Analysing self-reports from 766 predominantly Western recreational MDMA users with K-medoids clustering, the authors identified three consumption-setting types (party with friends, private home, mixed) and three intention types (euphoria/energy, self‑insight, mixed). Users with self‑insight or mixed intentions reported substantially greater long-term socio‑emotional benefits than those seeking euphoria/energy, while consumption setting was unrelated to these outcomes.
MDMA is a recreational drug commonly used to enhance euphoria, but it is also used in non-party settings with self-insight or social connection intentions. Yet, little is known about whether distinct consumer groups are formed based on consumption setting and intention. We aimed to characterize different types of recreational MDMA users based on consumption setting and intentions, and to examine their differences in perceptions of long-term social-emotional effects of MDMA use. We analyzed self-reports of 766 individuals (ages 18–61, mostly from Western countries), reporting on their MDMA consumption habits and perceived effects. We used a K-medoids clustering algorithm to identify distinct types of consumption settings and intentions. We identified three setting types – party settings with friends (N = 388), private home settings (N = 132), mixed settings (N = 246) – and three intention types – euphoria and energy (N = 302), self-insight (N = 219), mixed intentions (N = 245). Members of the self-insight and mixed intentions clusters reported considerably more long-term socio-emotional benefits than members of the euphoria and energy cluster. No differences were observed between the setting clusters. In this particular sample, more long-term benefits than harms were reported. Our findings suggest that the long-term social-emotional benefits of MDMA are associated with whether users seek self-insight or have mixed intentions.
MDMA (3,4-methylenedioxymethamphetamine) produces well-documented short-term effects such as euphoria, reduced fear, and increased sociability, and it is widely used recreationally in many Western countries. Previous research has focused largely on negative cognitive and emotional consequences of recreational MDMA use or on positive outcomes in therapeutic contexts; comparatively little is known about long-term social and emotional effects following recreational use, or about how users' intentions and the contexts in which they consume MDMA (set and setting) relate to those long-term outcomes. Johnstad and others have argued that naturalistic patterns of psychedelic use vary widely and that some patterns are associated with better long-term outcomes, but empirical characterisations of those patterns for MDMA are limited. Elmer and colleagues set out to fill this gap by identifying distinct types of recreational MDMA users based on (a) the physical and social settings in which they typically consume the drug and (b) their self-reported intentions for use, and then testing whether these user types differ in perceived long-term socio-emotional benefits and harms. Using an existing open dataset of recreational MDMA consumers, the study asks three questions: which setting-based user types exist, which intention-based user types exist, and whether these types report different long-term social-emotional effects of MDMA use. The investigators additionally planned multivariable analyses to adjust for potential confounders such as age, gender, frequency of recent use, an MDMA-adapted addiction screener, and mental health diagnoses.
