Dose-dependent Effects of Cumulative Mindfulness Practice on Self-Regulation in Gambling Disorder: A Mixed-effects Case-series Analysis

Abstract

Objectives

This case-series study intensively explored the impact of an 8-week mindfulness training program on nine individuals undergoing treatment for behavioral addictions (predominantly gambling disorder). The study aimed to examine the dynamic effects of mindfulness on craving, perceived control, and relapse risk.

Methods

Participants engaged in daily self-assessments via an online diary across five consecutive phases: baseline, 4-week mindfulness training, return to baseline, second 4-week training, and final return to baseline. During return-to-baseline periods, guided sessions were suspended, though trainees could continue meditating independently. A mixed-effects modeling approach, tailored for case-series data, was used to assess the influence of training duration, cumulative meditation practice, and acute session effects.

Results

Analyses revealed improvements across most trainees and outcome measures, with notable variability. Sustained improvements in craving, gambling control, and relapse risk were primarily associated with accumulated mindfulness practice, while acute session effects were modest and limited to current craving.

Conclusions

Although causal relationships cannot be definitively established due to the case-based design, consistent mindfulness engagement emerged as a key within-individual correlate of symptom reduction. These findings highlight the feasibility of linking symptom dynamics to individual training engagement and underscore the value of fine-grained analyses beyond traditional Randomized Controlled Trials (RCTs).

Keywords:

Mindfulness

Behavioral addiction

Gambling disorder

Craving

Emotion regulation

Relapse prevention

Introduction

Due to its extensive negative impact on individuals and their communities, Gambling Disorder (GD) has emerged as a significant public health issue (Abbot, 2020). Global estimates suggest that between 0.12% and 5.8% of the population is affected by GD (Calado & Griffiths, 2016). This prevalence underscores the urgent need for therapeutic interventions tailored to the disorder’s varied clinical presentations (Bodor et al., 2021). Recent conceptual and empirical advances have increasingly pointed to craving as a core psychological process involved in the maintenance and relapse of gambling behavior (Mallorquí-Bagué et al., 2023).

Although major nosologies do not include craving as a formal criterion for GD diagnosis, accumulating evidence identifies it as a critical factor in initiating and sustaining gambling episodes, as well as triggering relapse (Hasin et al., 2013; Mallorquí-Bagué et al., 2023; Rash et al., 2016), and craving intensity and frequency have been proposed to reduce perception of control over gambling (de Lara & Perales, 2020). This shift in focus has spurred further investigation into the processes underlying craving, aiming to refine treatment approaches and improve outcomes (Antons et al., 2020; Romanczuk-Seiferth et al., 2014). Craving in other putative behavioral addictions may differ from gambling-related craving (Cervigón-Carrasco et al., 2024); however, evidence indicates that craving is also a key driver of compulsive behavior in other areas, including disordered video gaming (the only other behavioral addiction officially recognized in any major diagnostic classifications; Antons et al., 2023; World Health Organization [WHO], 2019).

Craving is characterized by a strong urge to engage in substance use or other compulsive activities (Skinner & Aubin, 2010). The underlying components of this state remain a subject of debate, and evidence suggests that it encompasses both aversive and reward-driven elements. On the one hand, craving is often experienced as akin to anxiety, frustration, or restlessness, resembling withdrawal symptoms, and driving addictive behaviors through negative reinforcement (Baker et al., 2004; Hogarth, 2020). Unlike acute withdrawal, however, craving can persist over time and reemerge in response to stress, physiological changes, or environmental cues (Koob & Volkow, 2010; Vafaie & Kober, 2022).

On the other hand, both the Incentive-Sensitization Theory (IST; Robinson & Berridge, 2001) and the Elaborated Intrusion Theory (EIT; Kavanagh et al., 2005) conceptualize craving as reward-related. The IST proposes that repeated exposure to reward-related cues enhances their motivational significance, turning them into “motivational magnets”, and triggering anticipatory reward processing and approach behaviors. For the EIT, craving is boosted by intrusive thoughts and vivid mental imagery of reward consummation.

Despite these diverging conceptualizations, appetitive and aversive components of craving are not mutually exclusive; rather, they may represent different ingredients of a multifaceted construct. Research suggests that how craving is experienced may be context- and activity-dependent. In the case of gambling disorder, some studies describe craving as primarily aversive (Mallorquí-Bagué et al., 2023), while others suggest that its valence is influenced by factors such as the preferred gambling modality or the severity of the disorder (Muela, Ventura-Lucena et al., 2023). Previous research has yielded differentiated evidence regarding the affective correlates of craving. On the one hand, it has been found that gambling craving was associated with both positive and negative emotion-related constructs (Muela, Ventura-Lucena, et al., 2023). On the other hand, craving for video gaming, as well as gambling, showed consistent associations with dysregulation of positive affect (Rivero et al., 2023, 2025). Moreover, the interplay between these emotional components can fluctuate within an individual over time or across different behavioral situations (Sayette, 2016; Wilson, 2022).

