Impulse control disorders (ICDs) are a well-known adverse effect of dopamine agonists (DAAs). This critical review aims to summarize data on the prevalence and factors associated with the development of an ICD simultaneous to DAA use. A search of two electronic databases was completed from inception to July 2017. The search terms were medical subject headings (MeSH) terms including “dopamine agonists” AND “disruptive disorders”, “impulse control disorders”, or “conduct disorders”. Articles had to fulfill the following criteria to be included: (i) the target problem was an ICD; (ii) the medication was a dopaminergic drug; and (iii) the article was an original article. Of the potential 584 articles, 90 met the criteria for inclusion. DAAs were used in Parkinson’s disease (PD), restless legs syndrome (RLS) or prolactinoma. The prevalence of ICDs ranged from 2.6 to 34.8% in PD patients, reaching higher rates in specific PD populations; a lower prevalence was found in RLS patients. We found only two studies about prolactinoma. The most robust findings relative to the factors associated with the development of an ICD included the type of DAA, the dosage, male gender, a younger age, a history of psychiatric symptoms, an earlier onset of disease, a longer disease duration, and motor complications in PD. This review suggests that DAA use is associated with an increased risk in the occurrence of an ICD, under the combined influence of various factors. Guidelines to help prevent and to treat ICDs when required do exist, although further studies are required to better identify patients with a predisposition.

The concept of “food addiction” (FA) has aroused much focus because of evidence for similarities between overeating and substance use disorders (SUDs). However, few studies have explored this concept among the broad spectrum of eating disorders (ED), especially in anorexia nervosa (AN). This study aimed to assess FA prevalence in ED female patients and to determine its associated factors. We recruited a total of 195 adult women with EDs from an ED treatment center. The prevalence of FA diagnosis (Yale Food Addiction Scale) in the whole ED sample was 83.6%; AN restrictive type (AN-R), 61.5%; AN binge-eating/purging type (AN-BP), 87.9%; bulimia nervosa (BN), 97.6%; and binge-eating disorder (BED), 93.3%. The most frequently met criteria of FA were “clinically significant impairment or distress in relation to food”, “craving” and “persistent desire or repeated unsuccessful attempts to cut down”. An FA diagnosis was independently associated with three variables: presence of recurrent episodes of binge eating, ED severity, and lower interoceptive awareness. In showing an overlap between ED and FA, this study allows for considering EDs, and AN-R in particular, from an “addictive point of view”, and thus for designing therapeutic management that draws from those proposed for addictive disorders.

Background Individuals who gamble online may be at risk of gambling excessively, but internet gambling also provides a unique opportunity to monitor gambling behavior in real environments which may allow intervention for those who encounter difficulties. Objective The objective of this study was to model the early gambling trajectories of individuals who play online lottery. Methods Anonymized gambling‐related records of the initial 6 months of 1152 clients of the French national lottery who created their internet gambling accounts between September 2015 and February 2016 were analyzed using a two-step approach that combined growth mixture modeling and latent class analysis. The analysis was based upon behavior indicators of gambling activity (money wagered and number of gambling days) and indicators of gambling problems (breadth of involvement and chasing). Profiles were described based upon the probabilities of following the trajectories that were identified for the four indicators, and upon several covariates (age, gender, deposits, type of play, net losses, voluntary self-exclusion, and Playscan classification—a responsible gambling tool that provides each player with a risk assessment: green for low risk, orange for medium risk and red for high risk). Net losses, voluntary self-exclusion, and Playscan classification were used as external verification of problem gambling. Results We identified 5 distinct profiles of online lottery gambling. Classes 1 (56.8%), 2 (14.8%) and 3 (13.9%) were characterized by low to medium gambling activity and low values for markers of problem gambling. They displayed low net losses, did not use the voluntary self-exclusion measure, and were classified predominantly with green Playscan tags (range 90%-98%). Class 4 (9.7%) was characterized by medium to high gambling activity, played a higher breadth of game types (range 1-6), and had zero to few chasing episodes. They had high net losses but were classified with green (66%) or orange (25%) Playscan tags and did not use the voluntary self-exclusion measure. Class 5 (4.8%) was characterized by medium to very high gambling activity, played a higher breadth of game types (range 1-17), and had a high number of chasing episodes (range 0-5). They experienced the highest net losses, the highest proportion of orange (32%) and red (39%) tags within the Playscan classification system and represented the only class in which voluntary self-exclusion was present. Conclusions Classes 1, 2, 3 may be considered to represent recreational gambling. Class 4 had higher gambling activity and higher breadth of involvement and may be representative of players at risk for future gambling problems. Class 5 stood out in terms of much higher gambling activity and breadth of involvement, and the presence of chasing behavior. Individuals in classes 4 and 5 may benefit from early preventive measures.

