Abstract
Background
Most individuals self-excluding from gambling have lost control over their gambling behavior. Commercial solicitations are prohibited during the self-exclusion period, but resume immediately afterwards. The self-exclusion system appears insufficient, particularly for short self-exclusions and among the heaviest gamblers. We assessed the impact of extending suspension of commercial solicitations on gambling intensity.
Methods
In this parallel randomized study, we included 2548 French online gamblers who self-excluded for up to 3 months from May–November 2022. They were assigned 1:1 to optimized self-exclusion with extended commercial solicitations ban for 9 months or standard procedure. The primary outcome was the change in total loss over the past 4 weeks at Month 9. We also assessed total deposit, total stakes, compulsivity, number and duration of gaming sessions and subsequent self-exclusions at 6, 9, 12 and 18 months.
Results
Participants were randomized to the optimized group (n = 1265) or standard group (n = 1283). The results didn’t show a significant difference in the reduction in total losses. The optimized group showed significantly reduced 4-week total deposits versus standard group at 6 months (455.68 euros vs. 319.65 euros, p = 0.017), 9 months (451.92 euros vs. 343.21 euros, p = 0.040), and 12 months (484.27 euros vs. 370.02 euros, p = 0.025). Significance was lost at 18 months (492.61 euros vs. 404.73 euros, p = 0.087).
Conclusions
An extended 9-month ban on direct commercial solicitations after self-exclusion significantly reduces gambling deposits during, and 3 months after, the ban. This supports the effectiveness of improving self-exclusion procedure. Future research should explore longer bans and indirect commercial solicitations.
Trial registration: NCT05413564.
Keywords: Problem gambling, Self-exclusion, Commercial solicitations, Behavioral intervention, Responsible gambling
Introduction
Gambling disorder constitutes a major public health challenge, leading to substantial human and societal burdens [1]. With the increase in online gambling offers, gambling disorder is becoming more prevalent, affecting a wider range of people [20, 46]. In most countries, gambling providers must offer moderators to protect users, such as self-exclusion [1]. Recent studies show that self-exclusion is a widely used but limited tool for reducing gambling-related harm. Bijker et al. [9] report low and uneven global usage, whether they are centralized self-exclusion systems covering multiple providers, or those available at the provider’s level However, many breach their exclusion, revealing technical flaws [24]. Miles et al. [38] highlight high psychiatric comorbidity among users, and Tjernberg et al. [45] note that while the service is valued, it lacks follow-up and is easy to circumvent. Together, these findings suggest that self-exclusion should be part of a broader, more integrated support system. Although not all individuals who self-exclude meet clinical criteria for gambling disorder, self-reported motives frequently reveal significant distress, such as loss of control, financial pressure, or a desire to stop gambling altogether [34]. These subjective experiences are supported by behavioral data showing marked increases in gambling intensity prior to self-exclusion [13, 32]. Håkansson and Henzel [24] further demonstrate that self-excluders often present broader psychosocial vulnerabilities, including mental health issues and indebtedness. Despite the clear signal of difficulty expressed through self-exclusion, its actual effectiveness in reducing gambling behaviors is limited and often short-lived [32]. Systematic reviews confirm that while self-exclusion is predominantly used by individuals exhibiting problematic gambling behaviors [9, 39] it remains underutilized and inconsistently implemented across jurisdictions. Moreover, although Auer and Griffiths [8]caution against using self-exclusion as a definitive marker of problem gambling, the fact that individuals choose to exclude themselves for reasons tied to impaired control underscores the urgency of improving the tool’s impact. In particular, the immediate resumption of direct commercial solicitations once the exclusion period ends may significantly undermine the long-term effectiveness of self-exclusion strategies.
