Abstract
Background and aims
Gambling disorder (GD) is a behavioral addiction often co-occurring with various mental health concerns, such as problematic pornography use (PPU). The specific impact of the co-occurrence of GD and PPU on treatment outcome remains underexplored. This study aimed to compare the treatment outcomes of individuals actively receiving treatment for GD (n = 172; 3.49% females), distinguishing between those without PPU (n = 146) and those with co-occurring GD and PPU (n = 26).
Methods
Cognitive-behavioral therapy (CBT) was administered in 16 weekly sessions, with assessments of GD severity, impulsivity, emotion regulation, psychopathology, and personality. Dropout, relapses, number of sessions attended, number of relapses, and amount of money spent during relapses were assessed as the main treatment outcomes.
Results
Patients with co-occurring GD and PPU showed greater GD severity, psychopathology, impulsivity, and difficulties in emotional regulation compared to those with GD and without PPU. Moreover, the presence of PPU appeared to be mainly associated with higher likelihood of treatment dropout, and, consequently, fewer CBT sessions attended.
Discussion and Conclusions
It is important to evaluate GD/PPU co-occurrence and strengthen the CBT approach for GD patients with PPU by using supplementary strategies to improve treatment adherence.
Introduction
Gambling disorder (GD) is classified as a behavioral addiction in the fifth edition of the Diagnostic and Statistical Manual (DSM-5; APA, 2013). GD is defined as a repetitive pattern of wagering that leads to considerable distress or functional impairment (APA, 2013). Understanding GD involves examining various contributing factors, including its co-occurrence with other mental health conditions. For example, GD has been reported to frequently co-occur with substance use, impulse control, mood, anxiety, and personality disorders (Di Nicola et al., 2014; Grant & Chamberlain, 2020; Moore & Grubbs, 2021; Potenza et al., 2019; Szerman et al., 2023; Theule, Hurl, Cheung, Ward, & Henrikson, 2019). Additionally, other disorders that have been suggested to be associated with GD include eating disorders, schizophrenia, and post-traumatic stress disorders (Di Nicola et al., 2014; Granero et al., 2021; Lemón, Fernández-Aranda, Jiménez-Murcia, & Håkansson, 2021; Medeiros & Grant, 2018; Moore & Grubbs, 2021; Theule et al., 2019).
Despite similarities between GD and other formally defined and potential behavioral addictions (Banz, Yip, Yau, & Potenza, 2016; Choi et al., 2014; Sussman, Rozgonjuk, & van den Eijnden, 2017), the co-occurrence of GD with these conditions has been understudied. Few studies have explored the co-occurrence of GD with internet gaming disorder (André, Håkansson, & Claesdotter-Knutsson, 2022; Sanders & Williams, 2019), compulsive buying/shopping disorder (CBSD; Granero et al., 2016; Guerrero-Vaca et al., 2019), food addiction (Jiménez-Murcia, Granero, et al., 2017), compulsive sexual behavior disorder (CSBD; Cowie et al., 2019; Grant & Steinberg, 2005a; Tang, Kim, Hodgins, McGrath, & Tavares, 2020), and problematic pornography use (PPU; Mestre-Bach et al., 2023, 2024). Co-occurrence rates vary across studies. For instance, the co-occurrence between GD and CSBD has been reported to range from 6.5% (Cowie et al., 2019) to 19.6% (Grant & Steinberg, 2005b). GD and other potential behavioral addictions appear to be linked, in general, to a more maladaptive clinical profile. For example, the co-occurrence has been associated with greater psychopathology (Cowie et al., 2019; Granero et al., 2016; Jiménez-Murcia, Granero, et al., 2017; Tang et al., 2020), including other addictive behaviors (Cowie et al., 2019; Granero et al., 2016), higher motor impulsivity (Cowie et al., 2019), and a personality profile characterized by high harm avoidance and low cooperativeness (Granero et al., 2016; Jiménez-Murcia, Granero, et al., 2017). Such co-occurrence may be influenced by factors such as age and gender, with potential associations between co-occurrence and being male and younger (Cowie et al., 2019; Guerrero-Vaca et al., 2019; Tang et al., 2020). Moreover, the severity of one behavior may exacerbate the severity of the co-occurring behavior (André et al., 2022). However, there is no clear evidence that overinvolvement in one behavior is a strong predictor of over involvement in the other (Sanders & Williams, 2019).
In the specific case of GD and PPU, both have been suggested as behavioral addictions and appear to be characterized by patterns of persistent and repetitive engagement in the behaviors (gambling and pornography use) that lead to negative consequences, along with unsuccessful attempts to reduce or cease these behaviors (APA, 2013; Bőthe, Tóth-Király, Bella, et al., 2021; Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021). Therefore, both conditions may share underlying mechanisms (Brand, Young, Laier, Wölfling, & Potenza, 2016). However, the co-occurrence between GD and PPU remains relatively unexplored. The co-occurrence of GD and PPU seems to be associated with increased psychopathology, greater GD severity, impulsivity, difficulties in emotion regulation, and a personality profile characterized by low cooperativeness and self-directedness (Mestre-Bach et al., 2023). When analyzing potentially predictive factors, high impulsivity may predict this co-occurrence, whereas psychopathology may not have a direct association (Mestre-Bach et al., 2024), although more evidence is needed.
To the best of our knowledge, no studies have yet investigated how the co-occurrence of GD and PPU may impact treatment response. Although there remains controversy regarding how best to define recovery and treatment outcomes (e.g. relapse and dropout) (Mansueto, Challet-Bouju, Hardouin, & Grall-Bronnec, 2024; Mestre-Bach & Potenza, 2023; Pickering, Keen, Entwistle, & Blaszczynski, 2018), it is important to consider factors that may hinder treatment adherence and recovery for individuals with behavioral addictions. Previous research has indicated that factors such as emotion dysregulation (Vintró-Alcaraz et al., 2022) and impulsivity (Jara-Rizzo et al., 2019; Mallorquí-Bagué et al., 2018; Mena-Moreno et al., 2022) may be associated with poorer treatment outcomes. Further investigation is warranted to determine whether the specific co-occurrence of GD and PPU may affect treatment outcomes.
In this line, this study aimed to analyze the treatment outcome of 172 adults diagnosed with GD. Specifically, it sought to compare treatment responses (relating to dropout, relapse, number of sessions attended, number of relapses, and amount of money spent during relapses) between a group of individuals with GD without PPU and a group with co-occurring GD and PPU. In prior studies, we have investigated how the co-occurrence of GD+PPU relates to psychopathology and temperamental features (Mestre-Bach et al., 2023, 2024). Here, we build upon this prior research by exploring relationships with treatment outcomes in the sample. It was hypothesized that the co-occurrence of these conditions may be associated with a more maladaptive clinical profile (e.g., higher levels of psychopathology, impulsivity and emotion dysregulation) and poorer treatment outcome (mainly more frequent dropout and relapse).
Methods
Participants and procedure
The study sample included 172 consecutive adults who sought outpatient treatment for GD at a public hospital located in Barcelona (Spain) between January 25th, 2021, and April 18th, 2023. This sample was included in two previous studies (Mestre-Bach et al., 2023, 2024).
Only individuals whose primary mental health concern was GD were eligible for inclusion. None of the participants attended the hospital with the primary goal of seeking treatment for PPU. The sample was categorized into two groups regarding the presence or absence of PPU: the GD group (n = 146) and the GD+PPU group (n = 26). Patients who scored 20 or higher on the Problematic Pornography Consumption Scale (PPCS-6; Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021), with the cut-off established during the original validation, were classified in the GD+PPU group. The inclusion criteria required participants to be over 18 years old, of any gender, and seeking treatment for GD as their primary concern. Exclusion criteria included a history of brain injury or neurological disorders, as well as the presence of organic medical illnesses or neurodegenerative conditions. All participants were assessed through a face-to-face clinical interview conducted by expert clinical psychologists and psychiatrists with more than 15 years of clinical experience, who determined a diagnosis of GD based on DSM-5 criteria. In addition, the DSM-5 criteria were administered to patients in a question-based format as part of the assessment battery.
Within the total sample, most patients were men (96.5%), single (53.5%), had low (primary or less) educational attainment (53.5%) and were employed. The mean chronological age was 39.3 years (SD = 13.7).
Cognitive behavioral therapy
The cognitive-behavioral therapy (CBT) group program in this study involved 16 weekly outpatient sessions, each lasting 90 min, conducted at a public University Hospital. The sessions were facilitated by an experienced clinical psychologist and a licensed co-therapist. To maintain treatment fidelity, providers underwent specific training to adhere to the manualized protocol (S Jiménez-Murcia, Aymamí-Sanromà, Gómez-Peña, Álvarez-Moya, & Vallejo, 2006). The primary goal of the program was to teach patients how to apply CBT techniques to reduce all forms of gambling, with the manualized protocol aiming for complete abstinence as the standard treatment goal for all participants. Key topics covered included psychoeducation regarding the disorder (its course, risk factors, diagnostic criteria), stimulus control (money management, avoiding triggers, self-exclusion), response prevention through alternative behaviors, cognitive restructuring to address illusions of control and magical thinking related to gambling, emotion-regulation skills, and relapse-prevention strategies. A concerned significant other of each patient participated in the treatment, attending 7 out of the 16 sessions. This program has been previously described (Jiménez-Murcia et al., 2006), and its short- and medium-term effectiveness has been documented in other studies (Jiménez-Murcia et al., 2007; Jimenez-Murcia et al., 2012). Throughout the treatment, weekly records were kept of session attendance, spending behaviors, and the occurrence of relapses, which were defined as any gambling episode after treatment began, a common measure in GD studies (Jiménez-Murcia, Tremblay, et al., 2017; Müller et al., 2017). Missing three consecutive sessions was considered dropout.
