamblers (a = 0.93) (Raylu and Oei 2004b; Ashrafioun et al. 2013; Smith et al. 2012). Gambling Related Cognition Scale (GRCS) is a 23 item self-report questionnaire that records common thoughts associated with PG and a comparison with the South Oakes Gambling screen indicated the scale has good psycho-metric properties (a = 0.91) (Raylu and Oei 2004a). Alcohol Use Disorders Identification Test (AUDIT) is a non-diagnostic ten item questionnaire indicating hazardous alcohol and psycho-metric properties include superior specificity and sensitivity to those of other self-report screening measures and good test retest reliability and internal consistency (a = 0.91) (Reinert and Allen 2002). Arnett Inventory of Sensation Seeking (AISS) is a 20 item self-report questionnaire that measures sensation seeking personality traits and has been shown to be free from social desirability bias (a = 0.69) (Roth 2003). Multidimensional Scale of Perceived Social Support (MSPSS) is a 12 item self-report questionnaire containing three sub-scales (significant other, family and friends sub-scales) and is psychometrically sound, with good reliability, factorial validity, and adequate construct validity (a = 0.94) (Zimet et al. 1988). Work and Social Adjustment Scale is a self-report questionnaire used to measure a patient s perspective of their functional ability/impairment on a seven-point scale (a = 0.84) (Mundt et al. 2002).

9 At baseline participant s reported their gender, age, marital status, highest education level, employment status, and living arrangement. Self-reported data for duration of gambling problem and primary form of PG was also collected. Baseline Characteristics Demographic and clinical characteristics of the study cohort are presented in Table 1. When compared to previous normal population scores baseline DASS-21 means where higher for depression, anxiety, and stress scales, and in the moderate severity range (Lovibond and Lovibond 1995). Mean scores on the TAI were higher than previous normative data in working adults, college and high school students by at least one standard deviation (Spielberger et al. 1983). Stratifying the VGS self- harm subscale at cut score 21 found 94.9 % (n = 150) participants were classified as problem gamblers at study commencement. Statistical Methods Statistical analyses were conducted using Stata 10.0 (StataCorp. 2008). Baseline characteristics were compared between participants completing baseline measures only and participants with at least one follow-up measure using t tests for continuous variables, and v 2 tests of independence for categorical variables. A significance level of 5 % was used. For statistical modelling of predictors of relapse we used mixed-effects multinomial logistic regression to handle correlated observations from repeated measures within subjects, and the unordered or nominal response categories of remission, relapse, and continuing to problem gamble (Long and Freese 2006). Mixed-effects models do not assume equal time intervals for all participants, and therefore incomplete data is not excluded. Missing data was assumed to be missing at random (MAR) i.e. probability of missing was independent of the unobserved data and conditional on observed data. Models were fitted using the user-written program gllamm (generalised linear latent and mixed models) (Skrondal and Rabe-Hesketh 2003). Variable selection for regression models commenced with univariate analyses and were selected for model advancement based on p \.25 (Hosmer and Lemeshow 2000). Pairwise correlations among the significant variables were assessed for collinearity. An initial full model was created with variables significant at p \.25 and not collinear. Using backward manual elimination methods, variables with the least significant Wald statistic were removed from the model. A comparison of log likelihood values between the fitted model and the full model was conducted for each variable removed. The goal was to construct a model with the fewest number of variables without compromising a good fit of the data. To interpret effect sizes, odds ratios were calculated to represent the probability of experiencing one outcome category (relapse or continuing to gamble) over the probability of experiencing the reference category of remission. Results Participant Flow Overall, 352 treatment and support seeking problem gamblers were potentially eligible to participate in this study. Reasons for study exclusion were unstable mental state (n = 50) such as mania or suicidality following clinician assessment that indicated that the problem