Commercial solicitations can be direct or indirect [47]. Direct solicitations are messages sent directly to the gamblers by text messages, emails or phone calls, while indirect solicitations are advertisements on posters, television or online. Indirect solicitations include communications presenting the gambling service provider as a social platform or a source of information, for example through newsletters, sports channels, and other information not directly promoting gambling activity [15] These indirect messages contribute to gamblers' perception of advertising spam and can be seen as an effort to normalize gambling by portraying it as an ordinary activity with no addictive risk, similar to sport [15, 47]. While this conventional, indirect advertising does not necessarily encourage those who do not gamble to do so, promotional offers such as free bets often encourage non-gamblers to take the first plunge [10, 26, 37]. Commercial messages promoting the ease of gambling, the rapidity with which winnings can be payed out and the possibility of gambling with the service providers’ money via bonuses have shown serious addictive risks for recreational gamblers, which are even more elevated for at-risk gamblers [14]. Direct messages via SMS or email trigger gamblers’ behavior, leading to riskier and more impulsive behaviors [26, 28]. The content of these direct solicitations is often wagering inducements, such as bonus bets or money-back guarantees, promoting impulsive gambling behavior [27]. They are designed to give the gambler the impression of taking a “safe” bet, while encouraging further betting [28]. This is particularly problematic for gamblers who have already lost control over gambling [43], as it increases the desire to gamble amongst problem-, moderate-, and low-risk gamblers [26]. Problem gamblers are more responsive to advertising, and direct and indirect advertising messages prompt them to gamble [19, 44]. Self-excluders could therefore be particularly receptive and at risk of returning to problem gambling, triggered by these commercial solicitations.
Few studies have addressed the period following self-exclusion. Gamblers often self-exclude again after a first self-exclusion with, on average, more severe gambling behaviors the second time, indicating a potentially avoidable deterioration in the gambler's condition [32]. Moreover, the dynamics of gambling disorders are marked by relapses after periods of calm [21]. Consequently, direct solicitations after a period of calm could greatly heighten the risk of relapse.
Therefore, self-exclusion can be seen as a self-imposed behavioral strategy used by gamblers to protect themselves. It currently consists exclusively of a behavioral intervention, in the sense that it is preventing behavior [36]. Limiting the ban of commercial solicitations to the period of "forced" cessation of gambling alone through voluntary self-exclusion seems insufficient to effectively prevent relapse once the access to online gambling is restored. Early commercial solicitations immediately following the end of a self-exclusion period may trigger relapse in gamblers whose behavioral changes are recent and primarily supported by a stimulus control intervention. We hypothesized that after an initial short period of self-exclusion, an extended ban on direct commercial solicitations for 9 months would be more effective in reducing gambling intensity than a regular ban of the length of the self-exclusion period.
Methods
We conducted a parallel randomized controlled trial among all gamblers whose account was opened for at least 4 weeks (i.e., with a mandatory confirmed identity and age > 18), who self-excluded for between 3 days and 3 months from May 9th to November 24th 2022 on a French gambling platform (PMU). In France, the regulatory framework prohibits online casino games (e.g., slot machines, roulette), which significantly shapes the national gambling landscape. The PMU (Pari Mutuel Urbain), historically focused on horse-race betting, operates legally in the online market through horse betting, sports betting, and poker. Within this restricted market, PMU holds a dominant position in online horse-race betting, with an estimated 85% market share [5]. In 2024, its gross gaming revenue accounted for approximately 12% of the total French gambling market [6]. This platform provides online gambling services predominantly for horse betting, but also sport betting and poker. When a gambler self-excludes from the PMU site, he is unable to access any offer available on the gambling provider's site during the chosen self-exclusion period. This report follows the CONSORT-SPI guidelines [23].
Participants
Gamblers had a priori consented to the use of their account-based gambling data when opening an account, according to the general terms and conditions of use. There was no exclusion criterion. Self-excluders were randomized 1:1 into the extended ban on direct commercial advertising for 9 months or the regular ban of the length of the self-exclusion period, i.e., the procedure as usual, 3 months maximum. Each participant could only be included once.