Measures
GD severity
DSM-5 GD Criteria (APA, 2013)
Patients were diagnosed with gambling disorder (GD) if they met at least four criteria within the past 12 months, as outlined by the DSM-5 (APA, 2013). In this study sample, the internal consistency was α = 0.792.
South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987)
This self-report screening tool, comprising 20 items, was designed to distinguish between individuals with likely GD, problem gambling, and non-problem gambling. The Spanish-validated version used in this study showed high internal consistency (α = 0.94) and excellent test-retest reliability (r = 0.98) (Echeburúa, Báez, Fernández-Montalvo, & Páez, 1994). In the present sample, the internal consistency was α = 0.706.
PPU
PPCS-6 (Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021)
The PPCS-6 measures problematic pornography use (PPU) through six Likert-type items, each offering seven response options (ranging from 1 = never to 7 = all the time). This scale has demonstrated robust psychometric properties, including a solid factor structure, measurement invariance, and high reliability, both in the original version (Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021) and in the Spanish validation for clinical populations with behavioral addictions (Jiménez-Murcia et al., 2023). In the original validation, a score of 20 (out of 42) was identified as the optimal cutoff for diagnosing PPU (Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021). The internal consistency for the study sample was α = 0.877.
Psychopathology
Symptom Checklist-Revised (SCL-90-R; Derogatis, 1990)
The SCL-90-R is a psychometric instrument composed of 90 items that evaluate psychological distress and various psychopathological symptoms. It covers nine distinct symptom dimensions: somatization, obsessive-compulsive behaviors, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. Additionally, the overall distress level is represented by the Global Severity Index (GSI), which reflects general psychopathological distress. For this study, the Spanish version of the questionnaire was utilized (Derogatis, 2002), and only the GSI was included. In the study sample, the internal consistency for the GSI was α = 0.981.
Impulsivity
Impulsive Behavior Scale (UPPS-P; Whiteside, Lynam, Miller, & Reynolds, 2001)
The UPPS-P is a self-administered questionnaire designed to evaluate five dimensions of impulsive behavior through 59 items: negative urgency, positive urgency, lack of premeditation, lack of perseverance, and sensation-seeking. Participants are asked to rate their behavior and attitudes over the past six months. The Spanish version used in this study demonstrated good reliability, with Cronbach's alpha ranging from 0.79 to 0.93, and strong external validity (Verdejo-García, Lozano, Moya, Alcázar, & Pérez-García, 2010). In our sample, internal consistency was α = 0.911, with α = 0.836 for negative urgency, α = 0.928 for positive urgency, α = 0.832 for lack of premeditation, α = 0.772 for lack of perseverance, and α = 0.818 for sensation-seeking.
Emotion regulation
Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004)
This self-administered tool consists of 36 items divided into six subscales aimed at evaluating emotional dysregulation. These subscales are: (1) lack of emotional awareness, which assesses difficulties in recognizing emotional states; (2) lack of emotional clarity, measuring issues in identifying emotional experiences; (3) non-acceptance of emotional responses, indicating a tendency to experience negative secondary emotions; (4) difficulties in goal-directed behavior, highlighting struggles in completing tasks when emotions are intense; (5) limited access to emotion regulation strategies, reflecting the perception of few options for managing emotions during distress; and (6) impulse control difficulties, which assess challenges in controlling behavior under negative emotional states. The Spanish adaptation of the questionnaire (Hervás & Jódar, 2008) was used. In our sample, internal consistency ranged from 0.771 to 0.922.
Personality
Temperament and Character Inventory-Revised (TCI-R; Cloninger, 1999)
The TCI-R is a validated questionnaire consisting of 240 items that evaluate personality features. Participants respond using a 5-point Likert scale. The questionnaire measures seven key dimensions: four temperament features (novelty-seeking, harm avoidance, reward dependence, and persistence) and three character dimensions (self-directedness, cooperativeness, and self-transcendence). In this study, we employed the revised Spanish version of the TCI-R, which has shown strong internal consistency, with an average Cronbach's alpha of 0.87 (Gutiérrez-Zotes et al., 2004). For our sample, the internal consistency scores were α = 0.736 for reward dependence, α = 0.796 for novelty-seeking, α = 0.806 for harm avoidance, α = 0.811 for cooperativeness, α = 0.870 for self-transcendence, α = 0.858 for self-directedness, and α = 0.895 for persistence.
Other sociodemographic and clinical variables
A semi-structured face-to-face clinical interview (Jiménez-Murcia et al., 2006) assessed additional demographic (e.g. sex, age, education, employment), social/familial (e.g. civil status, social status), and clinical variables (e.g. age of GD onset, GD duration, preference of gambling type (non-strategic, strategic and mixed) and modality (land-based, online and mixed). The variables related to treatment outcome included likelihood of dropout, likelihood of relapse, number of sessions attended, number of relapses and amount of money spent during relapses.
Statistical analysis
Statistical data analysis was performed with Stata18 for Windows. In the first step, the comparison between the groups (for the features assessed at baseline and the treatment outcomes) was done with chi-square tests (χ2) for categorical variables and t-tests for continuous variables. For these procedures, the estimation of the effect size of the relationships was calculated with the standardized coefficients Cramer's-V (C-V) for the χ2 tests and Cohen's-d for the t-tests. For these coefficients, the thresholds 0.15 and 1.30 (for the C-V index) and 0.50 and 0.80 (for the |d| index) were interpreted as mild-moderate and large-high effect sizes (Kelley & Preacher, 2012). In addition, type-I error due the use of multiple significance statistical tests was controlled with the Finner's method (Finner, 1993), a family-wise error procedure.
The Kaplan-Meier product-limit estimator was used to obtain the cumulative survival functions for dropout and relapse. The survival function was used to measure the probability of patients “living” (surviving) without the presence of the treatment outcomes (dropout and relapse), during a certain amount of time after the onset of the intervention (Aalen, Borgan, & Gjessing, 2008). The comparison between both groups for the curves was done with the Log Rang (Mantel-Cox text) (Singer & Willett, 2003). In this study, because of the low sample size for some groups and exploratory nature of the study, results were considered relevant when statistically significant at p < 0.05 or when effect sizes were within a mild-moderate to large-high range.
Ethics
The study procedures were conducted in accordance with the Declaration of Helsinki. The Institutional Review Board of the Bellvitge University Hospital approved the study (ref. 19/21, November 4th, 2021). All subjects were informed about the study, and all provided informed consent.
Results
Psychopathological characteristics
Table 1 shows the descriptive for the whole sample at baseline, and the comparisons between both groups. Patients who reported co-occurring PPU demonstrated higher GD severity (measured using the SOGS and the DSM-5 criteria), worse psychological state (higher mean in the SCL-90-R GSI), higher impulsivity levels (assessed using the UPPS-P), more emotion dysregulation (evaluated using the DERS), and lower levels in the personality domains self-directedness and cooperativeness (assessed using the TCI-R).