10 Table 1 Demographic and clinical characteristics of 158 problem gamblers Data are presented as number (percentage) unless otherwise indicated. Percentages not always based on 158 participants owing to missing data GUS Gambling Urge Scale, WSAS Work and Social Adjustment Scale, DASS-21 Depression Anxiety and Stress Scale, MSPSS Multidimensional Scale of Perceived Social Support, VGS Victorian Gambling Screen, AUDIT Alcohol Use Disorders Identification Test, AISS Arnett Inventory of Sensation Seeking, GRCS Gambling Related Cognition Scale, TAI Trait Anxiety Inventory a Refers to scores collected at baseline Variable Value Age, mean ± SD, year 44 ± Male sex 85 (54) Marital status Married/defacto 63 (40) Single 55 (35) Separated 33 (21) Widowed 7 (4) Highest education level Primary school 2 (1) High school 88 (56) TAFE/trade qualification 43 (28) University degree 24 (15) Employment Full-time 58 (37) Part-time 28 (18) Not working 50 (31) Retired 17 (11) Student 5 (3) Living arrangement Alone 36 (24) Couple with dependent children 29 (19) Couple without dependent children 38 (25) Single parent 15 (10) Living with parents 24 (16) Sharing 11 (7) Primary form of gambling Gaming machines 138 (87) Duration of gambling problem \2 year 31 (20) 2 5 year 36 (23) [5 year 87 (57) Clinical measures, mean (SD) a GUS (11.52) WSAS (9.73) DASS-21: depression (6.35) DASS-21: anxiety 6.63 (5.65) DASS-21: stress (6.01) MSPSS (22.08) VGS: self-harm subscale (11.46) AUDIT 6.07 (7.46) AISS (7.65) GRCS (25.03) TAI (11.01)

11 gambler would not be able to participate fully in the study, confidentiality concerns (n = 15), limited English (n = 7), and no fixed postal address (n = 3). Reasons for nonparticipation included refusal (n = 68), inconsistent application of research protocol (n = 25), and no reason provided (n = 26). Of the 158 consented participants at baseline, 106 (67.1 %) were referred from SGTS, 31 (19.6 %) from GHL, 11 (7 %) from PA, 5 (3.2 %) from RA, and 5 (3.2 %) from OARS. Overall, median time for participants enrolment in the study was 8.38 months with 50 % of participants having times between 7 and 9.57 months (IQR = 2.57 months) and 25 % less than 7 months. Patterns of completed measures for points in time included 116 (73.4 %) with at least a 3 month followup, and 99 (62.7 %) with at least a 6 month follow-up. Overall, there were 34 baseline assessment completers only (21.5 %), with 124 participants (78.5 %) completing at least one follow-up measure. There were significantly more males with baseline scores only (28 males = 82.4 %) compared to females (6 females = 17.6 %), (v 1 2 = 14.21; p \.01). There were also significant differences on mean age between baseline completers only and those that had at least one follow-up assessment (38.5 ± vs ± 12.94, p \.01), AISS (49.37 ± 7.50 vs ± 7.56, p =.02), and TAI (50.62 ± vs ± 11.08, p =.05). There were no significant differences on remaining measures. Regression Models The observed sample sizes and response proportions by outcome category of remission, relapse, or continuing to problem gamble for outcome measures VGS and gambling behaviours are presented in Table 2. These observed proportions indicate an increase in remission and relapse rates across time, and a corresponding decrease in the proportion of participants continuing to gamble. Figure 1 shows change in proportions across time for VGS. Results of the univariate mixed-effects multinomial regression analysis describing each variable s association with gambling status are presented in Table 3. Results of the final regression models are shown in Table 4. For GUS scores the odds of participants experiencing a relapse over remission for each increase of one unit increased by 29 % while holding all other variables constant. The odds of participants continuing to Table 2 Gambling status as measured with the Victorian Gambling Screen (VGS) and gambling behaviours across time: response proportions and sample sizes (n) Outcome Time-point Baseline 1 month 3 months 6 months 12 months VGS Remission Relapsed Continuing n Gambling behaviours Remission Relapsed Continuing n