Intervention
For the group assigned to the 9-month ban extended ban direct commercial advertising, direct commercial solicitations (SMS, email) are interrupted, in a similar way to the self-exclusion period. During this period, gamblers do not receive any direct commercial solicitations from the gambling website. Under the usual procedure, solicitations resume immediately after the end of the self-exclusion period. Notably, once the self-exclusion is triggered, the duration cannot be altered.
Outcomes and data collection
The primary outcome was the change in total net loss since baseline at Month 9, measured in euros. Secondary outcomes included the change in total net loss since baseline at the other follow-up time points. The last 4-week loss and last 4-week stake were recorded for the total sample and as subgroups according to type of gambling game. Other secondary outcomes were: the last 4-week total deposit, the last 4-week number of episodes of compulsivity (defined by three consecutive deposits in a 12-h period), and the last 4-week number of gambling sessions (all games). A session was considered to represent the gambling behavior itself; where the beginning of a session was counted from when a gambling action occurs after no gambling action for at least 30 min, and the end of the gambling session when a gambling action is followed by no gambling action for 30 min. Finally, we noted the last 4-week total time spent on gambling (sum of all sessions). Values for unused gambling types were imputed as zero. The number of subsequent self-exclusions was collected at 18 months. We extracted routine sociodemographic data and the account-based gambling data at baseline, 6, 9, 12 and 18 months after the beginning of the self-exclusion period. See Fig. 1.
Fig. 1.
Study design
Statistical analysis
The sample size was calculated based on the primary outcome of change in total monetary loss. We hypothesized a mean reduction of €300 in the control group and €400 in the intervention group, with a common standard deviation of €900. Assuming a 1:1 allocation ratio and no loss to follow-up, a total of 2544 participants (1272 per group) would be required to detect this €100 difference with a two-tailed alpha of 0.05 and 80% power.
We conducted a descriptive analysis of the whole population and per group. We compared the change from baseline in all gambling variables between the groups at 6, 9, 12 and 18 months. Participants who closed their accounts during the study period, were imputed 0 for financial variables. The statistical tests used were Pearson's Chi-square test for qualitative variables and Student's t-test for quantitative variables. All tests were 2-sided and performed with Dataiku software.
Ethics
Gamblers were informed of, and consented to, personal and gambling data collection and analysis in the general conditions of use when opening an account on the website. The PMU ensures compliance with data protection regulations by adhering to the General Data Protection Regulation (GDPR) No. 2016/679 of April 27, 2016, and the amended French Data Protection Act of January 6, 1978. These measures guarantee the secure handling and protection of personal data throughout its operations and allow the analysis of the routinely recorded data for public health purposes.
The study obtained local IRB approval from Nimes Hospital n° 25.01.03 1. The approval included the fact that the participants were not aware that they were to one of two conditions, nor gave consent to participate since PMU performed optimized versus standard procedure testing as part of their ordinary operations [29]. It was prospectively registered on clinicaltrials.gov under the identifier NCT05413564.
Results
Population
We included 2548 online gamblers; 85.3% were men, and the median age was 43 years. They were randomly assigned to the intervention (extended ban) group A (n = 1265) or the control (regular ban) group B (n = 1283) (Fig. 2). The median time since account opening was 23 months. The median self-exclusion duration chosen was 31 days. Table 1 presents the baseline characteristics of the total sample and per group.
Fig. 2.
Flowchart
Table 1.