Papers cited by this study that are also in Blossom
Regan, A., Margolis, S., De Wit, H. et al. · PLOS ONE (2021)
Carhart-Harris, R. L., Goodwin, G. M. · Neuropsychopharmacology (2017)
Carhart-Harris, R. L., Nutt, D. J. · Journal of Substance Use (2010)
Colbert, R., Hughes, S. · Culture Medicine and Psychiatry (2022)
This cross-sectional study analysed survey data collected online from people who reported recreational MDMA use. Participants were recruited via online psychedelic communities, University of Amsterdam students, and word of mouth; students could receive course credit and other participants could enter a voucher lottery. Of 924 completers, exclusions for failed attention checks, therapeutic MDMA use, and incomplete responses produced a final sample of 766 individuals aged 18–61 (mean 29.4, SD 9.14) from mainly Western countries. The questionnaire captured demographic data, recent MDMA use (recency and frequency over the past 6 months), an adapted CAGE-AID measure for MDMA addiction, history of mental health diagnosis, relative-percentage measures of physical and social settings of use, eight intention items rated 0–100, and two sets of 12 items each assessing perceived long-term positive effects and long-term harms (also rated 0–100). Physical settings included seven categories (clubs, festivals, bars, home, nature, house parties, other) reported as percentages summing to 100%; social settings used six analogous percentage items. To identify user types, the researchers applied a three-step clustering procedure separately to setting and intention variables. For settings, a PCA (fuzzy coding appropriate for percentage data) reduced 13 setting variables to principal components with eigenvalues >1, and K-medoids (PAM) clustering on the first four components selected the optimal k by average silhouette width. For intentions, a non-fuzzy PCA produced two components which were clustered by K-medoids, again selecting k by silhouette width. Robustness checks included alternative k, clustering without PCA, and analysing principal components directly. To test associations with long-term outcomes, the investigators ran a series of multivariable linear regressions treating each long-term benefit or harm item as a dependent variable. Independent variables included dummy indicators for setting cluster and intention cluster, plus control variables: gender, age, frequency of MDMA use in the past 6 months (five ordinal levels), months since last use (capped at 24), MDMA-adapted CAGE score, and reported mental health diagnosis. Models both with and without the cluster indicators were estimated to compare explained variance (R2). The Benjamini–Hochberg procedure was applied to control false discovery for tests within each research question. All materials, data, and analysis scripts were made available on OSF.
Cluster identification: For settings, three clusters were selected (k = 3) with sizes N = 388, 132, and 246. These were labelled Party Setting With Friends (consumption in clubs, raves, festivals with friends), Private Home Settings (at home with partner, friends, or alone), and Mixed Physical and Social Settings (varied settings). For intentions, three clusters emerged (k = 3) with sizes N = 302, 219, and 245, labelled Euphoria and Energy (high scores on euphoria/energy only), Self-Insight (high on insight), and Mixed Intentions (higher scores across most intention items). Overlap: 55% of Party Setting members were in the Euphoria and Energy intention cluster; 61% of Private Home Setting members were in the Self-Insight intention cluster; the Mixed Setting cluster was more evenly distributed across intention types. Sample characteristics by cluster: Setting clusters did not differ on frequency or recency of use or on reported mental health status, but they did differ in mean MDMA-adapted CAGE scores (Mixed Setting highest M = 1.35, Party M = 1.15, Private home M = 0.90), age (Party M = 27.34, Private home M = 34.30, Mixed M = 30.01), and gender composition (Party more females; Mixed slightly more males). Intention clusters differed on age (Euphoria youngest M = 27.24; Self-Insight M = 31.75), mental health diagnosis prevalence (Euphoria 25.2% vs Self-Insight 38.8% and Mixed 40%), and CAGE scores (Mixed highest M = 1.54; Self-Insight lowest M = 0.78). Perceived long-term effects: Across the full sample, perceived long-term benefits averaged M = 55.2 (SD = 36.9) on 0–100 scales and harms averaged M = 8.63 (SD = 19.86). ANOVAs and adjusted regressions showed few differences by setting cluster: compared with Party Setting With Friends, Private Home Settings showed somewhat lower scores for appreciation of aesthetic experiences (b = -9.79, t(754) = -2.72, p = .023) and improved friendship quality (b = -10.63, t(754) = -2.93, p = .013); Mixed Setting did not differ significantly on long-term variables. In contrast, intention clusters showed substantial differences for positive long-term outcomes. Membership of the Self-Insight and Mixed Intentions clusters was associated with higher ratings on most positive long-term variables relative to the Euphoria and Energy cluster; standardized effects averaged β = 0.30 (SD = 0.11), typically medium-to-large. For example, belonging to the Self-Insight cluster (vs Euphoria and Energy) corresponded to an estimated 23-point increase on a 0–100 friendship-quality outcome (β ≈ 0.29). Negative long-term outcomes were mostly similar across intention clusters, but the Mixed Intentions cluster reported higher scores than the Euphoria cluster on several harms: unpleasant memories (b = 5.12, p = .010), worsened friendship quality (b = 2.89, p = .040), increased social anxiety (b = 6.12, p < .001), shallow emotional experiences (b = 3.77, p = .029), negative world view (b = 4.70, p < .001), and increased other drug use (b = 6.90, p < .001). The Self-Insight cluster reported lower dampened aesthetic experiences than the Euphoria cluster (b = -4.80, p = .024). Explained variance: Setting and intention cluster variables contributed meaningfully to explained variance for long-term benefits (average additional R2 = 0.11, SD = 0.06) but not for long-term harms (average additional R2 = 0.02, SD = 0.01). Robustness checks (alternative k, no PCA, principal components as predictors, regressions without covariates) produced conclusions consistent with the primary analyses.