Comparison between the GD groups with and without PPU at baseline
| Sociodemographic variables | Total (n = 172) | GD (n = 146) | GD+PPU (n = 26) | p | C-V | ||||
| n | % | n | % | n | % | ||||
| Sex | Female | 6 | 3.49% | 5 | 3.42% | 1 | 3.85% | 0.914 | 0.008 |
| Male | 166 | 96.51% | 141 | 96.58% | 25 | 96.15% | |||
| Civil status | Single | 92 | 53.49% | 73 | 50.00% | 19 | 73.08% | 0.094 | 0.166† |
| Married – couple | 67 | 38.95% | 61 | 41.78% | 6 | 23.08% | |||
| Divorced – separated | 13 | 7.56% | 12 | 8.22% | 1 | 3.85% | |||
| Education | Primary | 92 | 53.49% | 79 | 54.11% | 13 | 50.00% | 0.719 | 0.062 |
| Secondary | 69 | 40.12% | 57 | 39.04% | 12 | 46.15% | |||
| University | 11 | 6.40% | 10 | 6.85% | 1 | 3.85% | |||
| Employment | Unemployed | 59 | 34.30% | 51 | 34.93% | 8 | 30.77% | 0.680 | 0.031 |
| Employed | 113 | 65.70% | 95 | 65.07% | 18 | 69.23% | |||
| Social | High | 5 | 2.91% | 5 | 3.42% | 0 | 0.00% | 0.565 | 0.131 |
| Mean-high | 19 | 11.05% | 15 | 10.27% | 4 | 15.38% | |||
| Mean | 17 | 9.88% | 13 | 8.90% | 4 | 15.38% | |||
| Mean-low | 42 | 24.42% | 35 | 23.97% | 7 | 26.92% | |||
| Low | 89 | 51.74% | 78 | 53.42% | 11 | 42.31% | |||
| Gambling activity | n | % | n | % | n | % | p | C-V | |
| Preference** | Non-strategic | 71 | 41.28% | 62 | 42.47% | 9 | 34.62% | 0.745 | 0.058 |
| Strategic | 67 | 38.95% | 56 | 38.36% | 11 | 42.31% | |||
| Mixed | 34 | 19.77% | 28 | 19.18% | 6 | 23.08% | |||
| Modality | Land based | 84 | 48.84% | 73 | 50.00% | 11 | 42.31% | 0.767 | 0.056 |
| Online | 46 | 26.74% | 38 | 26.03% | 8 | 30.77% | |||
| Mixed | 42 | 24.42% | 35 | 23.97% | 7 | 26.92% | |||
| Ages and gambling severity | Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| Age (years-old) | 18 | 74 | 39.32 | 13.69 | 19 | 74 | 39.66 | 13.83 | 18 | 62 | 37.42 | 12.94 | 0.445 | 0.17 |
| GD onset (years-old) | 14 | 62 | 27.71 | 11.09 | 14 | 62 | 27.84 | 11.35 | 15 | 51 | 26.96 | 9.67 | 0.710 | 0.08 |
| GD duration (years) | 1 | 28 | 5.74 | 6.24 | 1 | 28 | 5.57 | 5.96 | 1 | 25 | 6.69 | 7.73 | 0.399 | 0.16 |
| DSM-5: total criteria for GD | 1 | 9 | 7.42 | 1.71 | 1 | 9 | 7.32 | 1.76 | 5 | 9 | 8.04 | 1.31 | 0.047* | 0.47 |
| SOGS: total | 3 | 18 | 11.18 | 3.40 | 3 | 18 | 10.85 | 3.36 | 7 | 17 | 13.04 | 3.03 | 0.002* | 0.68† |
| PPCS: total score | 6 | 39 | 12.59 | 7.25 | 6 | 19 | 10.11 | 3.91 | 21 | 39 | 26.54 | 5.73 | 0.001* | 3.35† |
| Psychology profile | Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| SCL-90-R GSI | 0 | 3 | 1.17 | 0.73 | 0 | 3 | 1.08 | 0.72 | 1 | 3 | 1.69 | 0.61 | 0.001* | 0.91† |
| UPPS-P: Lack premeditation | 12 | 42 | 25.09 | 5.98 | 12 | 42 | 24.97 | 6.01 | 15 | 35 | 25.77 | 5.89 | 0.533 | 0.13 |
| UPPS-P: Lack perseverance | 11 | 36 | 22.58 | 5.26 | 11 | 36 | 22.13 | 5.09 | 13 | 36 | 25.12 | 5.58 | 0.007* | 0.56† |
| UPPS-P: Sensation seeking | 12 | 47 | 29.05 | 8.06 | 12 | 46 | 28.20 | 7.82 | 17 | 47 | 33.81 | 7.90 | 0.001* | 0.71† |
| UPPS-P: Positive urgency | 14 | 56 | 32.70 | 10.31 | 14 | 56 | 32.18 | 10.34 | 17 | 54 | 35.65 | 9.81 | 0.113 | 0.34 |
| UPPS-P: Negative urgency | 13 | 47 | 32.37 | 7.08 | 13 | 47 | 32.05 | 7.22 | 17 | 44 | 34.12 | 6.05 | 0.172 | 0.31 |
| UPPS-P: total | 84 | 205 | 141.81 | 23.37 | 84 | 192 | 139.55 | 22.97 | 114 | 205 | 154.50 | 21.91 | 0.002* | 0.67† |
| DERS: total | 45 | 149 | 97.74 | 21.67 | 45 | 149 | 96.02 | 21.34 | 66 | 144 | 107.42 | 21.39 | 0.013* | 0.53† |
| Personality profile | Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | P | |d| |
| TCI-R: novelty seeking | 77 | 146 | 110.66 | 12.58 | 77 | 146 | 110.26 | 12.90 | 96 | 141 | 112.88 | 10.57 | 0.329 | 0.22 |
| TCI-R: harm avoidance | 63 | 159 | 99.88 | 16.24 | 63 | 159 | 99.50 | 16.53 | 72 | 126 | 102.04 | 14.59 | 0.464 | 0.16 |
| TCI-R: reward dependence | 55 | 131 | 94.08 | 13.38 | 60 | 131 | 94.60 | 13.29 | 55 | 116 | 91.15 | 13.76 | 0.228 | 0.25 |
| TCI-R: persistence | 63 | 168 | 111.20 | 19.16 | 63 | 154 | 110.77 | 18.55 | 69 | 168 | 113.58 | 22.56 | 0.494 | 0.14 |
| TCI-R: self-directedness | 69 | 170 | 124.98 | 19.33 | 69 | 170 | 126.51 | 19.58 | 89 | 143 | 116.38 | 15.55 | 0.013* | 0.57† |
| TCI-R: cooperativeness | 91 | 168 | 128.44 | 15.61 | 95 | 168 | 130.09 | 14.81 | 91 | 146 | 119.19 | 16.98 | 0.001* | 0.68† |
| TCI-R: self-transcendence | 32 | 114 | 64.30 | 16.38 | 32 | 114 | 63.36 | 16.56 | 38 | 96 | 69.54 | 14.50 | 0.076 | 0.40 |
Note. C-V: Cramer's-V coefficient; DERS: Difficulties in Emotion Regulation Scale; DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5th Edition; GD: gambling disorder; PPCS: Problematic Pornography Consumption Scale; PPU: problematic pornography use; SCL-90-R: Symptom Checklist-Revised; SD: standard deviation; SOGS: South Oaks Gambling Screen; TCI-R: Temperament and Character Inventory-Revised; UPPS-P: Impulsive Behavior Scale; |d|: Cohen's-d coefficient (absolute value).
*Bold: significant statistical comparison. †Bold: effect size within the ranges mild-moderate to large-high.
**Preference: non-strategic (including bingo, lottery and electronic gambling (slot) machines, among others), and strategic (including poker, blackjack and sports betting, among others).
Treatment outcomes
The overall likelihood of dropout was 33.9% (29.0% among GD and 61.5% among GD+PPU participants), and the overall likelihood of relapse during the treatment was 25.0% (22.6% among GD and 38.5% among GD+PPU participants) (see Table 2). The group with co-occurring PPU showed statistically significant differences with the group without PPU regarding greater likelihood of dropout and lower number of sessions attended during the intervention. The group with PPU also showed greater likelihood of relapse (at a level approaching but not reaching statistical significance), more relapses, and more money wagered during relapses.
Comparison between treatment outcomes for the GD groups with and without PPU
| Total (n = 172) | GD (n = 146) | GD+PPU (n = 26) | p | C-V | ||||
| n | % | n | % | n | % | |||
| Likelihood of dropout | 58 | 33.92% | 42 | 28.97% | 16 | 61.54% | 0.001* | 0.247† |
| Likelihood of relapse | 43 | 25.00% | 33 | 22.60% | 10 | 38.46% | 0.085 | 0.131 |
| Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | |
| Number of sessions attended | 1 | 16 | 11.58 | 6.00 | 1 | 16 | 12.31 | 5.49 | 1 | 16 | 7.46 | 7.16 | 0.010* | 0.76† |
| Number of relapses | 0 | 13 | 0.55 | 1.43 | 0 | 6 | 0.46 | 1.08 | 0 | 13 | 1.08 | 2.62 | 0.040* | 0.31 |
| Money bet during relapses (euros, total) | 0 | 6,400 | 159.76 | 605.21 | 0 | 3,000 | 122.95 | 384.72 | 0 | 6,400 | 366.46 | 1262.78 | 0.001* | 0.26 |
Note. C-V: Cramer's-V coefficient; GD: gambling disorder; PPU: problematic pornography use; SD: standard deviation; |d|: Cohen's-d coefficient (absolute value).
*Bold: significant statistical comparison. †Bold: effect sizes within the ranges of mild-moderate to large-high.
Table 3 displays the frequency distribution of the variables for individuals with dropout and treatment completion, stratified by the diagnostic subtype. Within the GD patients, the likelihood of dropout was higher for single patients, those with higher social position indexes, and those of younger age. Within the GD+PPU patients, the likelihood of dropout was higher for women, single individuals, and those with higher education levels, employment, higher social position levels, higher GD severity levels (as measured with the SOGS), non-strategic gambling preference, engagement in land-based gambling, higher impulsivity levels (lack premeditation and lack of perseverance), and lower self-transcendence levels. These results should be interpreted with caution, due to the low sample size for some groups (specifically, among those with GD+PPU).