Regardless of the perspective adopted, craving control is linked to emotion regulation, as craving is an inherently affective state (Giuliani & Berkman, 2015). Recent models identify two primary forms of emotion regulation: explicit (intentional) and implicit (incidental; Etkin et al., 2015). Explicit regulation involves conscious effort, active monitoring, and deliberate strategies to manage emotions, whereas implicit regulation occurs automatically through associative processes, likely before an individual becomes fully aware of the emotional experience. Consequently, difficulties in either form of emotion regulation, or reliance on less adaptive regulation strategies, may heighten sensitivity to emotional states (López-Guerrero et al., 2023; Navas et al., 2016, 2017; Williams et al., 2012). This, in turn, can amplify the experience of craving, weaken self-control over potentially problematic behaviors, and increase vulnerability to maladaptive responses. Moreover, in terms of treatment, gamblers with deficits in emotion regulation mechanisms appear to be more refractory to treatment (Navas et al., 2019). In this context, mindfulness-based training may represent a promising alternative to enhance these mechanisms (for a review, see López-Guerrero et al., 2025).

Mindfulness is a meditation practice aimed at cultivating awareness of the mind's tendency to shift between thoughts and emotions (Goleman & Davidson, 2017). It involves a series of techniques designed to sustain attention on the present moment and observe both external and internal stimuli without comparing, evaluating, or reflecting on them (Kabat-Zinn, 2003; Siegel et al., 2009). In the context of addiction, various mindfulness-based interventions have been developed, and Randomized Controlled Trials (RCTs) have shown positive results across different addictive behaviors (Demina et al., 2023; Maynard et al., 2018; O’Neill, 2017; Sancho et al., 2018; Tapper, 2018). Unfortunately, the likely presence of publication bias, the frequent lack of adequate control groups, and other suboptimal research practices may have inflated reported effect sizes, making it difficult to isolate the active components of mindfulness interventions and to elucidate the mechanisms that account for or moderate their efficacy (Demina et al., 2023; López-Guerrero et al., 2025).

Mindfulness is increasingly recognized as a potentially effective approach for attenuating craving through its influence on emotion regulation processes. By fostering non-judgmental and non-resistant awareness of craving-related experiences, mindfulness enables individuals to observe their urges without succumbing to their motivational pull (Cásedas et al., 2024; Priddy et al., 2018; Rosenthal et al., 2021; Tapper, 2018; Witkiewitz et al., 2012).

This effect may primarily operate through incidental emotion regulation mechanisms. As Hölzel et al. (2011) suggest, mindfulness can enable the controlled exposure to incentive-laden cues that elicit craving within a therapeutically safe context. Such exposure may promote extinction or counterconditioning of maladaptive responses. Alternatively, mindfulness may engage intentional emotion regulation strategies that do not require the elaboration of thoughts and may reduce cognitive load, relative to other emotion regulation strategies (i.e., cognitive reappraisal; May et al., 2015).

Most studies conducted in this field have primarily focused on substance addictions, with relatively few addressing behavioral addictions (Demina et al., 2023; Maynard et al., 2018; Sancho et al., 2018; Tapper, 2018). Moreover, the existing studies often present several limitations due to factors such as the heterogeneity of mindfulness interventions, the scarcity of appropriate behavioral measures, and the challenges associated with developing a suitable control group (for further details, see Davidson & Kaszniak, 2015; Demina et al., 2023). Randomized Controlled Trials (RCTs) and meta-analytic reviews have predominantly assessed between-group and pre–post differences at the aggregate level (Demina et al., 2023; Maynard et al., 2018; Sancho et al., 2018; Tapper, 2018), thereby emphasizing the chronic, trait-level outcomes of mindfulness-based interventions. Although these designs have significantly advanced the field, they provide limited insight into the temporal dynamics and acute mechanisms by which mindfulness modulates craving and self-regulatory processes.

Recent empirical efforts have begun to address these short-term mechanisms. Ruscio et al. (2016) demonstrated that a brief mindfulness exercise produced immediate reductions in nicotine craving, evidencing an acute, state-level modulation of craving. In a complementary neuroimaging study, Zheng et al. (2024) reported decreased cue-induced craving during the late phase of smoking-cue exposure when participants applied mindfulness-based regulation. Moreover, Garland et al. (2023), in a pilot Mindfulness-Oriented Recovery Enhancement (MORE) study incorporating Just-In-Time Mindfulness prompts triggered by wearable sensors (MORE + JITAI), found evidence of both acute and cumulative effects: mindfulness sessions were associated with immediate decreases in opioid craving, pain, and stress, as well as a progressive reduction in craving over time.

Taken together, these emerging findings suggest that mindfulness may exert both immediate and cumulative effects on craving, reinforcing the importance of examining not only its efficacy, but also the temporal dynamics and mechanisms by which such effects occur. However, to the best of our knowledge, only the aforementioned study has systematically explored these mechanisms (Garland et al., 2023), and there is no comparable evidence in the context of behavioral addictions. This gap underscores the need for designs capable of capturing both acute and cumulative effects, which motivates the present study.

Here, we thus aim to investigate the dynamic effects of a mindfulness-based training program on craving, as well as on the ensuing relapse risk and perceived control, in individuals with lived GD experience in rehabilitation. Since the association where the training was conducted also occasionally provides treatment, counseling and legal support for individuals with other suspected behavioral addictions, our sample included one person diagnosed with gaming disorder and another whose primary problematic activity was excessive stock trading. Given the similarities between gambling and trading, the latter will be considered here as a person with lived experience of gambling problems.

We hypothesize that mindfulness practice will have both acute (immediate) and cumulative (long-term) effects on these variables. More specifically, we expect each mindfulness session to produce an acute reduction in craving and relapse risk, as well as an acute increase in perceived control immediately after the session. However, in the absence of sustained practice, these acute effects may fade over time, leading to a partial rebound in craving and relapse risk before the next session.