Baseline characteristics of the total sample and by group of randomization
| Total sample (n = 2548) | Extended ban (Group A) (n = 1265) | Regular ban (Group B) (n = 1283) | |
|---|---|---|---|
| Age (years), mean (sd) | 44.9 (14.5) | 45.3 (14.3) | 44.7 (14.7) |
| Sex (male), n (%) | 2173 (85.28%) | 1073 (82.82%) | 1100 (85.74%) |
| Time since account opening (months), mean (sd) | 46.04 (56.22) | 47.2 (57.7) | 44.9 (54.6) |
| Chosen duration of self-exclusion, (days) mean (sd) | 24.9 (13.78) | 24.9 (13.5) | 24.9 (14.02) |
| Types of gambling game used in the last month, n (%) | |||
| Exclusive sports betting | 324 (12.72) | 153 (12.09) | 171 (13.33) |
| Exclusive horse betting | 995 (39.05) | 512 (40.47) | 483 37.65) |
| Exclusive poker | 252 (9.89) | 131 (10.36) | 121 (9.43) |
| Mixed: predominantly sports betting | 187 (7.34) | 87 (6.88) | 100 (7.79) |
| Mixed: predominantly horse betting | 487 (19.11) | 238 (18.81) | 249 (19.41) |
| Mixed: predominantly poker | 230 (9.03) | 117 (9.25) | 113 (8.81) |
| Not gambled in the last month | 73 (2.86) | 27 (2.13) | 46 (3.59) |
| Last 4-week total deposits (€), mean (sd) | 634 (1456.21) | 691.52 (1758.32) | 576.43 (1076.17) |
| Last 4-week total loss (€), mean (sd) | − 410.13 (1131.97) | − 439.84 (1304.56) | − 380.83 (930.58) |
| Last 4-week loss in sport betting, mean (sd) | − 74.07 (487;60) | − 84.12 (621.56) | − 64.16 (302.06) |
| Last 4-week loss in horse betting, mean (sd) | − 245.07 (615.03) | − 238.75 (616.59) | − 251.31 (613.65) |
| Last 4-week loss in poker, mean (sd) | − 90.98 (788.52) | − 116.96 (1000.77) | − 65.37 (638.24) |
| Last 4-week total stakes (€), mean (sd) | 3383.86 (32,364.95) | 3052.00 (17,688.50) | 3711.07 (42,099.70) |
| Last 4-week stakes in sport betting, mean (sd) | 395.49 (3126.00) | 473.85 (4202.63) | 318.22 (1409.96) |
| Last 4-week stakes in horse betting, mean (sd) | 970.03 (2564.37) | 1000.77 (2474.05) | 939.73 (2651.02) |
| Last 4-week stakes in poker, mean (sd) | 2018.34 (32,114.37) | 1577.37 (16,855.11) | 2453.11 (42,053.58) |
| Last 4-week number of gambling sessions | 35.05 (35.71) | 34.72 (34.98) | 33.78 (34.41) |
| Last 4-week duration of sessions (minutes), mean (sd) | 6585.20 (7208.88) | 204.63 (79.52) | 199.16 (82.66) |
| Last 4-week number of compulsive episodes | 6.50 (12.56) | 6.38 (12.43) | 6.67 (12.72) |
Change in gambling behavior
Results didn’t show a significant difference in the reduction of total losses over the last 4 weeks between baseline, 6, 9, 12 and 18 months (Table 2). Nor was there any significant difference in the change in total losses according to the type of game played (Table 2). Although not significant, the reduction in poker and sport betting losses in the extended procedure group was almost twice the one in the standard group. In contrast, the mean reduction for losses in horse betting was very similar between groups. Gamblers who received the extended ban procedure had a significantly greater reduction in the amount of their total deposit on the website over the 4 previous weeks between baseline and 6 months (A: 455.68 euros B: 319.65 euros p = 0.017), 9 months (A: 451.92 euros B: 343.21 euros p = 0.040), and 12 months (A: 484.27euros, B: 370.02 euros, p = 0.025). At 18 months, this change was no longer significant (A: 492.61euros B: 404.73euros p = 0.087) (Table 2). These results were significant only at the whole sample level; no significant differences were noted in the subgroup analyses according to the type of game (Table 2). The change in average session duration reached significance only at 9 months (p = 0.045).
Table 2.