12 remission continuing relapsed time (months) Fig. 1 Proportion versus time for outcome status using the Victorian Gambling Screen gamble over remission increased by 20 %. For each one unit increase on GRCS the odds of participants experiencing a relapse over remission increased by 6 %. The odds of continuing to gamble over remission increased by 8 %. For each one unit increase on the Work and Social Adjustment Scale the odds of a participant continuing to gamble over remission increased by 13 %. Whilst AUDIT was significant in the overall regression model (p =.02) there was no significant effect in either the relapse or continuing to gamble categories when compared to the remission category. For outcome measure gambling behaviours i.e. gambling once or more than either planned abstinence or controlled gambling, each one unit increase on GUS the odds of participants experiencing a relapse over remission increased by 16 %. The odds of participants continuing to gamble over remission increased by 9 %. The odds of participants continuing to gamble over remission increased by 18 % with each one unit increase on the WSAS. Discussion The primary aim of this study was to model the probability of a study participant experiencing a relapse in problem gambling following a period of remission as a function of potential predicting variables. Outcome events were determined by two independent methods for comparative purposes: scores on the VGS with a cut score of 21 and changes in gambling behaviours. The candidate predictor variables covered a range of proximal and distal factors including general mental health status, levels of social support and functionality, sensation seeking traits, gambling specific measures of urge and cognitions, and socio-demographic variables. When the outcome status of relapse, remission, or continuing to gamble at each time point was determined with scores on the VGS we found that gambling urge and gambling related cognitions were significant predictors of relapse. Predictive factors of continuing to gamble over remission were gambling urge, gambling related cognitions, and work and social functionality. For outcome gambling behaviour, urge was a significant predictor of both relapse and continuing to gamble. Work and social functionality was also a significant predictor of continuing to gamble.

13 Table 3 Univariate mixed-effects multinomial logistic regression analyses describing each variable s association with outcome patterns of response Variables Victorian Gambling Screen Gambling behaviours Wald p value Advanced to final model? a Wald p value Advanced to final model? a Time \.001 Yes \.001 Yes Demographic Age, years Yes \.001 Yes Gender Yes Yes Marital status Highest education level Yes Employment Living arrangement Yes Duration of gambling problem, years Measures GUS \.001 Yes \.001 Yes WSAS \.001 Yes \.001 Yes DASS \.001 Yes \.001 Yes MSPSS Yes Yes AUDIT Yes AISS Yes Yes GRCS \.001 Yes \.001 Yes TAI b \ \.001 GUS Gambling Urge Scale, WSAS Work and Social Adjustment Scale, DASS-21 Depression Anxiety and Stress Scale, MSPSS Multidimensional Scale of Perceived Social Support, AUDIT Alcohol Use Disorders Identification Test, AISS Arnett Inventory of Sensation Seeking, GRCS Gambling Related Cognition Scale, TAI Trait Anxiety Inventory a Advanced to final model if p \.25 b Collinearity with DASS-21, therefore did not advance to final model Similar to craving in substance use disorders, the urge to gamble has been designated a conspicuous symptom in gambling disorders based on brain imaging studies and is an antecedent to relapse (van Holst et al. 2010). Cognitions such as inability to stop gambling have also been linked to relapse (Raylu and Oei 2002)) and a qualitative investigation has identified both gambling related urges and erroneous cognitions as precipitants to relapse (Oakes et al. 2011). Similarly, a prospective study of gambling relapse found that cognitions relating to winning and giving into urges were associated with relapse (Hodgins and el-guebaly 2004). These findings indicate that relapse prevention should include techniques targeting urge and cognitions with similar priority to producing initial behaviour change (Brandon et al. 2007). Problem gamblers may experience similar trajectories to relapse as in substance use disorders where proximal and distal risk factors interact (Katie Witkiewitz and Marlatt 2007). For example, an individual with a baseline co-morbid disorder of depression (distal risk) may be more vulnerable to return to previous gambling behaviours through changes in proximal effects of gambling related cognitions such as gambling makes things seem better (Raylu and Oei 2004a). Similarly, the urge to gamble may be translated by the