As trainees progress through the program, we anticipate that this rebound effect will gradually diminish, resulting in a cumulative reduction in craving and relapse risk and a more stable increase in perceived control. This would indicate that the immediate effects of each session do not completely dissipate but instead contribute to a long-term improvement in these variables. In terms of craving, this means that fluctuations between sessions will become less pronounced over time, leading to a more sustained reduction in craving levels. Similarly, we expect a progressive decrease in relapse risk. Regarding perceived control, we anticipate that the initial session-by-session improvements will consolidate into a more stable and lasting enhancement in self-regulation.

The key strength of our approach is the intensive and individualized assessment conducted throughout the intervention. Single-case studies customarily analyze individual symptom dynamics, as therapy or training progresses, and try to establish links between intervention milestones and relevant changes in clinical status. Case-series studies are frequently just a juxtaposition of case studies. Here, however, we use mixed-effects modeling to capitalize on the availability of several of these cases, to consider each trainee as a grouping unit to link individual observations, and assuming that each of them was already immersed in a positive or negative trajectory independently of mindfulness training. Once baseline differences and individual underlying trajectories are discounted, we can test whether within-individual improvements can be traced to training, and, more specifically, to accumulated meditation practice and proximity of a meditation session, regardless of time in treatment.

Case-series studies, such as the one carried out here, provide a necessary complement to RCTs, particularly when exploring complex or emerging phenomena, like the ones of interest here. They allow us to observe within-individual patterns, providing nuanced insights into variability and treatment response. Unlike RCTs, which often require strict protocols and large samples, case-series designs are more flexible and can be conducted in naturalistic settings, making them especially useful for studying real-world intervention practice. They also enable the rapid generation of hypotheses and can highlight potential mechanisms or therapeutic effects that might be missed in more rigid experimental designs.

Methods

Participants

Trainees were recruited from local associations providing rehabilitation services for individuals with gambling problems. The series included nine individuals who had been independently diagnosed prior to receiving any treatment in the collaborating associations (7 with gambling disorder, 1 with gaming disorder, and 1 for whom the main problematic activity was stock trading), and who were currently abstinent, regardless of whether they still presented symptoms or not. All trainees provided informed consent prior to participation, and the study was approved by the ethics committee of the core team’s affiliation institution. To be eligible for the study, prospective trainees were required to be at least 18 years old and to exhibit moderate to high craving levels as measured by a pre-evaluation using a questionnaire based on the five craving items from the brief version of the Granada Assessment for Cross-Domain Compulsivity (GRACC-18; Muela, Navas, et al., 2023). Moderate to high craving was defined as a mean score of 2 or higher across the five GRACC items, where a score of 5 represents the maximum level of craving. Additionally, trainees needed to show sufficient craving variability, defined as a standard deviation of at least 1 point across daily craving assessments. All trainees had been in treatment and abstinent for a minimum of one month prior to enrolment.

Exclusion criteria were applied retrospectively, based on data collected during the study. Trainees were excluded from analysis (but not from the program) if: (1) their initial craving level was below the threshold of moderate craving (i.e., a score lower than 2); (2) their craving variability was too low, defined as a standard deviation lower than 1 point; (3) they failed to complete at least 50% of the intervention; or (4) they failed to engage in daily assessments, which would make it impossible to analyze their session-by-session progress.

Procedure

Two weeks before the program began, prospective trainees were gathered in person to receive information about the study’s purpose, program structure, ethical considerations, and guidelines (e.g., attendance, completion of a daily assessment starting that day). This daily assessment was used for pre-program tracking and continued for two weeks after the last session. Afterward, trainees provided informed consent and completed an assessment protocol. In the present study, the pre-post measures from this protocol were only reported descriptively in the case characterization and were not included in the statistical analyses. Following the two-week baseline, the intervention consisted of four weekly sessions, after which trainees completed two weeks of intermediate baseline assessments without intervention. This was followed by another four weekly intervention sessions and, finally, two weeks of post-intervention assessments.

Intervention

The intervention was an eight-week mindfulness-based program adapted for individuals with behavioral addiction problems, aimed at reducing craving and preventing relapse. The program integrated cognitive-behavioral strategies for craving regulation with mindfulness practices, drawing on principles from Mindfulness-Based Relapse Prevention and Mindfulness-Based Stress Reduction (MBRP; Bowen et al., 2021; MBSR; Kabat-Zinn, 2003). Each two-hour session was delivered in person by the first author, a psychologist with a master’s degree in addiction treatment and specific mindfulness training.

The program structure was as follows:

Sessions 1–2. Foundations of mindfulness and awareness of triggers. Trainees explored habitual behavior patterns (“autopilot”) and learned to identify triggers and cravings. Practices included mindful eating, body scan, and “surfing the urge” exercises.

Sessions 3–4. Emotional and cognitive regulation. Focus on present-moment awareness, emotional recognition, the “three-minute pause,” and mindfulness in challenging situations, including walking meditation.

Sessions 5–6. Acceptance and observing thoughts. Cultivation of acceptance and non-judgment toward all internal and external experiences, reduction of experiential avoidance, and training individuals to recognize thoughts as mental events rather than objective facts.

Sessions 7–8. Self-care, compassion, and social relationships. Development of balanced lifestyle routines, Metta meditation for self-compassion and compassion toward others, assertive communication, and planning for continued mindfulness practice.

Materials included meditation cushions and mats, session slides, handouts detailing home practice exercises, and audio recordings for guided mindfulness practice at home.