Change in gambling behavior from baseline to 18 months
| Extended ban (Group A) (n = 1265) | Regular ban (Group B) (n = 1283) | p-value | |
|---|---|---|---|
| Number of subsequent self-exclusions at month 18, mean (sd) | 0.20 (0.56) | 0.21 (0.59) | 0.661 |
| Change from baseline in last 4-week total deposits (€), mean (sd) | |||
| At month 6 | 455.68 (1740.61) | 319.65 (1039.04) | 0.017* |
| At month 9 | 451.92 (1586.15) | 343.21 (1021.45) | 0.040* |
| At month 12 | 484.27 (1534.00) | 370.02 (974.12) | 0.025* |
| At month 18 | 492.61 (1500.37) | 404.73(1051.34) | 0.087 |
| Change from baseline in last 4-week total loss, mean (sd) | |||
| At month 6 | − 295.82 (1250.54) | − 223.22 (937.89) | 0.097 |
| At month 9 | − 308.73 (1399.06) | − 250.81 (1020.41) | 0.232 |
| At month 12 | − 301.63(1212.16) | − 254.77(888.06) | 0.269 |
| At month 18 | − 317.22(1356.90) | − 275.29 (927.85) | 0.362 |
| Change from baseline in last 4-week total loss in sport betting, mean (sd) | |||
| At month 6 | − 71.75 (623.94) | − 38.02 (293.75) | 0.080 |
| At month 9 | − 65.81 (442.04) | − 48.95 (324.77) | 0.272 |
| At month 12 | − 66.27 (530.21) | − 53.74 (299.21) | 0.462 |
| At month 18 | − 71.88 (687.24) | − 52.68 (308.86) | 0.368 |
| Change from baseline in last 4-week total loss in horse betting, mean (sd) | |||
| At month 6 | − 132.81 (595.47) | − 137.32 (592.05) | 0.848 |
| At month 9 | − 150.74 (986.59) | − 157.19 (694.35) | 0.849 |
| At month 12 | − 130.87 (552.42) | − 153.04 (562.87) | 0.316 |
| At month 18 | − 146.20 (619.88) | − 171.87 (605.54) | 0.290 |
| Change from baseline in last 4-week total loss in poker, mean (sd) | |||
| At month 6 | − 91.25 (890.55) | − 47.87 (642.80) | 0.158 |
| At month 9 | − 92.18 (894.12) | − 44.68 (647.10) | 0.124 |
| At month 12 | − 104.16 (910.75) | − 48.00 (641.62) | 0.072 |
| At month 18 | − 99.13(904.40) | − 50.72 (627.28) | 0.116 |
| Change from baseline in last 4-week total stakes, mean (sd) | |||
| At month 6 | 2093.49 (17,725.58) | 2846.00 (42,236.33) | 0.559 |
| At month 9 | 2229.06 (17,263.70) | 2786.33 (35,872.97) | 0.618 |
| At month 12 | 2371.46 (17,404.27) | 2580.32 (38,735.06) | 0.861 |
| At month 18 | 2256.03 (17,267.36) | 3070.98 (42,189.90) | 0.525 |
| Change from baseline in last 4-week total stakes on sport betting, mean (sd) | |||
| At month 6 | 410.99 (4206.03) | 217.93 (1371.55) | 0.118 |
| At month 9 | 383.10 (3856.09) | 234.44 (1505.24) | 0.199 |
| At month 12 | 373.40 (3413.96) | 264.09 (1419.83) | 0.290 |
| At month 18 | 372.45 (2906.36) | 269.35 (1414.91) | 0.254 |
| Change from baseline in last 4-week total stakes on horse betting, mean (sd) | |||
| At month 6 | 556.45 (2220.59) | 384.80 (3625.18) | 0.150 |
| At month 9 | 546.89 (2259.86) | 489.28 (2132.00) | 0.508 |
| At month 12 | 570.00 (2120.46) | 512.58 (2392.76) | 0.525 |
| At month 18 | 591.29 (2757.75) | 570.26 (2488.17) | 0.840 |
| Change from baseline in last 4-week total stakes on poker, mean (sd) | |||
| At month 6 | 1126.06 (16,977.64) | 2243.28 (42,077.02) | 0.381 |
| At month 9 | 1299.08 (16,630.79) | 2062.61 (35,808.38) | 0.491 |
| At month 12 | 1428.45 (16,819.69) | 1803.65 (38,651.64) | 0.751 |
| At month 18 | 1292.28 (16,780.75) | 2231.36 (42,114.61) | 0.461 |
| Change from baseline in last 4-week number of gambling sessions | |||
| At month 6 | 19.52 (34.32) | 18.16 (33.73) | 0.311 |
| At month 9 | 19.85. (35.05) | 19.18 (34.03) | 0.626 |
| At month 12 | 21.51 (32.91) | 20.90(34.51) | 0.648 |
| At month 18 | 22.18 (34.84) | 22.45 (33.97) | 0.848 |
| Change from baseline in last 4-week total avg duration of sessions (minutes), mean (sd) | |||
| At month 6 | 98.44(126.68) | 89.42(127.78) | 0.074 |
| At month 9 | 108.83 (125.73) | 98.79 (127.32) | 0.045* |
| At month 12 | 111.84(127.49) | 109.74(122.71) | 0.672 |
| At month 18 | 120.12 (128.36) | 116.78 (125.73) | 0.508 |
| Change from baseline in last 4-week number of compulsive episodes | |||