Variables associated with dropout within each diagnostic subtype
| Sociodemographic | GD (n = 146) | p | C-V | GD+PPU (n = 26) | p | C-V | |||||||
| Dropout = No (n = 104) | Dropout = Yes (n = 42) | Dropout = No (n = 10) | Dropout = Yes (n = 16) | ||||||||||
| n | % | n | % | n | % | n | % | ||||||
| Sex | Female | 5 | 4.9% | 0 | 0.0% | 0.146 | 0.121 | 0 | 0.0% | 1 | 6.3% | 0.420 | 0.158† |
| Male | 98 | 95.1% | 42 | 100% | 10 | 100% | 15 | 93.8% | |||||
| Civil status | Single | 46 | 44.7% | 26 | 61.9% | 0.164 | 0.158† | 5 | 50.0% | 14 | 87.5% | 0.091 | 0.430† |
| Married – couple | 48 | 46.6% | 13 | 31.0% | 4 | 40.0% | 2 | 12.5% | |||||
| Divorced – separated | 9 | 8.7% | 3 | 7.1% | 1 | 10.0% | 0 | 0.0% | |||||
| Education | Primary | 55 | 53.4% | 23 | 54.8% | 0.810 | 0.054 | 7 | 70.0% | 6 | 37.5% | 0.070 | 0.452† |
| Secondary | 40 | 38.8% | 17 | 40.5% | 2 | 20.0% | 10 | 62.5% | |||||
| University | 8 | 7.8% | 2 | 4.8% | 1 | 10.0% | 0 | 0.0% | |||||
| Employment | Unemployed | 35 | 34.0% | 16 | 38.1% | 0.638 | 0.039 | 5 | 50.0% | 3 | 18.8% | 0.093 | 0.329† |
| Employed | 68 | 66.0% | 26 | 61.9% | 5 | 50.0% | 13 | 81.3% | |||||
| Social | High | 4 | 3.9% | 1 | 2.4% | 0.269 | 0.189† | 0 | 0.0% | 0 | 0.0% | 0.254 | 0.395† |
| Mean-high | 9 | 8.7% | 6 | 14.3% | 1 | 10.0% | 3 | 18.8% | |||||
| Mean | 9 | 8.7% | 4 | 9.5% | 0 | 0.0% | 4 | 25.0% | |||||
| Mean-low | 29 | 28.2% | 5 | 11.9% | 3 | 30.0% | 4 | 25.0% | |||||
| Low | 52 | 50.5% | 26 | 61.9% | 6 | 60.0% | 5 | 31.3% | |||||
| Gambling activity | n | % | n | % | p | C-V | n | % | n | % | p | C-V | |
| Preference | Non-strategic | 46 | 44.7% | 16 | 38.1% | 0.739 | 0.065 | 2 | 20.0% | 7 | 43.8% | 0.457 | 0.246† |
| Strategic | 39 | 37.9% | 17 | 40.5% | 5 | 50.0% | 6 | 37.5% | |||||
| Mixed | 18 | 17.5% | 9 | 21.4% | 3 | 30.0% | 3 | 18.8% | |||||
| Modality | Land based | 52 | 50.5% | 20 | 47.6% | 0.427 | 0.108 | 3 | 30.0% | 8 | 50.0% | 0.446 | 0.249† |
| Online | 29 | 28.2% | 9 | 21.4% | 3 | 30.0% | 5 | 31.3% | |||||
| Mixed | 22 | 21.4% | 13 | 31.0% | 4 | 40.0% | 3 | 18.8% | |||||
| Ages and gambling severity | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| Age (years-old) | 19 | 74 | 41.59 | 14.25 | 20 | 63 | 35.00 | 11.82 | 0.009* | 0.50† | 20 | 62 | 37.00 | 14.54 | 18 | 55 | 37.69 | 12.33 | 0.898 | 0.05 |
| GD onset (years-old) | 14 | 62 | 28.76 | 12.03 | 14 | 54 | 25.52 | 9.36 | 0.121 | 0.30 | 15 | 51 | 25.05 | 10.74 | 17 | 49 | 28.15 | 9.09 | 0.438 | 0.31 |
| GD duration (years) | 1 | 28 | 5.58 | 6.44 | 1 | 20 | 5.52 | 4.72 | 0.957 | 0.01 | 1 | 25 | 5.90 | 7.28 | 1 | 25 | 7.19 | 8.19 | 0.688 | 0.17 |
| DSM-5: total criteria for GD | 1 | 9 | 7.34 | 1.75 | 3 | 9 | 7.24 | 1.82 | 0.754 | 0.06 | 5 | 9 | 7.90 | 1.45 | 6 | 9 | 8.13 | 1.26 | 0.679 | 0.17 |
| SOGS: total | 3 | 18 | 10.80 | 3.16 | 4 | 17 | 10.98 | 3.89 | 0.772 | 0.05 | 7 | 17 | 12.10 | 3.14 | 8 | 17 | 13.63 | 2.90 | 0.218 | 0.50† |
| PPCS: total score | 6 | 18 | 10.28 | 3.86 | 6 | 19 | 9.79 | 4.04 | 0.490 | 0.13 | 21 | 39 | 25.60 | 6.15 | 21 | 39 | 27.13 | 5.57 | 0.520 | 0.26 |
| Psychology profile | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| SCL-90-R GSI | 0 | 3 | 1.02 | 0.70 | 0 | 3 | 1.24 | 0.75 | 0.098 | 0.30 | 1 | 3 | 1.80 | 0.55 | 1 | 3 | 1.62 | 0.65 | 0.464 | 0.31 |
| UPPS-P: Lack premeditation | 12 | 42 | 24.73 | 6.23 | 12 | 39 | 25.67 | 5.48 | 0.396 | 0.16 | 15 | 31 | 23.50 | 6.06 | 16 | 35 | 27.19 | 5.50 | 0.123 | 0.64† |
| UPPS-P: Lack perseverance | 11 | 36 | 21.94 | 5.29 | 14 | 30 | 22.74 | 4.55 | 0.394 | 0.16 | 13 | 31 | 23.00 | 5.12 | 18 | 36 | 26.44 | 5.60 | 0.129 | 0.64† |
| UPPS-P: Sensation seeking | 12 | 46 | 27.87 | 7.98 | 16 | 43 | 29.24 | 7.34 | 0.341 | 0.18 | 22 | 43 | 34.00 | 7.12 | 17 | 47 | 33.69 | 8.58 | 0.924 | 0.04 |
| UPPS-P: Positive urgency | 14 | 56 | 32.40 | 11.17 | 14 | 45 | 31.83 | 8.10 | 0.767 | 0.06 | 23 | 49 | 35.30 | 8.53 | 17 | 54 | 35.88 | 10.81 | 0.888 | 0.06 |
| UPPS-P: Negative urgency | 13 | 47 | 32.09 | 7.61 | 21 | 46 | 32.02 | 6.31 | 0.962 | 0.01 | 27 | 41 | 34.50 | 4.79 | 17 | 44 | 33.88 | 6.86 | 0.804 | 0.11 |
| UPPS-P: total | 84 | 192 | 139.05 | 23.91 | 93 | 182 | 141.50 | 20.39 | 0.561 | 0.11 | 114 | 186 | 150.40 | 20.49 | 119 | 205 | 157.06 | 23.02 | 0.462 | 0.31 |
| DERS: total | 45 | 149 | 96.88 | 22.45 | 61 | 139 | 93.83 | 18.69 | 0.438 | 0.15 | 86 | 127 | 106.70 | 13.68 | 66 | 144 | 107.88 | 25.49 | 0.895 | 0.06 |
| Personality profile | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| TCI-R: novelty seeking | 77 | 146 | 109.28 | 13.60 | 92 | 133 | 112.79 | 10.91 | 0.140 | 0.28 | 100 | 141 | 114.20 | 12.66 | 96 | 136 | 112.06 | 9.38 | 0.626 | 0.19 |
| TCI-R: harm avoidance | 63 | 159 | 99.63 | 17.04 | 66 | 147 | 99.24 | 15.63 | 0.898 | 0.02 | 83 | 126 | 103.80 | 15.44 | 72 | 118 | 100.94 | 14.44 | 0.636 | 0.19 |
| TCI-R: reward dependence | 62 | 131 | 94.90 | 13.60 | 60 | 118 | 93.83 | 12.82 | 0.663 | 0.08 | 78 | 116 | 93.50 | 11.96 | 55 | 110 | 89.69 | 14.96 | 0.503 | 0.28 |
| TCI-R: persistence | 63 | 154 | 110.78 | 19.27 | 73 | 139 | 111.14 | 16.93 | 0.915 | 0.02 | 74 | 155 | 109.40 | 21.05 | 69 | 168 | 116.19 | 23.74 | 0.467 | 0.30 |
| TCI-R: self-directedness | 69 | 170 | 126.27 | 20.00 | 81 | 162 | 126.81 | 18.89 | 0.882 | 0.03 | 101 | 143 | 117.90 | 15.18 | 89 | 143 | 115.44 | 16.20 | 0.703 | 0.16 |
| TCI-R: cooperativeness | 95 | 168 | 131.51 | 14.95 | 99 | 149 | 126.62 | 14.24 | 0.072 | 0.34 | 98 | 146 | 121.30 | 17.89 | 91 | 146 | 117.88 | 16.84 | 0.627 | 0.20 |
| TCI-R: self-transcendence | 32 | 114 | 63.92 | 16.66 | 33 | 111 | 62.43 | 16.40 | 0.624 | 0.09 | 38 | 96 | 75.80 | 17.02 | 38 | 91 | 65.63 | 11.59 | 0.081 | 0.70† |
Note. C-V: Cramer's-V coefficient; DERS: Difficulties in Emotion Regulation Scale; DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5th Edition; GD: gambling disorder; PPCS: Problematic Pornography Consumption Scale; PPU: problematic pornography use; SCL-90-R: Symptom Checklist-Revised; SD: standard deviation; SOGS: South Oaks Gambling Screen; TCI-R: Temperament and Character Inventory-Revised; UPPS-P: Impulsive Behavior Scale; |d|: Cohen's-d coefficient (absolute value).
*Bold: significant statistical comparison. †Bold: effect size within the ranges mild-moderate to large-high.