Participants were encouraged to engage in daily mindfulness exercises and to track their practice throughout the program.

Measures

Pre-post measures

Severity of gambling-related problems. The Spanish version of the Diagnostic Questionnaire for Gambling Disorder (GD-9; Jiménez-Murcia et al., 2009), adapted from the previous DSM-IV-TR version was used to assess the nine DSM-5 criteria for gambling disorder (Jiménez-Murcia et al., 2019). The questionnaire comprises 17 yes-or-no items (e.g., “Have you frequently thought about ways of getting money with which to gamble?”). For criteria assessed with two items, a criterion was coded as positive if the trainee responded affirmatively to at least one item. Meeting four or more criteria constitutes the diagnostic threshold for GD. In the validated Spanish version of the DSM-IV measure, internal consistency was also satisfactory, with Cronbach’s alpha values of 0.84 in the control sample, 0.80 in the patient sample, and 0.95 in the combined sample. This measure was used with the seven trainees with lived GD experience and the one with problematic stock trading.

Severity of video gaming-related problems. A specifically adapted Spanish version of the Internet Gaming Disorder Scale (IGD-9; Beranuy et al., 2020) was used to maximize comparability with the GD-9 while maintaining reliability. This adaptation uses nine yes-or-no items, each corresponding to a DSM-5-TR criterion for Internet Gaming Disorder (American Psychiatric Association [APA], 2022). The diagnostic threshold was set at five or more criteria. The internal consistency in the adapted Spanish version of the IGD-9 was satisfactory, with Cronbach’s alpha = 0.85 and Omega = 0.85 (95% CI [0.83, 0.87]), indicating solid reliability of the scale. This measure was used with the only trainee presenting disordered video gaming.

Emotion regulation strategies. Cognitive reappraisal (six items; e.g., “When I want to feel more positive emotion, I change what I’m thinking about”) and expressive suppression (four items; e.g., “When I am feeling negative emotions, I make sure not to express them”) were assessed using the Spanish version of the Emotion Regulation Questionnaire (ERQ; Cabello et al., 2013). Items are rated on a 7-point scale from 1 = totally disagree to 7 = totally agree. In the Spanish validation of the scale, Cronbach’s α coefficients were α = 0.75 for Suppression and α = 0.79 for Reappraisal, indicating satisfactory internal consistency.

Mindfulness. The Spanish version of the Five Facet Mindfulness Questionnaire (FFMQ; Cebolla et al., 2012) was used to assess dispositional mindfulness. The FFMQ conceptualizes mindfulness as a multidimensional construct comprising observing (attending to internal and external experiences), describing (labeling internal experiences with words), acting with awareness (focusing on present activities), non-judging of inner experience (adopting a non-evaluative stance toward thoughts and feelings), and non-reactivity to inner experience (allowing thoughts and feelings to come and go without being carried away). The Spanish adaptation of the FFMQ showed adequate reliability across its five factors. Specifically, Cronbach’s α reached 0.81 for observing, 0.91 for describing, 0.89 for acting with awareness, 0.91 for non-judging, and 0.80 for non-reacting. The overall scale demonstrated good internal consistency, with an α of 0.88 for the total score.

Impulsivity. The Spanish version of the brief UPPS-P (Cándido et al., 2012) was used to evaluate five facets of impulsivity: negative urgency, positive urgency, lack of premeditation, lack of perseverance, and sensation seeking. Within this study, urgency traits were the sole measures of interest. The internal consistency coefficients (Cronbach’s α) of the Spanish validated version were 0.68 for negative urgency and 0.61 for positive urgency.

Cross-domain compulsivity. The brief Granada Assessment for Cross-Domain Compulsivity (GRACC18; Muela, Navas, et al., 2023) consists of 18 items on a 5-point Likert scale (1 = totally disagree to 5 = totally agree), with versions tailored to either gambling or video gaming. The internal consistency reported in the validation was in the excellent range (α = 0.98). Given that some trainees were asymptomatic (in terms of DSM-5 criteria) at the moment of entering the mindfulness training program, this measure was used to assess remaining signs of compulsivity regarding the problematic activity.

Daily measures during the intervention

Trainees were asked to report these measures everyday, at any time or location that was convenient for them.

Autonomous meditation. Single autonomous meditation sessions were recorded with the question: “When was the last time you meditated on your own?” with response options ranging from “Today” (score 0) to “Five or more days ago”, including “I have not meditated yet.” (score 6). Session duration was assessed with the question: “How long did you meditate the last time on your own?” with response options: “0–15 minutes”, “15–30 minutes”, “30–45 minutes”, or “more than 45 minutes”. For guided sessions, occurrence and duration were coded from attendance records. These responses were carefully explored to identify the exact dates when the trainee meditated, including those in which they forgot to fill the daily entry.

Momentary craving. Participants reported their current desire to engage in gambling (or video gaming) using a single item: “Please indicate the desire you currently feel to engage in gambling [video gaming] on a scale from 1 (no desire at all) to 9 (a desire so intense it is practically irresistible)”. Craving was defined broadly to include any image, thought, emotion, dream, or urge that in some way generates desire and/or anxiety to gamble or game, even if those feelings do not result in actual behavior.

Daily craving. The question was very similar to the previous one, but referred to craving throughout the day: “Next, please indicate the desire you have felt to engage in gambling [video gaming] throughout the day”. They rated this desire using the same 9-point scale described above (1 = no desire at all; 9 = a desire so intense it is practically irresistible).