Elmer and colleagues interpret their findings to mean that recreational MDMA users can be categorised into distinct groups by setting and by intention, and that users' intentions are strongly associated with self-reported long-term social-emotional benefits whereas the physical/social setting shows little association with those benefits or harms. Specifically, individuals reporting Self-Insight or Mixed Intentions attributed more long-term positive outcomes—improved social relationships, empathy, and richer emotional experiences—than those in the Euphoria and Energy cluster. Effect sizes for these intention-related associations were generally medium to large. By contrast, setting clusters (party, private home, mixed) were not linked to most long-term outcomes, although private-home users reported somewhat lower long-term aesthetic appreciation and friendship-quality gains compared with party users. The authors situate these results within prior literature emphasising the importance of intention or 'set' in both recreational and therapeutic psychedelic use, noting parallels with research on classic psychedelics and with clinical MDMA trials that combine drug administration with psychotherapeutic intent. They suggest the findings may inform harm-reduction and psychoeducation efforts—particularly interventions at festivals and parties where hedonic motivations and the Euphoria and Energy profile are more common—while recognising that intentions, rather than the mere physical location of use, may be a key determinant of perceived long-term benefits. Several limitations are acknowledged. The sample was self-selected and recruited from online psychedelic communities and university settings, so it may over-represent individuals with positive experiences and is not nationally representative. The cross-sectional, self-report design prevents causal inference and is susceptible to memory bias and attributional confounding (for example, happier people may be more likely to report self-insight intentions and positive outcomes). Although the authors adjusted for several covariates (age, gender, recent frequency of use, MDMA-adapted CAGE score, mental health diagnoses), unobserved confounders remain possible. The analysis did not record lifetime number of uses, although past-6-month use did not moderate the observed associations. Finally, the authors call for longitudinal and mechanistic research to test whether changing intentions can alter long-term outcomes, to examine dosage and frequency effects, and to probe whether aspects of setting such as comfort and control—rather than setting category per se—mediate long-term effects.
The study concludes that recreational MDMA users cluster into distinct groups by intention and by typical setting, and that the intentions with which people consume MDMA are associated with their self-reported long-term psychological benefits. In particular, using MDMA with self-insight or mixed intentions is linked to greater perceived long-term socio-emotional benefits than primarily hedonic (euphoria/energy) intentions. While recognising substantial remaining uncertainties and limitations, the authors highlight the importance of considering user intentions when studying MDMA's long-term effects and when designing harm-reduction or psychoeducational interventions.