Table 4 contains the comparison between patients with and without relapses, stratified by the diagnostic subtype. Within the GD patients, the likelihood of relapses was higher for divorced/separated patients, and those with lower social position indexes, higher GD severity (as measured with the SOGS), higher negative urgency, and lower self-directedness. Within the GD+PPU patients, the likelihood of relapse was higher for women, non-single individuals, and those with higher education levels, higher social position levels, engagement in land-based gambling, higher psychopathology distress, higher impulsivity (sensation-seeking, positive urgency and total), and higher self-directedness and cooperativeness. These results should be interpreted with caution due to the low sample size for some subgroups (specifically, among those with GD+PPU).
Variables associated relapse within each diagnostic subtype
| Sociodemographic | GD (n = 146) | p | C-V | GD+PPU (n = 26) | p | C-V | |||||||
| Relapses = No (n = 133) | Relapses = Yes (n = 33) | Relapses = No (n = 16) | Relapses = Yes (n = 10) | ||||||||||
| n | % | n | % | n | % | n | % | ||||||
| Sex | Female | 4 | 3.5% | 1 | 3.0% | 0.887 | 0.012 | 0 | 0.0% | 1 | 10.0% | 0.197 | 0.253† |
| Male | 109 | 96.5% | 32 | 97.0% | 16 | 100% | 9 | 90.0% | |||||
| Civil status | Single | 59 | 52.2% | 14 | 42.4% | 0.058 | 0.198† | 13 | 81.3% | 6 | 60.0% | 0.314 | 0.299† |
| Married – couple | 48 | 42.5% | 13 | 39.4% | 3 | 18.8% | 3 | 30.0% | |||||
| Divorced – separated | 6 | 5.3% | 6 | 18.2% | 0 | 0.0% | 1 | 10.0% | |||||
| Education | Primary | 61 | 54.0% | 18 | 54.5% | 0.822 | 0.052 | 9 | 56.3% | 4 | 40.0% | 0.372 | 0.276† |
| Secondary | 45 | 39.8% | 12 | 36.4% | 7 | 43.8% | 5 | 50.0% | |||||
| University | 7 | 6.2% | 3 | 9.1% | 0 | 0.0% | 1 | 10.0% | |||||
| Employment | Unemployed | 42 | 37.2% | 9 | 27.3% | 0.294 | 0.087 | 5 | 31.3% | 3 | 30.0% | 0.946 | 0.013 |
| Employed | 71 | 62.8% | 24 | 72.7% | 11 | 68.8% | 7 | 70.0% | |||||
| Social | High | 4 | 3.5% | 1 | 3.0% | 0.078 | 0.240† | 0 | 0.0% | 0 | 0.0% | 0.130 | 0.466† |
| Mean-high | 13 | 11.5% | 2 | 6.1% | 2 | 12.5% | 2 | 20.0% | |||||
| Mean | 10 | 8.8% | 3 | 9.1% | 3 | 18.8% | 1 | 10.0% | |||||
| Mean-low | 21 | 18.6% | 14 | 42.4% | 2 | 12.5% | 5 | 50.0% | |||||
| Low | 65 | 57.5% | 13 | 39.4% | 9 | 56.3% | 2 | 20.0% | |||||
| Gambling activity | n | % | n | % | p | C-V | n | % | n | % | p | C-V | |
| Preference | Non-strategic | 48 | 42.5% | 14 | 42.4% | 0.337 | 0.122 | 6 | 37.5% | 3 | 30.0% | 0.821 | 0.123 |
| Strategic | 46 | 40.7% | 10 | 30.3% | 6 | 37.5% | 5 | 50.0% | |||||
| Mixed | 19 | 16.8% | 9 | 27.3% | 4 | 25.0% | 2 | 20.0% | |||||
| Modality | Land based | 58 | 51.3% | 15 | 45.5% | 0.788 | 0.057 | 6 | 37.5% | 5 | 50.0% | 0.639 | 0.186† |
| Online | 28 | 24.8% | 10 | 30.3% | 6 | 37.5% | 2 | 20.0% | |||||
| Mixed | 27 | 23.9% | 8 | 24.2% | 4 | 25.0% | 3 | 30.0% | |||||
| Ages and gambling severity | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| Age (years-old) | 19 | 74 | 39.33 | 14.51 | 25 | 66 | 40.79 | 11.32 | 0.595 | 0.11 | 18 | 55 | 35.88 | 13.79 | 24 | 62 | 39.90 | 11.70 | 0.452 | 0.31 |
| GD onset (years-old) | 14 | 62 | 27.42 | 10.59 | 15 | 62 | 29.28 | 13.72 | 0.410 | 0.15 | 15 | 49 | 27.24 | 9.69 | 16 | 51 | 26.50 | 10.14 | 0.853 | 0.08 |
| GD duration (years) | 1 | 28 | 5.74 | 6.08 | 1 | 27 | 4.97 | 5.57 | 0.513 | 0.13 | 1 | 25 | 7.81 | 9.45 | 1 | 10 | 4.90 | 3.35 | 0.360 | 0.41 |
| DSM-5: total criteria for GD | 1 | 9 | 7.18 | 1.76 | 2 | 9 | 7.79 | 1.67 | 0.079 | 0.36 | 6 | 9 | 7.88 | 1.31 | 5 | 9 | 8.30 | 1.34 | 0.432 | 0.32 |
| SOGS: total | 3 | 18 | 10.55 | 3.38 | 7 | 18 | 11.88 | 3.13 | 0.045* | 0.41 | 7 | 17 | 12.88 | 3.40 | 8 | 16 | 13.30 | 2.45 | 0.735 | 0.14 |
| PPCS: total score | 6 | 19 | 9.96 | 3.85 | 6 | 18 | 10.61 | 4.13 | 0.409 | 0.16 | 21 | 39 | 27.50 | 6.56 | 22 | 35 | 25.00 | 3.89 | 0.288 | 0.46 |
| Psychology profile | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| SCL-90-R GSI | 0 | 3 | 1.05 | 0.70 | 0 | 3 | 1.18 | 0.79 | 0.374 | 0.17 | 1 | 2 | 1.54 | 0.51 | 1 | 3 | 1.93 | 0.70 | 0.117 | 0.63† |
| UPPS-P: Lack premeditation | 12 | 42 | 25.21 | 6.02 | 12 | 35 | 24.15 | 5.97 | 0.374 | 0.18 | 15 | 34 | 25.50 | 6.18 | 15 | 35 | 26.20 | 5.71 | 0.775 | 0.12 |
| UPPS-P: Lack perseverance | 11 | 36 | 22.20 | 4.92 | 11 | 33 | 21.88 | 5.71 | 0.748 | 0.06 | 13 | 36 | 25.38 | 6.11 | 18 | 33 | 24.70 | 4.90 | 0.771 | 0.12 |
| UPPS-P: Sensation seeking | 12 | 46 | 28.17 | 7.76 | 15 | 44 | 28.30 | 8.15 | 0.931 | 0.02 | 21 | 47 | 35.50 | 7.97 | 17 | 45 | 31.10 | 7.39 | 0.172 | 0.57† |
| UPPS-P: Positive urgency | 14 | 56 | 31.34 | 9.66 | 14 | 56 | 35.06 | 12.12 | 0.069 | 0.34 | 23 | 54 | 39.13 | 8.18 | 17 | 49 | 30.10 | 10.02 | 0.019* | 0.99† |
| UPPS-P: Negative urgency | 13 | 45 | 31.25 | 6.72 | 13 | 47 | 34.82 | 8.25 | 0.012* | 0.47 | 27 | 44 | 34.75 | 5.94 | 17 | 40 | 33.10 | 6.40 | 0.510 | 0.27 |
| UPPS-P: total | 84 | 192 | 138.19 | 22.42 | 94 | 182 | 144.21 | 24.54 | 0.186 | 0.26 | 114 | 205 | 160.25 | 24.48 | 119 | 162 | 145.30 | 13.48 | 0.091 | 0.76† |
| DERS: total | 46 | 147 | 95.52 | 20.77 | 45 | 149 | 97.73 | 23.44 | 0.603 | 0.10 | 66 | 144 | 104.69 | 21.89 | 67 | 137 | 111.80 | 20.93 | 0.421 | 0.33 |
| Personality profile | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| | Min | Max | Mean | SD | Min | Max | Mean | SD | p | |d| |
| TCI-R: novelty seeking | 77 | 144 | 109.15 | 12.30 | 90 | 146 | 114.06 | 14.32 | 0.054 | 0.37 | 96 | 141 | 113.25 | 12.21 | 100 | 127 | 112.30 | 7.83 | 0.829 | 0.09 |
| TCI-R: harm avoidance | 63 | 147 | 98.90 | 15.86 | 69 | 159 | 101.55 | 18.77 | 0.421 | 0.15 | 80 | 126 | 102.75 | 11.95 | 72 | 126 | 100.90 | 18.73 | 0.760 | 0.12 |
| TCI-R: reward dependence | 60 | 125 | 93.40 | 13.19 | 75 | 131 | 98.70 | 13.03 | 0.044 | 0.40 | 55 | 105 | 89.13 | 13.22 | 62 | 116 | 94.40 | 14.68 | 0.352 | 0.38 |
| TCI-R: persistence | 63 | 151 | 112.05 | 17.94 | 73 | 154 | 106.39 | 20.17 | 0.123 | 0.30 | 69 | 168 | 114.38 | 26.56 | 90 | 147 | 112.30 | 15.35 | 0.825 | 0.10 |
| TCI-R: self-directedness | 78 | 170 | 128.36 | 18.20 | 69 | 164 | 120.18 | 22.90 | 0.034* | 0.40 | 89 | 143 | 112.75 | 15.55 | 105 | 143 | 122.20 | 14.42 | 0.135 | 0.63† |
| TCI-R: cooperativeness | 95 | 168 | 129.76 | 14.75 | 98 | 151 | 131.21 | 15.21 | 0.622 | 0.10 | 91 | 146 | 114.63 | 17.94 | 109 | 146 | 126.50 | 12.96 | 0.082 | 0.76† |
| TCI-R: self-transcendence | 32 | 111 | 63.52 | 15.96 | 38 | 114 | 62.82 | 18.72 | 0.831 | 0.04 | 38 | 96 | 67.13 | 14.93 | 58 | 92 | 73.40 | 13.63 | 0.292 | 0.44 |
Note. C-V: Cramer's-V coefficient; DERS: Difficulties in Emotion Regulation Scale; DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5th Edition; GD: gambling disorder; PPCS: Problematic Pornography Consumption Scale; PPU: problematic pornography use; SCL-90-R: Symptom Checklist-Revised; SD: standard deviation; SOGS: South Oaks Gambling Screen; TCI-R: Temperament and Character Inventory-Revised; UPPS-P: Impulsive Behavior Scale; |d|: Cohen's-d coefficient (absolute value).