Perceived risk of relapse. Participants were asked the following question: “Next, please rate, based on how you feel right now, the likelihood of experiencing gambling [video gaming] problems again in your life”. They responded using a 5-point scale (1 = no chance at all; 5 = extremely likely).

Perceived control over high-risk situations. Perceived control was assessed by asking trainees: “Finally, please indicate, on a scale from 1 to 5, how much control you currently feel over situations that might trigger your desire to gamble [video game]”. Responses ranged from 1 (no control at all) to 5 (complete control).

Statistical analysis and results

Case characterization

The trainees listed here (IDs: 29429, 44837, 77465, 27116, 20086, 26992, 35667, 11358, and 33557) are those who met the inclusion/exclusion criteria described above. Relevant baseline and post-intervention measures are presented in Table 1 (for access to the full dataset, see the Data Availability section).

Table 1
Case descriptives (Pre/Post).
Trainees	Severity	Compulsivity	Negative Urgency	Positive Urgency	Reappraisal	Suppression	Observe	Describe	Awareness	Non- judging	Non- reactivity
29429	7/8	1.27/1.27	4/3	3.25/3	4.5/4.67	4.5/5.25	2.62/3.75	3.12/3.75	3.12/3.12	3.5/3	2.57/3.14
44837	9/2	4.44/2.16	3.75/3	3.5/2.75	3.66/3.16	6.5/5.5	2.12/2.75	1.75/3.37	2.62/4.12	2.62/2.62	2.14/3.28
77465	7/2	4.05/1.22	3.25/2.75	3.5/2.25	4.5/7	7/1.25	3.87/5	3/4.87	1.75/4.87	1.37/4.75	3.57/4
27116	7/6	1.77/1.38	4/4	1.75/2.75	6.5/5.83	5.5/6	3.75/3.75	4.75/4.37	3.12/3.62	1.87/2.12	3.43/3.57
20086	0/0	1.27/1.05	1.51/1.5	2.75/2	4/4.66	4/2.75	1.75/2.75	3.37/3.87	1.87/3.87	2.75/4.25	2.71//3.28
26992	8/8	3.5/3.6	1.25/2.5	3.75/3.25	3.66/3.17	4.75/2.75	2.87/2.75	4/3.25	4.12/3.37	2.87/2.5	2.85/3
35667	9/0	1.56/1	2.75/2.75	2.5/2.5	4.33/3.83	3.75/3.5	3.37/4	3.25/3.37	4/3.62	3.25/3.25	2.85/3.42
11358	0/0	1.78/1	2.75/2.25	2.5/1.75	3.5/4.67	3.5/4	3.5/3.25	3.37/3.12	3.62/4.12	3.12/3.75	3/3.28
33557	5/-	1.78/-	3.75/-	3.25/-	4/-	5.25/-	3/-	2.62/-	2.75/-	2.12/-	3/-
Means	5.78/3.25	2.33/1.59	3/2.71	2.97/2.53	4.3/4.62	4.97/3.81	2.98/3.5	3.25/3.75	3/3.78	2.61/3.28	2.9/3.37
Note: Severity was measured as the number of positive DSM-5 criteria in GD/IGD scales; Compulsivity was measured with the GRACC-18 scale; Positive and Negative Urgency were measured with the UPPS-P scale; Reappraisal and Suppression were measured with the ERQ questionnaire; Observe, Describe, Awareness, Non-judging, and Non-reactivity stand for dimensions of the FFMQ. Post measures for trainee 33557 were not available. See Measurements section for details.

Except for gender (all trainees are male), the trainees exhibited considerable heterogeneity in their personal characteristics and circumstances. To ensure strict confidentiality and to prevent any potential linkage between individual descriptions and behavioral measures, the cases are described below in random order and without reference numbers.

32-year-old male who completed secondary education. Both parents completed secondary education. The monthly household income exceeds €2,500. His preferred gambling activities are betting, poker, and roulette. He entered treatment two years ago, relapsed several times, and resumed treatment. He has now been in treatment for 11 months and in abstinence for 11 months.

48-year-old male with incomplete secondary education. Both parents completed compulsory education. The monthly household income is moderate, ranging from €1,500 to €2,000. His preferred gambling activities are slot machines and state betting. He has been in abstinence and in treatment for 11 months.

25-year-old male who completed compulsory education. Neither parent had formal education. The monthly household income exceeds €2,500. His preferred form of gambling is roulette. He has been two years in treatment with several relapses and is currently 4 months in abstinence.

69-year-old male who completed university studies. Both parents completed compulsory education. The monthly household income ranges between €2,000 and €2,500. His preferred form of gambling is slot machines.

28-year-old male who completed compulsory education. His father had no formal education, while his mother completed university studies. The monthly household income is moderate, ranging from €1,000 to €1,500. His preferred gambling activities are sports betting and casino games. He relapsed halfway through treatment, presents comorbidity with cocaine addiction, had one month in abstinence at the beginning of treatment, and has been in the treatment program for approximately six months.

48-year-old male with incomplete university studies. Both parents completed university studies. The monthly household income exceeds €2,500. His preferred form of gambling is online sports betting. He had been 3 months in treatment and abstinent when starting the intervention.

53-year-old male who completed university studies. Neither parent completed compulsory education. The monthly household income exceeds €2,500. His preferred gambling activities are video slots, video poker, and slot machines. He has a brain tumor and has been ten months in treatment and abstinent.