| At month 6 | 3.99 (11.96) | 3.78 (12.35) | 0.684 |
| At month 9 | 4.09 (11.77) | 4.25 (12.26) | 0.751 |
| At month 12 | 4.21 (11.59) | 4.53 (12.36) | 0.491 |
| At month 18 | 4.44 (12.57) | 4.65 (12.87) | 0.681 |
A greater absolute value means a greater change. Losses are written as negative values. P-values in bold and with asterisks denote significant differences
There was no significant difference between the groups at any timepoint in the last 4-week change in total stakes, number of gambling sessions or in the number of compulsive episodes.
Table 2 presents the change in gambling behavior from baseline to 18 months. Even after the ban on commercial solicitation had been lifted, there was still a €100 greater reduction in deposits in group A than in group B.
Discussion and conclusions
We conducted a large-scale in situ randomized controlled trial to determine whether an extended 9-month ban on direct commercial solicitations after a short self-exclusion influenced the gambling behavior of individuals who self-excluded, measured by changes in gambling intensity after the self-exclusion period. We observed a significant difference in the change in deposit amounts between the two groups at 6, 9, and 12 months, i.e., up to 9 months after the self-exclusion period. Surprisingly, other gambling-related indicators did not reach between group difference. Despite an increasing literature, there still is no consensus regarding the most appropriate outcome measures when using account-based gambling data, and a combination of behavioral markers of harms could be more accurate [18]. Moreover, screening for people at-risk and showing an improvement after an intervention do not necessarily involve the same markers, and little data are available for this second configuration [18]. While total losses are commonly used to assess changes in gambling behavior [22], this metric could not be the more accurate: it is highly skewed and sensitive to the randomness of gambling outcomes. Even people with a gambling disorder can experience winnings and gambling behavior differs after winnings as compared to after losses. In addition, short observation windows may increase imprecision [2, 40, 42]. Compared to losses, deposits may also be less sensitive to boundary effects of the observation period—for example, a large win or loss occurring at the very beginning or end of the window, which can produce a misleading value even though the corresponding stake was placed outside the observed timeframe. In contrast, total deposit amounts may provide a more stable and reliable indicator of harms, as they capture the financial effort devoted to gambling—that is, the money withdrawn from an external bank account and allocated to gambling—and rather than moment-to-moment decision-making under uncertainty that characterizes gambling itself [48]. Deposits can thus more accurately reflect the “cool” decision [30] and valuation process that occurs between gambling and non-gambling activities, since they require an external interaction with the bank account and an explicit willingness to pay to continue gambling. Additional markers of harms related to deposits could also have been of interest but were not available for this study: frequency of deposits, deposits rejected because the bank account had no funds, deposits declined by the payment provider or bank [18]. Deposits may serve as a proxy for financial difficulties. Although such difficulties can be moderated by individual income, deposit behavior has been repeatedly shown to be associated with gambling disorder [18]. Financial difficulties are also been demonstrated to be the main dimension of quality of life leading to a decrement of utility [33] this legitimates the use of account-based financial proxies in settings or designs when a clinical assessment is not possible and a self-assessment would perturbate the ecology of the process to be assessed, as it is the case here.