The current study aims to address these gaps in the literature by, first, examining different types of recreational MDMA users and their preferred settings and commonly-held intentions. Second, we examine the relationship between the identified types of MDMA users and their self-reports of the personal long-term socio-emotional effects of MDMA use. Specifically, we will address the following three research questions: 1. Which types of recreational MDMA users exist with regards to the settings in which they experience MDMA? 2. Which types of recreational MDMA users exist with regards to the intentions with which they consume MDMA? 3. Are there differences in the perceived long-term social-emotional effects of MDMA use between the identified types of MDMA consumers (in terms of settings and intentions)? We explored these three research questions using a dataset collected by Elsey et al., who surveyed 886 recreational MDMA consumers about the perceived short-and long-term benefits and harms they subjectively experienced after consuming MDMA. Elsey et al.collected the data with the intention of providing an openly available dataset on the long-term effects of recreational MDMA use. Although their preprint provides various descriptive aspects of the data, including bivariate correlations between the variables collected, they do not differentiate between types of MDMA consumers and how they differentially perceive these long-term social and emotional outcomes. Moreover, in our analyses, we investigate these associations not only bivariately, but using a multivariable regression framework-allowing us to control for potentially confounding variables such as age, gender, frequency of MDMA use, MDMA addiction, and mental health conditions. By understanding the factors that may influence the long-term effects of MDMA use, this study aims to add to the gaps in the literature and help inform safe consumption that promotes positive long-term socio-emotional outcomes.
Sample averages of the physical and social setting variables (RQ1) and intention variables (RQ2) are presented in Tableof the Supplementary Materials. An analysis of the eigenvalues of the setting variables revealed the presence of four eigenvalues above 1 (3.03, 1.76, 1.31, 1.13, 0.98, …). Thus, the first four principal components of the settings variable were used for the cluster analysis. To determine the ideal number of clusters, the Medoids clustering algorithm was applied on k numbers of clusters and the value of k with the highest average silhouette width S was chosen, as it is a measure of how well data points are assigned to clusters. Our analysis yielded the following values: S k=2 = 0.31, S k=3 = 0.34, S k=4 = 0.32, S k=5 = 0.30, S k=6 = 0.25. Given that k = 3 showed the highest S value, our participants were assigned to three different clusters with sizes N 1 = 388, N 2 = 132, N 3 = 246. For robustness analyses on alternative clustering analysis choices with (a) k = 2 and (b) clustering analysis without prior PCA, please refer to the Supplementary Materials Figs.. Figureillustrates the three identified clusters and their average values per setting variable. We named the three setting clusters according to their unique patterns displayed in Fig.. The first cluster was named Party Setting With Friends, characterized by consuming MDMA in clubs, raves, or festivals together with friends. The second cluster was named Private Home Settings, as individuals in this cluster mostly consume MDMA at home with their partner, friends, or by themselves. The third cluster was named Mixed Physical and Social Settings, as individuals in this cluster consume MDMA in various physical and social settings. Tablesandin the Supplementary Materials report the results of analyses of differences between the setting clusters in terms of a number of covariates (i.e. age, gender, frequency and recency of MDMA use, CAGE MDMA addiction score, mental health status). There were no differences between the setting clusters in terms of frequency and recency of MDMA use (F frequency (2) = 1.062, p = 0.346; F recency (2) = 1.727, p = 0.178), and mental health status (χ 2 (4) = 4.31, p = 0.120), but there were significant differences between the setting clusters in the CAGE MDMA addiction score (F setting (2) = 6.23, p = 0.002), age (F setting (2) = 31.59, p = < 0.001), and gender identity (χ 2 (4) = 13.32, p = 0.010). CAGE MDMA addiction scores were highest in the Mixed Setting cluster with M = 1.35 (SD = 1.18), whereas the Party Setting with Friends cluster scored on average 1.15 (SD = 1.18) and the private home setting M = 0.90 (SD = 1.17). Participants in the cluster Party Setting with Friends were on average 27.34 (SD = 7.26) years old, whereas the private home setting cluster and the mixed setting clusters were on average 34.30 (SD = 11.37) and 30.01 (SD = 9.46) years old, respectively. There were more females than males in the Party Settings with Friends cluster (N female = 211; N male = 173; N other = 4) and slightly more males in the mixed setting cluster (N female = 113; N male = 121; N other = 12).