*Bold: significant statistical comparison. †Bold: effect size within the ranges mild-moderate to large-high.
Figure 1 shows the cumulate survival functions for dropout and relapse, as well as the results of the comparison between the groups. The group with PPU demonstrated greater likelihoods of dropout and relapse.
Survival curves for dropout and relapse
Note. GD: gambling disorder; PPU: problematic pornography use.
Citation: Journal of Behavioral Addictions 14, 1; 10.1556/2006.2025.00023
Discussion
The present study aimed to evaluate the treatment outcome of 172 adults with GD. More specifically, treatment responses (dropout and relapse, number of sessions attended, number of relapses, and the amount of money spent during relapses) were compared between two groups: those with and without PPU. It was hypothesized that individuals with co-occurring GD and PPU would exhibit a more maladaptive clinical profile. In line with our hypothesis, patients with GD and PPU presented with more severe GD, more psychopathology, higher impulsivity, greater emotion dysregulation and lower cooperativeness and self-directedness. These findings are consistent with previous studies, both those focusing on co-occurrence between GD and PPU (Mestre-Bach et al., 2023) and those focusing on co-occurrence between GD and other behavioral addictions (Cowie et al., 2019; Granero et al., 2016; Jiménez-Murcia, Granero, et al., 2017; Tang et al., 2020). Moreover, it was hypothesized that the co-occurrence of GD and PPU would be related to poorer treatment outcome.
Regarding dropout, it is usually understood as leaving an intervention before it is finished (Mestre-Bach & Potenza, 2023). In the treatment of GD, dropout percentages of 39% have been described (Pfund et al., 2021). In the present study, although the dropout likelihood of the total sample was 33.9%, similar to previous studies (Pfund et al., 2021), the dropout likelihood of the group with co-occurring GD and PPU was about twice as high (61.54%). Factors such as impulsivity (Mestre-Bach et al., 2019), emotional dysregulation (Vintró-Alcaraz et al., 2022), and psychopathology (Baño et al., 2021; Jiménez-Murcia et al., 2007), among others, have been associated with greater dropout likelihoods in individuals with GD. Considering that patients with co-occurring GD and PPU show higher impulsivity, greater emotional dysregulation, and more psychopathology (Mestre-Bach et al., 2023, 2024), this could in part explain the significant differences in dropout between both groups in the present study. This clinical profile may be associated with greater functional impairment, which could negatively impact treatment engagement in individuals with co-occurring GD and PPU. Moreover, the group with both clinical conditions may be experiencing greater stigma, which may also contribute to dropout. While this notion is currently speculative, it warrants future direct study.
Relapse involves gambling that goes against an individual's personal goals while striving for abstinence (Mestre-Bach & Potenza, 2023). In the present study, the likelihood of relapse of the group with PPU was higher than that of the group without PPU (38.46% and 22.60% respectively), although this difference was not statistically significant. The group with co-occurrence demonstrated a higher likelihood of relapse, as well as more money wagered during relapses (exceeding double the amount compared to the group without PPU). Individuals with co-occurring GD and PPU may relapse more frequently and spend more money during relapses due to, among other factors, higher levels of impulsivity and greater difficulties in emotional regulation. These challenges may impair their ability to control gambling behavior, leading to relapse. Furthermore, they may struggle to stop the gambling episode, resulting in higher amounts of money spent during the relapse.
The cumulate survival functions for relapse showed that the group with PPU was more likely to relapse. Differences in findings depending on modeling of relapse have been previously reported (Mestre-Bach & Potenza, 2023), with some models approaching and others reaching statistical significance. Potential discrepancies between our findings and previous studies may be due to the different definitions and heterogeneous assessments of relapse across studies (Mestre-Bach & Potenza, 2023). For instance, variations in how relapse is conceptualized (such as the number of gambling episodes considered or the presence of “loss of control” feelings) could be influencing the results. Additionally, differences in the study populations may also contribute to potential inconsistencies, highlighting the need for standardized criteria to better compare results across studies.
Clinical implications
The co-occurrence of GD and PPU appears to be associated with a more severe clinical profile, including greater GD severity, more psychopathology, greater impulsivity, and more emotional dysregulation. Therefore, assessing GD and PPU co-occurrence prior to starting treatment appears important. Additionally, patients with both conditions appear at higher risk of dropout during CBT. Given that dropout was highest during the first four sessions in the present study, it is important to consider strategies that may help promote engagement at treatment onset when designing interventions for individuals with co-occurring GD and PPU. For example, some recommendations from the World Federation of Societies of Biological Psychiatry guidelines for CSBD (Turner et al., 2022) could be incorporated into CBT for patients with co-occurring GD and PPU: (a) building a network of supportive people; (b) reducing shame associated with the disorders and restoring self-esteem; (c) teaching personal skills through communication; (d) helping patients reacquire control of sexual behavior and develop a healthier approach to sexuality; (e) decreasing distress; (f) managing financial and legal consequences; and (g) treating somatic concerns. Additionally, incorporating strategies in early sessions to reduce impulsivity and improve emotional regulation, such as those strategies found in mindfulness-based treatments, could help reduce drop-out (which is high early in treatment). Since core elements of both disorders would benefit from being addressed, this approach may be considered from a transdiagnostic perspective.
Moreover, it is relatively common for comorbid concerns, such as behavioral addictions (e.g., PPU, compulsive buying, gaming disorder), that are not the primary reason for consultation to remain undisclosed unless specifically explored. Patients may not voluntarily disclose these issues, often due to associated shame and stigma or ambivalence about ceasing the behaviors. Consequently, clinicians may face situations where patients discontinue treatment (as suggested by the findings of the present study) due to the severity and complexity of both conditions, which in part may have gone unnoticed and unaddressed. Such oversight may limit treatment outcomes. Therefore, conducting a comprehensive assessment of all behavioral addictions is important, regardless of the presenting concern. In this sample, individuals were not treated for PPU. The extent to which individuals with co-occurring PPU may be interested in PPU treatment and the extent to which specific treatments for PPU may help address both the PPU and co-occurring concerns warrants further investigation, especially as treating co-occurring disorders in the context of GD has been associated with improvements in both GD and the co-occurring disorder (Potenza et al., 2019).
Limitations and future studies
Study limitations should be considered. First, the sample sizes between the two clinical groups were not balanced, with the group experiencing co-occurring GD and PPU being smaller. The interpretation of the findings should be made with caution due to the small sample size, which may limit the stability and generalizability of the results. Additionally, the sex distribution was unbalanced, with more men than women included in the study. Future research could benefit from larger, more balanced groups. Second, the lack of established diagnostic criteria for PPU, as it is not yet recognized as a formal diagnostic entity, complicates its assessment. We chose to use the PPCS-6, as it is one of the most widely used tools in both clinical practice and research. However, it may introduce potential biases, such as social desirability bias. The same applies to other variables in this study, which were measured using self-report instruments. Third, the study focused specifically on the co-occurrence of GD and PPU, without assessing other potential co-occurring conditions (e.g., substance use disorders, depression, anxiety, etc.). Future studies could explore additional co-occurrences, as well as different types of interventions, given that this research only focused on CBT. Along this line, we included patients with GD with or without PPU from a clinic in which GD was the main reason for seeking help. The extent to which the findings extend to other groups is not known. Fourth, both dropout and relapse are constructs for which there is still no consensus regarding their optimal assessment (Mestre-Bach & Potenza, 2023), complicating efforts to compare the present findings with previous ones. Finally, the study did not include a post-treatment assessment of the factors measured (e.g., GD severity, impulsivity, emotional regulation, etc.). Future research on treatment outcomes might consider including post-treatment evaluations, rather than merely focusing on relapse and dropout.
Conclusions
The existing literature has identified shared mechanisms between GD and other behavioral addictions, but few studies have specifically analyzed the impact of GD-PPU co-occurrence on treatment outcomes. The present study sought to address this gap by analyzing treatment outcomes in individuals with GD. The co-occurrence of GD and PPU appears to be associated with a more severe clinical profile and poorer treatment outcomes (including higher dropout and relapse rates), highlighting the importance of comprehensive assessments and personalized interventions. Incorporating into CBT strategies such as improving emotional regulation, reducing impulsivity, and addressing stigma could potentially improve adherence and outcomes, supporting a transdiagnostic approach.