29-year-old male who completed university studies. Both parents completed compulsory education. The monthly household income exceeds €2,500. His preferred activity is video gaming, specifically Genzlin Impact and League of Legends. He has been six months in treatment and six months without gaming.

30-year-old male with incomplete university studies. Both parents completed secondary education. The monthly household income exceeds €2,500. His preferred activity is stock market trading. He has been one month in treatment and abstinent, with a relapse occurring two weeks before completing treatment.

Figures 1–4 present all available data points for the outcome measures (current craving, daily craving, perceived control over gambling, and perceived relapse risk) for the nine trainees across program days. The total number of days varies slightly due to feasibility factors, such as holidays or the availability of the facilities where training sessions were held. For additional details, please refer to the Methods section.

As shown by the trendlines added to each individual graph, most trainees exhibited a general trend towards reduced craving, increased perceived control, and lower perceived relapse risk over the course of the program. However, this trend was not uniform, and the slopes varied considerably across individuals. Cases 35667 and 33557, though reporting moderate craving scores during the pre-assessment, consistently reported almost negligible craving levels throughout the program. Notably, case 35667 presented inconsistent data: despite reporting very low levels of craving and relapse risk, their perceived control scores remained near the minimum (see Fig. 4). Given this incongruity, his data were excluded from further analyses. Finally, case 33557 abandoned the program halfway through. As he had completed a sufficient number of sessions, this trainee was retained in the analyses, although no post-treatment measures are available for him.

As shown in Table 1, in general, pre-post changes are in accordance with daily score dynamics. Most trainees showed an improvement in the majority of clinically relevant indices. Interestingly, the compulsivity scale seemed more sensitive to improvements than the DSM-5 criteria-based scales, i.e., out of the 8 trainees with pre and post measures, 4 of them showed a reduction in DSM symptoms, whereas 6 showed a reduction in their compulsivity scores.

Dissociation of acute and accumulated effects of meditation from time in the program

This analysis is designed to complement the individual case specificities depicted in the previous section. Although there is no fixed criterion, it is customarily recommended for mixed-model analyses to have a minimum of 5 groupings levels, that is, five random intercepts and slopes, and a large number of observations per level. In the present case, trainee’s identity number was the grouping factor, with 8 analyzable levels being, in principle, sufficient for this type of modeling, and a very large number of observations per level (as daily entries were collected during the whole assessment and training period).

As previously noted, the primary aim of this study was to disentangle (a) the dose-dependent accumulated effect of mindfulness practice and (b) the acute effect of individual mindfulness sessions from the mere passage-of-time effect. Time in the program (day number) is conceptualized to capture the individual-intrinsic underlying craving dynamics, including, for instance, spontaneous recovery or the impact of treatment-as-usual. Any differences in outcomes that were present before training started are assumed to be captured by the individual random intercepts. Target exposures, in turn, capture the added impact of individual mindfulness engagement.

To this end, we fitted random-effects models to the outcome variables using two key exposures: the number of mindfulness sessions completed up to each measurement point (accumulated mindfulness practice) and the time elapsed since the most recent meditation session—whether autonomous or guided—categorized from 0 (same day) to 6 (six or more days ago). These variables were included as fixed slopes, representing any systematic within-participant effect of the theoretically relevant exposures.

To account for individual variability in their underlying time-dependent trajectories, we incorporated a random slope for day number, and trainee ID code as a random intercept. This specification controlled for within-subject dependencies and mitigated the risk of pseudoreplication. Additionally, following established recommendations and to avoid inflating random effects, time (day number) also entered the model as a fixed-effect covariate. To enable model convergence, both day number and accumulated mindfulness sessions were zero-centered and scaled using the overall sample mean and standard deviation. Because the transformations were linear, inter-individual differences in the original measurements were preserved.

The model was applied to all outcome variables except for daily craving. This exception was due to the phrasing of the daily craving question (“Next, I would like you to indicate the desire to engage in gambling or video gaming that you have experienced throughout the day”), which referred to the entire day on which the entry was made. This allowed for the possibility that craving episodes occurred before meditation on days when trainees reported having meditated. For instance, a trainee may indicate having meditated on a given day (i.e., responded “0” to the corresponding question), but only after experiencing craving earlier in the day, thereby obscuring any acute effects of the session. In contrast, the current craving score reflects the individual’s state at the precise moment of daily entry and, by definition, is always reported after meditation, if any occurred that day.

Accordingly, the mixed-effects model was initially fitted to current craving scores using the lmer function from the lme4 package in R version 3.4.3. (Bates et al., 2015; R Core Team, 2017). The model formula was: current craving ~ days since last meditation + accumulated meditation + day number + (day number | case), with Restricted Maximum Likelihood (REML = TRUE) and default settings. Best-fitting parameter estimates for this model appear in the left panel of Table 2.