This study also demonstrates the technical feasibility of implementing an extended commercial ban. It suggests a simple and practical measure to protect individuals with gambling disorders who self-identify and choose to protect themselves through self-exclusion. Commercial solicitations have been identified as triggers for relapses among people with gambling problems [26, 28] and are perceived as unethical from gamblers' perspectives [35]. In response to these concerns, several European countries have implemented regulatory measures to limit gambling advertising. In France, the National Gaming Authority (ANJ) has introduced guidelines to reduce advertising pressure across media channels and encourage responsible practices by influencers and ambassadors, aiming to protect minors and individuals with excessive gambling behaviors [3]. Similarly, Spain has enacted regulations that significantly reduce gambling advertising, bonuses, and sponsorships, impacting new accounts and total money gambled, although active accounts remain largely unaffected [7]. In the United Kingdom, while a voluntary "whistle-to-whistle" ban has reduced televised gambling adverts during live sports broadcasts, research shows that most exposure persists through pitchside hoardings, shirt sponsorships, and incidental branding—areas not subject to regulation—which continues to saturate the viewing environment [41]. These measures reflect a growing recognition of the need to mitigate the risks associated with gambling marketing.
This perception is particularly salient when individuals, having voluntarily self-excluded, acknowledge the need for external constraints to support behavior change. The discrepancy between their efforts and commercial interventions may undermine self-efficacy when self-exclusion was an action to commit into change [25], potentially exacerbating the problem. Self-efficacy construct has not been assessed in this naturalistic study and could interestingly be explored in a future study to understand the underlying psychological processes of self-protection measures from gambling.
Recurrent findings indicate that the public strongly supports limits on gambling advertising [11]. As responsible gambling efforts must balance divergent stakeholder interests [31], the extended ban appears to offer a feasible, effective, and widely acceptable tool for regulatory authorities to promote or even mandate.
Implications for indirect solicitation
This study focused exclusively on direct solicitation for reasons of feasibility. However, the findings align with existing evidence on the role of commercial solicitations in problem gambling. While these results cannot be directly applied to indirect solicitation (e.g., advertising), they prompt reflection on how to better protect vulnerable individuals.
Our study has notable strengths. Its design allowed for the observation of different exclusion durations in a naturalistic setting, without the introduction of additional interventions, assessments or direct researcher contact with participants, thereby minimizing potential sources of bias. Furthermore, all individuals who self-excluded during the observation period were systematically included, reducing the risk of selection bias. These methodological features strengthen the internal validity of the findings and provide a clearer view of how the duration of the ban period—during which individuals are not exposed to commercial solicitations—may influence gambling behavior.