14 Table 4 Mixed-effects multinomial logistic regression model (variables significant overall at p \.05) describing the association between variables and patterns of response measured with the Victorian Gambling Screen and gambling behaviours Variable Victorian Gambling Screen OR (95 % CI) a Gambling behaviours OR (95 % CI) a Relapse versus remission Continuing versus remission Relapse versus remission Continuing versus remission Time 1.23( )* 0.76( )* 1.26( )* 0.78( )* Gender 0.40( ) 0.37( ) Living 1.98( ) 0.49( ) arrangement GUS 1.29( )* 1.20( )* 1.16( )* 1.09( )* WSAS 0.93( ) 1.13( )* 0.94( ) 1.18( )* DASS ( ) 0.98( ) MSPSS 1.00( ) 1.02( ) AUDIT 1.09( ) 0.96( ) AISS 1.01( ) 1.05( ) GRCS 1.06( )* 1.08( )* 1.02( ) 1.02( ) GUS Gambling Urge Scale, WSAS Work and Social Adjustment Scale, DASS-21 Depression Anxiety and Stress Scale, MSPSS Multidimensional Scale of Perceived Social Support, AUDIT Alcohol Use Disorders Identification Test, AISS Arnett Inventory of Sensation Seeking, GRCS Gambling Related Cognition Scale * 95 % confidence interval significant at p \.05 a Odds ratio = e b expectation of a more tolerable mood state and result in a relapse (Anton 1999; Raylu and Oei 2004b). In this study we did not identify any distal factors which may have been a result of the use of more global self-report measures such as DASS-21. Further studies may consider more formal assessments to identify comorbid conditions such as clinical depression and the interactions with more immediate precipitants such as gambling related thoughts. We accounted for the temporal associations between gambling behaviour and predictors by using a repeated measure design or equivalently, a cross-sectional time series. For example, it was found that gambling urge was a significant predictor of both relapse and maintenance of problem gambling across time on two possible outcome measures. Modelling of this trend focused on the individual deviation from the overall population trend using an iterative method based on all available data. This provided a more precise estimate of the probability of relapse from differing amounts of information prior to and following behaviour at the individual level. This approach has been used previously in a longitudinal study of relapse in cigarette smoking (Hedeker and Mermelstein 1996). Other strategies to predicting relapse include non-linear methods such as cusp catastrophe modelling as previously used in alcohol use disorders (Katie Witkiewitz and Marlatt 2007). To increase sensitivity to minor changes in risk the catastrophe model comprises of two possible values of control parameters (proximal and distal risks) that may result in more than one value of the behaviour parameter (relapse). This approach has shown to provide an overall better fit of the data when compared to linear models of drinking behaviour. A drawback of this study was that cases having missing data were excluded from analyses and model estimates were based on a complete case analysis. Alternative

15 methods such as mixed-effects models or multiple imputation may have enhanced external validity of findings by including all available data. Our study has several limitations. Approximately 67 % of participant s were recruited from a single treatment service, limiting the generalisability of results to the wider population of problem gamblers. However, strengths of this homogenous sample included minimisation of confounding that may result from a number of treatment types in a cohort study. These findings would be useful for developing a model of relapse with treatment specificity to problem gambling. For other addictions it has been suggested that different treatments may be associated with different relapse processes that requires further research (Brandon et al. 2007). Approximately 22 % of participants did not complete a follow-up assessment beyond baseline who were generally younger (p \.01), mostly male (p \.01), and had higher AISS scores for sensation seeking traits (p =.02). Therefore, conclusions from this study are drawn from a partially observed dataset. Although this loss of power could not be reversed we used maximum likelihood estimation for all available data to help minimise the impact of missing data. Our strategy for improving response rates included mailing out of questionnaires to limit participant burden in attending appointments and offering unconditional incentives. The plausibility of data missing at random (MAR), a necessary assumption of mixed-models, may have been value-added in this study by collecting additional secondary information on the ease of obtaining data. Future studies on gambling relapse may consider this as part of a handling missing data strategy. (White et al. 2011). Finally, there are other potentially important risk factors that were not investigated in the current study. For example, self-efficacy where a person s diminished confidence in their perceived ability to execute control of gambling behaviours may mediate the effects of an urge to gamble on relapse (Sharpe 2002). In terms of a multifaceted approach, low self-efficacy may also interact with distal traits such as impulsivity or the related construct of sensation seeking traits. We examined sensation seeking traits as an independent predictor whereas further research could explore alternative causal pathways for coping strategies (Ledgerwood and Petry 2006). Conclusion This study identified predictive factors of relapse in problem gambling from a broad range of candidate variables. Utilising alternative outcome measurements provided the opportunity to compare explanatory models and consider relapse processes beyond a singular definition of relapse. A more dynamic approach to modelling of relapse will enable better matching of aetiological factors to gambling interventions and reduce risk of relapse. This study also highlighted the challenges presented from a cohort study of treatment seeking problem gamblers. Acknowledgments We would like to acknowledge the assistance of Statewide Gambling Therapy Service, Gambling Helpline, Relationships Australia South Australia, Pokies Anonymous, and Offenders Aid and Rehabilitation Service in recruitment and ongoing support for the follow-up of participants. This project was funded by Gambling Research Australia (Grant No. 084/06). Conflict of interest The authors have no other conflicts of interest to declare.

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