Table 2
Best fitting parameters and estimates for the saturated models for craving, relapse risk, and perceived control scores.
	Current craving			Relapse risk			Perceived control
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
Intercept	1.78*	1.08–2.48	< 0.001	1.92*	1.56–2.29	< 0.001	3.96*	3.31–4.62	< 0.001
Time since last meditation	0.08*	0.05–0.12	< 0.001	0.01	-0.02–0.03	0.543	0.02	-0.01–0.05	0.108
Accumulated meditation	-0.53*	-0.96 - -0.10	0.015	-0.62*	-0.92 - -0.32	< 0.001	0.70*	0.27–1.13	0.002
Day number	0.34	-0.17–0.85	0.190	0.44*	0.10–0.78	0.011	-0.14	-0.75–0.47	0.650
Random Effects
σ²	0.55			0.21			0.31
τ₀₀	0.99 _ID			0.26 _ID			0.87 _ID
τ₁₁	0.20 _{ID.Day number}			0.07 _{ID.Day number}			0.42 _{ID.Day number}
ρ₀₁	-0.40 _ID			0.06 _ID			0.28 _ID
ICC	0.69			0.61			0.81
N	8 _ID			8 _ID			8 _ID
Observations	491			464			464
Marginal R² / Conditional R²	0.104 / 0.725			0.210 / 0.692			0.206 / 0.846
Note: CI = Confidence Interval; estimates with p values smaller than 0.05 are marked with an asterisk. Please see text for a discussion of limitations regarding the interpretation of statistical significance in the present analyses.

The analysis included 491 observations from the 8 analyzable trainees. When the model was fitted using data from all 9 trainees (i.e., including case 35667), results remained nearly identical, but the model failed to converge.

For current craving scores, both accumulated meditation and time elapsed since the last session exhibited statistically significant effects after controling for day number. However, given the non-random, purposively selected case series, these parameters estimates should not be interpreted as population-level effect estimates, and interpreting statistical significance becomes challenging. Nevertheless, the central objective here was to determine whether variability in outcome variables was better explained by theoretically meaningful exposures rather than merely by time. To test this, each exposure—accumulated meditation and time since last session—was independently removed from the saturated model, and nested model comparisons were conducted using Maximum Likelihood (ML) estimation. Results showed that excluding time since last meditation substantially reduced model fit (AIC = 1184.6 vs. 1167.2 for the reduced and full models, respectively), as did removing accumulated meditation (AIC = 1184.6 vs. 1170.0). Thus, both predictors significantly contributed to explaining variability in current craving. Predicted craving values from the saturated model are illustrated in the left panel of Fig. 5.

Parallel analyses were conducted for perceived relapse risk (see middle panels in Table 2 and Fig. 5), with the difference that only 464 observations were fitted. This difference was due to the fact that perceived control and relapse risk measures were included a bit later in the first baseline phase for some trainees (as shown in some panels in Figs. 3 and 4).

In this case, removing the acute mindfulness effect slightly improved model fit (AIC = 657.54 vs. 659.09), whereas excluding accumulated meditation worsened it (AIC = 670.53 vs. 659.09). This suggests that perceived relapse risk declined with increased accumulated meditation, while acute effects were negligible.

Similar analyses for perceived control over gambling behavior (see Table 2 and right panel of Fig. 5) revealed comparable patterns. Model fit was virtually unchanged with or without the acute effect (AIC = 852.85 and 852.45), whereas the model including accumulated meditation showed better fit than the one excluding it (AIC = 852.85 vs. 860.07). Accumulated mindfulness was associated with increased perceived control over gambling, whereas the acute effect again appeared minimal.

In summary, accumulated meditation exhibited robust and consistent effects across outcome measures, indicating a dose-dependent benefit of mindfulness practice that could not be attributed solely to the passage of time. The acute effect of individual mindfulness sessions, by contrast, was appreciable only for current craving scores.

Discussion

A growing body of research has evaluated the effects of mindfulness training on clinically meaningful outcomes in gambling disorder and other forms of addiction. However, the majority of these studies utilize a pre-post-follow-up design, with or without control groups, and typically focus on group-level comparisons (see López-Guerrero et al., 2025, for a recent review and meta-analysis). This methodological approach limits the capacity to capture dynamic, within-person changes during the intervention.

Although a small number of case studies are available (see, for example, Toneatto et al., 2007; de Lisle et al., 2011), they offer limited generalizability despite their richness in qualitative detail. While these studies provide specific insights into symptom evolution, interpreting change remains complex due to the multiple, often interacting variables present during intervention. In the present case-series study, we adopted an intermediate methodological approach that bridges the richness of case study data with the analytical precision of mixed-effects modeling, enabling us to examine longitudinal individual-level patterns.

The data presented in Table 1 show pre-post improvements in DSM symptoms in 4 out of 8 trainees, and in compulsivity in 6 out of 8. We have however refrained from running any statistical analyses on these differences, first, because pre-post differences are hardly interpretable in the absence of a control group and, second, because it diverges from the aim of the present study. Namely, to capitalize on rich data of individual dynamics and their linkages to chronic and acute effects of mindfulness practice, independently of the mere passage of time. These linkages remain, by definition, conflated in any pre-post differences.

Converging with these pre-post differences, Figs. 1–5 indicate that, in cases where craving was present and demonstrated intraindividual variability, participation in the program was generally associated with improvements in the measured constructs. Most trainees exhibited reduced perceived craving, decreased perceived relapse risk, and improved perceived gambling control over time.

Verbal reports further suggested that trainees viewed mindfulness practice as helpful. Illustrative statements include: “With mindfulness, it becomes easier to want to quit gambling” and “I learned a great deal from meditation about remaining with the urge to gamble without acting upon it”. Another trainee emphasized the protective role of the intervention, noting, “Had it not been for mindfulness practice, I would have relapsed, as I experienced work-related difficulties that I previously managed through gambling”. However, these accounts remain anecdotal and may be influenced by expectancy and social desirability biases, both on the part of the trainees and their therapist (Goldstein et al., 2017; Schell et al., 2021).