Limitations
This study has several limitations. Our study did not collect self-reported measures of gambling practices, which could have complemented behavioral data and provided insight into subjective experiences of gambling during self-exclusion. Nor were there clinical indicators of gambling-related harm or psychological impact, which previous research has emphasized as essential for assessing the consequences of gambling behavior [12, 16, 17]. The absence of such measures limits the ability to capture the full extent of gambling-related harm, as behavioral data alone may not reflect the subjective severity, diversity, and clinical significance of gambling problems.While self-excluders generally report loss of control [34], we cannot confirm that all participants met the criteria for gambling disorder. About one-third of participants engaged in mixed gambling activities. Future studies could explore whether findings differ in more homogeneous samples. Furthermore, mixed gambling may have influenced the construction of variables “total loss and stakes by type of game”, as values for unused gambling types were imputed as zero. A subgroup analysis according to gambling type could address this issue. Information on the type and amount of direct commercial solicitations received by participants in the control group was unavailable. Calculating theoretical loss considering pay-back percentage would have offered an additional informative outcome measure, but our primary interest was in reflecting the actual financial investment of gamblers, which justified our use of net loss as the reported outcome. We did not have data on repeated self-exclusions during follow-up. Since participants in both randomized groups could self-exclude again at any time, this likely reflects usual gambling behavior rather than a direct effect of the intervention. Nevertheless, the absence of this information limits our ability to assess repeated SEs as an outcome. Our study did not provide information on gambling activity outside the PMU platform, observable effects may have been diluted by continued gambling on other licensed operators or illegal sites. Participants may also have been exposed to solicitations from other regulated or illegal gambling operators, which which were not covered by the self-exclusion applied here, at the provider level. However, the French regulatory authority reports an average of 1.4 active accounts per gambler [4, 5], suggesting this issue likely affected only a minority of participants. This number refers to the overall gambling population rather than specifically to heavy gamblers or self-excluders, and it could plausibly be higher within these subgroups. Another limitation was that self-exclusion periods could vary widely between participants. Even if we only included gamblers who chose short self-exclusion periods, there could be a variation from 3 days to 3 months. Short SE periods (< 3 months) have been associated in the literature with a higher risk of relapse [32], supporting their relevance for study. Methodologically, restricting participants to identical SE durations would have been challenging and could have limited the practical feasibility of the study. The extended ban lasted 9 months, therefore we cannot infer the impact of longer or permanent bans, although the observed pattern suggests efficacy diminishes once the ban ends. Further studies could examine the effects of longer bans. This strategy could be combined with other measures to enhance the impact of self-exclusion on gambling behavior.
Conclusion
This study shows that extending a ban on direct commercial solicitations for 9 months after a self-exclusion significantly reduces gambling deposits after the end of the self-exclusion period, with effects lasting three months beyond the ban. These results support the use of extended bans as a scalable and effective tool to optimize the self-exclusion procedure. Future studies should investigate the impact of longer bans and the role of indirect solicitations to further enhance self-exclusion policies.
Acknowledgements
We are grateful to Anne Clarisse Simonet for regulatory help and Sarah Kabani for editing the manuscript.
Author contributions
CvH, AB, AL. All authors had full access to all study data and take responsibility for the integrity of the data and the accuracy of the data analysis CvH and AL: Conceived and designed the study. CvH wrote the manuscript. AL did the supervision and result interpretation and edited the manuscript. AB contributed to the conception and design of the study and reviewed the manuscript.
Funding
This study was financed by a grant issued via the Pari Mutuel Urbain (PMU) gambling service provider within its obligations to redistribute 0.002% of stakes from its platforms to academic research, as regulated by the French observatory of addictive behaviors (OFDT). Independency of the research with no constraint on the protocol, the analysis and the publication were guaranteed by a strict convention between universities, hospitals and the PMU.
Data availability
Study data will be made available upon reasonable request to the corresponding author.
Declarations
Ethics approval and consent to participate
Gamblers were informed of, and consented to, personal and gambling data collection and analysis in the general conditions of use when opening an account on the website. The PMU ensures compliance with data protection regulations by adhering to the General Data Protection Regulation (GDPR) No. 2016/679 of April 27, 2016, and the amended French Data Protection Act of January 6, 1978. These measures guarantee the secure handling and protection of personal data throughout its operations and allow the analysis of the routinely recorded data for public health purposes. The study obtained local IRB approval of Nimes Hospital n° 25.01.03. The approval included the fact that the participants were not aware that they were randomly assigned to one of two conditions, nor gave consent to participate since PMU implemented the optimized versus standard procedure testing as part of their ordinary operations. The study was prospectively registered on clinicaltrials.gov under the identifier NCT05413564.
Consent for publication
Not applicable.
Competing interests
AL signed a data sharing agreement for the "OSE" study, through an academic-private convention with the FDJ and for a previous study with Winamax. Independency of the research with no constraint on the protocol, the analysis and the publication were guaranteed by a strict convention between the hospital and the FDJ/Winamax, and no funding was part of the conventions. CVH and AB dont have competing interests as defined by BMC.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Study data will be made available upon reasonable request to the corresponding author.