Funding sources
This work was supported by the ITEI B23-010 project (Universidad Internacional de La Rioja). We thank CERCA Programme/Generalitat de Catalunya for guarante institutional support. This manuscript and research was supported by grants from Plan Nacional sobre Drogas Convocatoria de subvenciones para proyectos de investigación financiados con fondos europeos 2022 (EXP2022/008847), Ministerio de Ciencia e Innovación (PDI2021-124887OB-I00), Instituto de Salud Carlos III (ISCIII) (Exp: FIS22053 – Ref: DTS22/00072), European Union’s Horizon 2020 research and innovation programme under Grant agreement no. 101080219 (eprObes), and cofounded by FEDER (funds/European Regional Development Fund (ERDF), a way to build Europe). CIBERObn is an initiative of ISCIII. Additional funding was received by AGAUR-Generalitat de Catalunya (2021-SGR-00824) and Instituto de Salud Carlos III (ISCIII) (Exp: FIS23069 – Ref: FORT23/00032_2). RG is supported by the Catalan Institution for Research and Advanced Studies (ICREA-Academia, 2021- Programme). FFA is supported by the Catalan Institution for Research and Advanced Studies (ICREA-Academia, 2024- Programme). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Authors' contribution
GMB, RG, FFA, SJM: study concept and design; GMB, JCU: methodology; RG: statistical analysis; RG, SJM, GMB: interpretation of data; MNP, SJM, FFA: study supervision; GMB, MNP, RG, AH: first draft of the manuscript; GMB, MNP, RG, JCU, AH, FFA, SJM: revision of the manuscript.
Conflict of interest
Dr. Potenza discloses that he has consulted for and advised Boehringer Ingelheim, Baria-Tek, and Opiant Therapeutics; been involved in a patent application with Yale University and Novartis; received research support from the Mohegan Sun Casino and the Connecticut Council on Problem Gambling; consulted for or advised legal, non-profit and gambling entities on issues related to impulse control, internet use and addictive behaviors; provided clinical care related to impulse-control and addictive behaviors; performed grant reviews; edited journals/journal sections; given academic lectures in grand rounds, CME events, and other clinical/scientific venues; and generated books or chapters for publishers of mental health texts. He serves as an associate editor of the Journal of Behavioral Addictions. Dr. Fernández-Aranda and Dr. Jiménez-Murcia received consultancy honoraria from Novo Nordisk. The other authors have no conflicts of interest with respect to the content of this manuscript.
References
Aalen, O. O., Borgan, O., & Gjessing, H. K. (2008). Survival and event history analysis: A process Point of view. Springer.
André, F., Håkansson, A., & Claesdotter-Knutsson, E. (2022). Gaming, substance use and distress within a cohort of online gamblers. Journal of Public Health Research, 11(1), 3434. https://doi.org/10.4081/jphr.2021.3434.
APA (2013). Diagnostic and statistical Manual of mental disorders 5 edition (DSM-5) 5.. American Psychiatric Association.
Baño, M., Mestre-Bach, G., Granero, R., Fernández-Aranda, F., Gómez-Peña, M., Moragas, L., … Jiménez-Murcia, S. (2021). Women and gambling disorder: Assessing dropouts and relapses in cognitive behavioral group therapy. Addictive Behaviors, 123, 107085. https://doi.org/10.1016/j.addbeh.2021.107085.
Banz, B. C., Yip, S. W., Yau, Y. H. C., & Potenza, M. N. (2016). Behavioral addictions in addiction medicine: From mechanisms to practical considerations. Progress in Brain Research, 311–328. https://doi.org/10.1016/bs.pbr.2015.08.003.
Bőthe, B., Tóth-Király, I., Bella, N., Potenza, M. N., Demetrovics, Z., & Orosz, G. (2021). Why do people watch pornography? The motivational basis of pornography use. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 35(2), 172–186. https://doi.org/10.1037/adb0000603.
Bőthe, B., Tóth-Király, I., Demetrovics, Z., & Orosz, G. (2021). The short version of the problematic pornography consumption scale (PPCS-6): A reliable and valid measure in general and treatment-seeking populations. Journal of Sex Research, 58(3), 342–352. https://doi.org/10.1080/00224499.2020.1716205.
Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neuroscience and Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033.
Choi, S.-W., Kim, H., Kim, G.-Y., Jeon, Y., Park, S., Lee, J.-Y., … Kim, D.-J. (2014). Similarities and differences among Internet gaming disorder, gambling disorder and alcohol use disorder: A focus on impulsivity and compulsivity. Journal of Behavioral Addictions, 3(4), 246–253. https://doi.org/10.1556/JBA.3.2014.4.6.
Cloninger, C. R. (1999). The temperament and character inventoryrevised. St. Louis,MO: Center for Psychobiology of Personality.
Cowie, M. E., Kim, H. S., Hodgins, D. C., McGrath, D. S., Scanavino, M. D. T., & Tavares, H. (2019). Demographic and psychiatric correlates of compulsive sexual behaviors in gambling disorder. Journal of Behavioral Addictions, 8(3), 451–462. https://doi.org/10.1556/2006.8.2019.35.
Derogatis, L. (1990). SCL-90-R. Administration, scoring and procedures manual (Clinical P).
Derogatis, L. R. (2002). SCL-90-R. Cuestionario de 90 síntomas-Manual. TEA Editorial.
Di Nicola, M., De Risio, L., Pettorruso, M., Caselli, G., De Crescenzo, F., Swierkosz-Lenart, K., … Janiri, L. (2014). Bipolar disorder and gambling disorder comorbidity: Current evidence and implications for pharmacological treatment. Journal of Affective Disorders, 167, 285–298. https://doi.org/10.1016/j.jad.2014.06.023.
Echeburúa, E., Báez, C., Fernández-Montalvo, J., & Páez, D. (1994). Cuestionario de Juego Patológico de South Oaks (SOGS): validación española. Análisis y Modificación de Conducta, 20(74), 769–791.
Finner, H. (1993). On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association, 88, 920–923.
Granero, R., Fernández-Aranda, F., Baño, M., Steward, T., Mestre-Bach, G., Del Pino-Gutiérrez, A., … Jiménez-Murcia, S. (2016). Compulsive buying disorder clustering based on sex, age, onset and personality traits. Comprehensive Psychiatry, 68, 1–10. https://doi.org/10.1016/j.comppsych.2016.03.003.
Granero, R., Fernández-Aranda, F., Pino-Gutierrez, A. Del, Etxandi, M., Baenas, I., Gómez-Peña, M., … Jiménez-Murcia, S. (2021). The prevalence and features of schizophrenia among individuals with gambling disorder. Journal of Psychiatric Research, 136, 374–383. https://doi.org/10.1016/j.jpsychires.2021.02.025.
Grant, J. E., & Chamberlain, S. R. (2020). Gambling and substance use: Comorbidity and treatment implications. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 99, 109852. https://doi.org/10.1016/j.pnpbp.2019.109852.
Grant, J. E., & Steinberg, M. A. (2005a). Compulsive sexual behavior and pathological gambling. Sexual Addiction & Compulsivity, 12(2–3), 235–244. https://doi.org/10.1080/10720160500203856.
Grant, J. E., & Steinberg, M. A. (2005b). Compulsive sexual behavior and pathological gambling. Sexual Addiction & Compulsivity, 12(2–3), 235–244. https://doi.org/10.1080/10720160500203856.
Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. Journal of Psychopathology and Behavioral Assessment, 26(1), 41–54. https://doi.org/10.1023/B:JOBA.0000007455.08539.94.
Guerrero-Vaca, D., Granero, R., Fernández-Aranda, F., González-Doña, J., Müller, A., Brand, M., … Jiménez-Murcia, S. (2019). Underlying mechanism of the comorbid presence of buying disorder with gambling disorder: A pathways analysis. Journal of Gambling Studies, 35(1), 261–273. https://doi.org/10.1007/s10899-018-9786-7.
Gutiérrez-Zotes, J. A., Bayón, C., Montserrat, C., Valero, J., Labad, A., Cloninger, C. R., & Fernández-Aranda, F. (2004). Temperament and Character Inventory Revised (TCI-R). Standardization and normative data in a general population sample. Actas Españolas de Psiquiatria, 32(1), 8–15.
Hervás, G., & Jódar, R. (2008). Adaptación al castellano de la Escala de Dificultades en la Regulación Emocional. Clínica y Salud, 19(2), 139–156.
Jara-Rizzo, M. F., Navas, J. F., Steward, T., López-Gómez, M., Jiménez-Murcia, S., Fernández-Aranda, F., & Perales, J. C. (2019). Impulsivity and problem awareness predict therapy compliance and dropout from treatment for gambling disorder. Adicciones, 31(2), 147–159. https://doi.org/10.20882/adicciones.1041.
Jiménez-Murcia, S., Álvarez-Moya, E. M., Granero, R., Neus Aymami, M., Gómez-Peña, M., Jaurrieta, N., … Vallejo, J. (2007). Cognitive–behavioral group treatment for pathological gambling: Analysis of effectiveness and predictors of therapy outcome. Psychotherapy Research, 17(5), 544–552. https://doi.org/10.1080/10503300601158822.