Our primary analyses indicate that improvements are not only substantial, but more strongly tied to sustained engagement in mindfulness practice than to either total time spent in the program or the immediate (acute) effects of individual sessions. While session immediacy yielded a limited positive effect on craving, it had no relevant influence on other outcome measures. Put simply, the cumulative practice effect suggests that progress increases with continued mindfulness practice but tends to plateau when practice ceases. Specifically for craving, the waning of any acute session-related effects may lead to a temporary resurgence when practice is discontinued for several consecutive days. In contrast, perceived control and relapse risk do not exhibit signs of such rebound effects.

Accumulated mindfulness practice may influence key outcomes through several interconnected mechanisms, including the gradual learning to notice the emergence of craving as a transient mental event rather than an irresistible command. This process, known as decentering (see Cásedas et al., 2024 for a review), creates space between the craving and the behavioral response, fostering a non-reactive stance (Hölzel et al., 2011; Tapper, 2018). As this skill develops, individuals often report a stronger sense of control over their impulses. What once felt automatic or uncontrollable becomes more manageable, cultivating a greater sense of agency and enhancing self-efficacy. Core elements of the training program, such as focused attention, also help interrupt rumination and reduce the mental chatter that fuels perceived loss of control (O’Neill, 2017; Witkiewitz et al., 2013).

As noted earlier, mindfulness may also support emotional regulation, a critical factor given that gambling is frequently used to avoid or suppress negative emotions (Navas et al., 2019; Perales et al., 2020; Sancho et al., 2018). By cultivating non-judgmental awareness of emotional states, individuals learn to tolerate distress without immediately seeking escape through gambling (Maynard et al., 2015; Tang et al., 2016; Teper et al., 2013). Over time, this may increase emotional resilience and reduce vulnerability to relapse (Priddy et al., 2018; Witkiewitz et al., 2013).

Finally, mindfulness has been linked to improved value-based decision making (Schmitzer-Torbert, 2020; Verdejo-García et al., 2019). By fostering greater awareness of long-term goals, individuals can make more intentional choices aligned with their values, rather than being driven by fleeting urges. This shift from automaticity to deliberate action represents a cornerstone of sustained relapse prevention.

Limitations, strengths, and final remarks

While the proposed mechanisms offer plausible explanations for the effects of accumulated mindfulness practice, the current study does not provide direct empirical evidence to support these causal pathways. Furthermore, the observed dose-dependent relationship between accumulated practice and clinical outcomes may, at least in part, reflect the influence of unmeasured third-variable factors. For example, individuals who are already experiencing more favorable trajectories may be inherently more motivated to engage in consistent, autonomous daily practice.

In other words, trainees showing earlier improvements might also be more inclined to adhere closely to the program’s guidelines. With a larger sample, it would be possible to investigate between-participant variability more thoroughly, for instance, by examining whether baseline characteristics predict subsequent training adherence. However, due to the limited sample size inherent in this case-series design, the present study is best suited to identifying and interpreting intra-individual patterns of change over time. As such, caution is warranted when generalizing these findings or inferring directionality.

Nevertheless, our analysis demonstrates noteworthy methodological strengths. In addition to the granularity with which it captures outcome dynamics, it offers a significant advantage over conventional BABAB case or case-series designs (i.e., baseline–intervention–return to baseline–intervention–return to baseline). These traditional frameworks typically rely on the assumption that observed improvements are directly and exclusively associated with the presence of the intervention. Consequently, conclusions are drawn from contrasts between intervention (A) and baseline (B) phases, inherently limiting the ability to detect lasting changes induced by the intervention, precisely the effects most critical to therapeutic efficacy.

By contrast, the mixed-effects modeling approach employed in the present analysis enables a nuanced distinction between chronic (i.e., enduring) and acute (i.e., transient) therapeutic effects, while simultaneously accounting for individual time-dependent variability that might remain confounded with the intervention. This form of analysis offers a level of mechanistic insight that traditional Randomized Controlled Trials (RCTs) often fail to capture, particularly in relation to temporally distributed therapeutic processes.

Data availability

The datasets generated and analyzed during the current study are available in the Open Science Framework repository at the following anonymous link for peer review: https://osf.io/tz86n/?view_only=8583058d78e34077b6ba610f3dd95b67

Author Contribution

F.J.R. conceptualized the study, designed and implemented the protocol, supervised the project, and wrote the original draft.E.A.G.G. implemented the protocol and reviewed and edited the manuscript.J.L.G. and I.M. reviewed and edited the manuscript.J.C.P. conceptualized the study, supervised the project, acquired funding, and wrote the original draft.All authors reviewed and approved the final manuscript.

Data Availability

Acknowledgement

The authors would like to thank the staff of the local associations for their support and coordination, and the patients who generously contributed to this study.

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Fig. 1

Current craving (VAS) scores across trainees and days.

Fig. 2

Daily craving (VAS) scores across trainees and days.

Fig. 3

Perceived control over gambling scores across trainees and days.

Fig. 4

Perceived relapse risk across trainees and days.

Fig. 5

Predicted craving scores (left panel), perceived relapse risk (middle panel), and perceived control over gambling (right panel) from full models.

Note: SD = Standard Deviation. Predicted values are derived from full mixed-effects models. Lines represent the mean (dotted), + 1 SD (dashed), and − 1 SD (solid) of accumulated meditation. Shaded areas indicate 95% confidence intervals.

Yes