Following the analysis of RQ1, we proceeded to examine the eigenvalues of the intention variables, revealing two eigenvalues above one: 2.27 and 1.44, respectively. We then utilized the two principal components of a PCA on the intention variables. Subsequently, the Medoids clustering algorithm was executed on k numbers of clusters to determine the k with the highest average silhouette width S for further analysis. The resulting values were as follows: S k=2 = 0.35, S k=3 = 0.40, S k=4 = 0.33, S k=5 = 0.34, S k=6 = 0.34. Based on these results, we assigned our participants to three distinct clusters, with k = 3 having the highest S value. The three clusters were of sizes N 1 = 302, N 2 = 219, N 3 = 245. For robustness analyses with alternative clustering analysis choices (a) k = 2 and (b) clustering analysis without prior PCA, please refer to Supplementary Materials Figs.. In Fig., the assignment of individuals to the three identified clusters is displayed along the population's average values per intention variable. The clusters were named according to the observed patterns in Fig.. The first cluster was dubbed Euphoria and Energy due to the high average values on the euphoria and energy items, and relatively low values on all other items. The second cluster, which we named Self-Insight, consists of individuals scoring high mainly on the insight item. The third cluster was composed of individuals with Mixed Intentions, scoring higher on most items compared to the other two clusters of individuals. As described in Tablesandin the Supplementary Materials, there were no differences between the intention clusters in terms of gender identification (χ 2 (4) = 3.01, p = 0.556) and frequency and recency of MDMA use (F frequency (2) = 0.06, p = 0.801; F recency (2) = 2.35, p = 0.126), but differences in terms of age (F(2) = 13.89, p = < 0.001), mental health status (χ 2 (4) = 17.26, p = < 0.001), and CAGE MDMA addiction score (F(2) = 12.68, p = < 0.001). Individuals in the Self-Insight cluster were on average 31.75 years old (SD = 10.13), whereas the mixed intention cluster was M = 29.95 (SD = 8.95) years old. The Euphoria and energy cluster was the youngest with M = 27.24 years old (SD = 8.06). The Euphoria and Energy cluster consisted of fewer participants (25.2%), who reported ever having had a mental health diagnosis, compared to the self-insight (38.8%) and mixed intentions clusters (40%). The average CAGE MDMA addiction score was highest in the mixed intention cluster (M = 1.54, SD = 1.24). The Self-Insight (M = 0.78, SD = 1.02) and Euphoria and energy clusters (M = 1.15, SD = 1.16) scored lower on average. Tableshows the overlap between the setting and intention clusters. Most notable is the strong overlap between individuals in the Party Setting with Friends cluster and the Euphoria and Energy intention cluster: 55% of the individuals assigned the Party Setting with Friends cluster were also in the Euphoria and Energy intention cluster. Those individuals consuming MDMA mostly in Private Home Settings were, in 61% of the cases, also part of the self-insight intention cluster. The Mixed Physical and Social Setting cluster was about equally represented in all three intention clusters. RQ3: are there differences in the perceived long-term social-emotional effects of MDMA use between the identified types of MDMA consumers (in terms of settings and intentions)? Participants generally reported high levels of perceived benefits (M = 55.2; SD = 36.9) and low levels of perceived harms (M = 8.63; SD = 19.86), as displayed in detail in Tableof the Supplementary Materials. Tablepresents the means per identified setting and intention cluster of the long-term social-emotional variables. Additionally, the results of an ANOVA analysis, aimed at examining differences in means of long-term variables between the clusters, are also displayed in Table. The findings indicate that only a few long-term variables show significant differences between the setting clusters, whereas most long-term variables show significant differences exist between the intention clusters. To further examine these patterns while accounting for relevant covariates (see "Methods" section), we conducted a series of multivariate linear regression models. Figureshows the unstandardized regression coefficients for each long-term variable separated by the setting and intention cluster variables. The full model results can be found in the R-Markdown file at OSF. io/ t7p59.