Jimenez-Murcia, S., Aymamí, N., Gómez-Peña, M., Santamaría, J. J., Álvarez-Moya, E., Fernández-Aranda, F., … Menchón, J. M. (2012). Does exposure and response prevention improve the results of group cognitive-behavioural therapy for male slot machine pathological gamblers? British Journal of Clinical Psychology, 51(1), 54–71. https://doi.org/10.1111/j.2044-8260.2011.02012.x.
Jiménez-Murcia, S., Aymamí-Sanromà, M., Gómez-Peña, M., Álvarez-Moya, E., & Vallejo, J. (2006). Protocols de tractament cognitivoconductual pel joc patològic i d’altres addiccions no tòxiques [Cognitive-behavioral treatment protocols for pathological gambling and other non-toxic addictions. Barcelona, Spain: Hospital Universitari de Bellvitge, Departament de Salut, Generalitat de Catalunya.
Jiménez-Murcia, S., Granero, R., Testa, G., Tarragón, E., Potenza, M. N., Bothe, B., … Mestre-Bach, G. (2023). Spanish validation of the PPCS-6 in clinical population with behavioral addictions.
Jiménez-Murcia, S., Granero, R., Wolz, I., Baño, M., Mestre-Bach, G., Steward, T., … Fernández-Aranda, F. (2017). Food addiction in gambling disorder: Frequency and clinical outcomes. Frontiers in Psychology, 8, 473. https://doi.org/10.3389/fpsyg.2017.00473.
Jiménez-Murcia, S., Tremblay, J., Stinchfield, R., Granero, R., Fernández-Aranda, F., Mestre-Bach, G., … Menchón, J. M. (2017). The involvement of a concerned significant other in gambling disorder treatment outcome. Journal of Gambling Studies, 33(3), 937–953. https://doi.org/10.1007/s10899-016-9657-z.
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137–152. https://doi.org/10.1037/a0028086.
Lemón, L., Fernández-Aranda, F., Jiménez-Murcia, S., & Håkansson, A. (2021). Eating disorder in gambling disorder: A group with increased psychopathology. Journal of Behavioral Addictions, 10(3), 540–545. https://doi.org/10.1556/2006.2021.00060.
Lesieur, H. R., & Blume, S. B. (1987). The South Oaks gambling screen (SOGS): A new instrument for the identification of pathological gamblers. The American Journal of Psychiatry, 144(9), 1184–1188.
Mallorquí-Bagué, N., Mestre-Bach, G., Lozano-Madrid, M., Fernandez-Aranda, F., Granero, R., Vintró-Alcazaz, C., … Jiménez-Murcia, S. (2018). Trait impulsivity and cognitive domains involving impulsivity and compulsivity as predictors of gambling disorder treatment response. Addictive Behaviors, 87, 169–176. https://doi.org/10.1016/j.addbeh.2018.07.006.
Mansueto, A., Challet-Bouju, G., Hardouin, J.-B., & Grall-Bronnec, M. (2024). Definitions and assessments of recovery from gambling disorder: A scoping review. Journal of Behavioral Addictions, 13(2), 354–412. https://doi.org/10.1556/2006.2024.00008.
Medeiros, G. C., & Grant, J. E. (2018). Gambling disorder and obsessive–compulsive personality disorder: A frequent but understudied comorbidity. Journal of Behavioral Addictions, 7(2), 366–374. https://doi.org/10.1556/2006.7.2018.50.
Mena-Moreno, T., Testa, G., Mestre-Bach, G., Miranda-Olivos, R., Granero, R., Fernández-Aranda, F., … Jiménez-Murcia, S. (2022). Delay discounting in gambling disorder: Implications in treatment outcome. Journal of Clinical Medicine, 11(6), 1611. https://doi.org/10.3390/jcm11061611.
Mestre-Bach, G., & Potenza, M. N. (2023). Features linked to treatment outcomes in behavioral addictions and related disorders. International Journal of Environmental Research and Public Health, 20(4), 2873. https://doi.org/10.3390/ijerph20042873.
Mestre-Bach, G., Potenza, M. N., Granero, R., Uríszar, J. C., Fernández-Aranda, F., & Jiménez-Murcia, S. (2024). Statistical predictors of the co-occurrence between gambling disorder and problematic pornography use. Journal of Psychiatric Research, 178, 125–129. https://doi.org/10.1016/j.jpsychires.2024.07.033.
Mestre-Bach, G., Potenza, M. N., Granero, R., Uríszar, J. C., Tarragón, E., Chiclana Actis, C., … Jiménez-Murcia, S. (2023). Understanding the Co-occurrence of gambling disorder and problematic pornography use: Exploring sociodemographic and clinical factors. Journal of Gambling Studies, 40(3), 1295–1314. https://doi.org/10.1007/s10899-023-10274-3.
Mestre-Bach, G., Steward, T., Granero, R., Fernández-Aranda, F., del Pino-Gutiérrez, A., Mallorquí-Bagué, N., … Jiménez-Murcia, S. (2019). The predictive capacity of DSM-5 symptom severity and impulsivity on response to cognitive-behavioral therapy for gambling disorder: A 2-year longitudinal study. European Psychiatry, 55, 67–73. https://doi.org/10.1016/j.eurpsy.2018.09.002.
Moore, L. H., & Grubbs, J. B. (2021). Gambling disorder and comorbid PTSD: A systematic review of empirical research. Addictive Behaviors, 114, 106713. https://doi.org/10.1016/j.addbeh.2020.106713.
Müller, K. W., Wölfling, K., Dickenhorst, U., Beutel, M. E., Medenwaldt, J., & Koch, A. (2017). Recovery, relapse, or else? Treatment outcomes in gambling disorder from a multicenter follow-up study. European Psychiatry, 43, 28–34. https://doi.org/10.1016/j.eurpsy.2017.01.326.
Pfund, R. A., Peter, S. C., McAfee, N. W., Ginley, M. K., Whelan, J. P., & Meyers, A. W. (2021). Dropout from face-to-face, multi-session psychological treatments for problem and disordered gambling: A systematic review and meta-analysis. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 35(8), 901–913. https://doi.org/10.1037/adb0000710.
Pickering, D., Keen, B., Entwistle, G., & Blaszczynski, A. (2018). Measuring treatment outcomes in gambling disorders: A systematic review. Addiction, 113(3), 411–426. https://doi.org/10.1111/add.13968.
Potenza, M. N., Balodis, I. M., Derevensky, J., Grant, J. E., Petry, N. M., Verdejo-Garcia, A., & Yip, S. W. (2019). Gambling disorder. Nature Reviews Disease Primers, 5(1). https://doi.org/10.1038/s41572-019-0099-7.
Sanders, J., & Williams, R. (2019). The relationship between video gaming, gambling, and problematic levels of video gaming and gambling. Journal of Gambling Studies, 35(2), 559–569. https://doi.org/10.1007/s10899-018-9798-3.
Singer, J. B., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.
Sussman, S., Rozgonjuk, D., & van den Eijnden, R. J. J. M. (2017). Substance and behavioral addictions may share a similar underlying process of dysregulation. Addiction, 112(10), 1717–1718. https://doi.org/10.1111/add.13825.
Szerman, N., Basurte-Villamor, I., Vega, P., Mesías, B., Martínez-Raga, J., Ferre, F., & Arango, C. (2023). Is there such a thing as gambling dual disorder? Preliminary evidence and clinical profiles. European Neuropsychopharmacology, 66, 78–91. https://doi.org/10.1016/j.euroneuro.2022.11.010.
Tang, K. T. Y., Kim, H. S., Hodgins, D. C., McGrath, D. S., & Tavares, H. (2020). Gambling disorder and comorbid behavioral addictions: Demographic, clinical, and personality correlates. Psychiatry Research, 284, 112763. https://doi.org/10.1016/j.psychres.2020.112763.
Theule, J., Hurl, K. E., Cheung, K., Ward, M., & Henrikson, B. (2019). Exploring the relationships between problem gambling and ADHD: A meta-analysis. Journal of Attention Disorders, 23(12), 1427–1437. https://doi.org/10.1177/1087054715626512.
Turner, D., Briken, P., Grubbs, J., Malandain, L., Mestre-Bach, G., Potenza, M. N., & Thibaut, F. (2022). The World Federation of Societies of Biological Psychiatry guidelines on the assessment and pharmacological treatment of compulsive sexual behaviour disorder. Dialogues in Clinical Neuroscience, 24(1), 10–69. https://doi.org/10.1080/19585969.2022.2134739.
Verdejo-García, A., Lozano, Ó., Moya, M., Alcázar, M. Á., & Pérez-García, M. (2010). Psychometric properties of a Spanish version of the UPPS–P impulsive behavior scale: Reliability, validity and association with trait and cognitive impulsivity. Journal of Personality Assessment, 92(1), 70–77. https://doi.org/10.1080/00223890903382369.
Vintró-Alcaraz, C., Munguía, L., Granero, R., Gaspar-Pérez, A., Solé-Morata, N., Sánchez, I., … Fernández-Aranda, F. (2022). Emotion regulation as a transdiagnostic factor in eating disorders and gambling disorder: Treatment outcome implications. Journal of Behavioral Addictions, 11(1), 140–146. https://doi.org/10.1556/2006.2022.00004.
Whiteside, S. P., Lynam, D. R., Miller, J. D., & Reynolds, S. K. (2001). Validation of the UPPS impulsive behavior scale: A four factor model of impulsivity. ProQuest Dissertations and Theses, 574(May 2004), 90–90. https://doi.org/10.1002/per.556.