Aims To estimate whether the use of wagering inducements has a significant impact on the gambling behaviors of on‐line gamblers and describe this temporal relation under naturalistic conditions. Design This longitudinal observational study is part of the second stage of the Screening for Excessive Gambling Behaviors on the Internet (EDEIN) research program. Setting Gambling tracking data from the French national on‐line gambling authority (poker, horse race betting and sports betting) and from the French national lottery operator (lotteries and scratch games). Participants A total of 9306 gamblers who played poker, horse race or sports betting and 5682 gamblers who played lotteries and scratch games completed an on‐line survey. The gender ratio was largely male (between 87.1% and 92.9% for poker, horse race betting and sports betting, and equal to 65.1% for lotteries). Median age ranged from 35 (sports betting) to 53 (horse race betting and lotteries). Measurements The survey used the Problem Gambling Severity Index (PGSI) to determine the status of the gamblers (at‐risk or not). Gambling tracking data included weekly gambling intensity (wagers, deposits), gambling frequency (number of gambling days), proxies of at‐risk gambling behaviors (chasing and breadth of involvement) and use of wagering inducements. Findings The use of wagering inducements was associated with an increase of gambling intensity [β between −0.06 (−0.08; –0.05) and 0.57 (0.54; 0.60)], gambling frequency [β between 0.12 (0.10; 0.18) and 0.29 (0.28; 0.31)] and at‐risk gambling behaviors [odds ratio between 1.32 (1.16; 1.50) and 4.82 (4.61; 5.05)] at the same week of their use. This effect was stronger for at‐risk gambling behaviors and at‐risk gamblers. Conclusions Wagering inducements may represent a risk factor for developing or exacerbating gambling problems.

Background and aims Gambling disorder is characterized by problematic gambling behavior that causes significant problems and distress. This study aimed to develop and validate a predictive model for screening online problem gamblers based on players' account data. Methods Two random samples of French online gamblers in skill-based (poker, horse race betting and sports betting, n = 8,172) and pure chance games (scratch games and lotteries, n = 5,404) answered an online survey and gambling tracking data were retrospectively collected for the participants. The survey included age and gender, gambling habits, and the Problem Gambling Severity Index (PGSI). We used machine learning algorithms to predict the PGSI categories with gambling tracking data. We internally validated the prediction models in a leave-out sample. Results When predicting gambling problems binary based on each PGSI threshold (1 for low-risk gambling, 5 for moderate-risk gambling and 8 for problem gambling), the predictive performances were good for the model for skill-based games (AUROCs from 0.72 to 0.82), but moderate for the model for pure chance games (AUROCs from 0.63 to 0.76, with wide confidence intervals) due to the lower frequency of problem gambling in this sample. When predicting the four PGSI categories altogether, performances were good for identifying extreme categories (non-problem and problem gamblers) but poorer for intermediate categories (low-risk and moderate-risk gamblers), whatever the type of game. Conclusions We developed an algorithm for screening online problem gamblers, excluding online casino gamblers, that could enable the setting of prevention measures for the most vulnerable gamblers.