As shown in Fig., the results indicate that the two setting variables-Private Home Settings and Mixed Physical and Social Settings-did not differ significantly in any of the perceived positive or negative long-term variables from the cluster Party Setting With friends. Only the Private Home Setting cluster showed slightly lower values in appreciate aesthetic experiences (b setting=2 = -9.79, t(754) = -2.72, p = .023) and improved friendship quality (b setting=2 = -10.63, t(754) = -2.93, p = .013) than the Party Setting With Friends cluster.
By contrast, the two intention clusters-Self-Insight and Mixed Intentions-differed significantly from the Euphoria and Energy cluster in most positive long-term effect variables (see left panel of Fig.). These effect sizes-with an average standardized β of 0.30 (SD = 0.11), ranging between 0.04 and 0.45were generally medium to large-only for the Amazing Memories variable did these clusters not differ ( b intention=2 = 1.87, t(754) = 0.87, p = .595 , b intention=3 = 3.90, t(754) = 2.01, p = 0.126 ). To contextualize these effect sizes, we examine the association of belonging to the Self-Insight cluster on the perceived long-term positive effects on friendships, indicated by β = 0.29 (i.e. the average effect size). In our model, being in this cluster, as opposed to the Euphoria and Energy cluster, is linked to a substantial increase of 23 points on a scale of 0-100. This reflects a notably large effect. Interestingly, the two intention clusters, Self-Insight and Mixed Intentions, did not differ significantly from the Euphoria and Energy cluster in most negative long-term effect variables (see right panel of Fig.). Only the Mixed Intentions cluster differed from the Euphoria and E ne rg y clu ste r i n u npl e a s ant me mor i e s ( b intention=3 = 5.12, t(754) = 3.02, p = .010 ) , w o r s e n e d f r i e n d s h i p q u a l i t y ( b intention=3 = 2.89, t(754) = 2.49, p = .040 ) , i n c r e a s e d s o c i a l a n x i e t y (b intention=3 = 6.12, t(754) = 3.91, p < .001 ) , s h a l l ow e m o t i o n e x p e r i e n c e s ( b intention=3 = 3.77, t(754) = 2.61, p = .029 ), negative world view (b intention=3 = 4.70, t(754) = 4.02, p < .001 ), and increased other drug use (b intention=3 = 6.90, t(754) = 4.36, p < .001 ). The Self-Insight cluster showed lower values in the dampened aesthetic experiences variable compared to the Euphoria and Energy cluster ( b intention=2 = -4.80, t(754) = -2.67, p = 0.024).
By comparing the explained variance R 2 of linear regression models with and without the setting and intention clusters (while controlling for covariates), we further assessed how much of the explained variance of the outcome variables can be attributed to the setting and intention clusters. Figureshows the explained variance R 2 attributed to the setting and intention cluster variables per long-term outcome variable. Setting and intention Table. Mean (and SD) of perceived long-term benefits and harms by setting and intention clusters. For each benefit and harm item, participants were asked how MDMA may have affected them over the long term (i.e. at times when they are not on MDMA or recovering from taking MDMA). N = 766, df = 2, M mean, SD standard deviation, F = F value of an ANOVA analysis comparing the cluster mean. cluster variables explained a notable proportion of the variance in long-term benefits with an average of (M = 0.11, SD = 0.06), but not for the long-term harms (M = 0.02, SD = 0.01).
We conducted several analyses to test the robustness of our results under differ assumptions and procedures. Specifically, (a) we assessed a two-cluster solution for the setting and intention variables, (b) we applied the clustering algorithms without performing a PCA first, (c) we analyzed RQ3 with the principal components as independent variables instead of the clusters identified by the clustering algorithm, and (d) we ran a regression analysis without any covariates (i.e. only the dummy variables for the intention and setting clusters). The conclusions drawn from these robustness analyses are identical to the ones described in this Results section. For details on the results of these robustness analyses, please see the section Robustness Analyses in the Supplementary Materials.
This study aimed to identify different types of recreational MDMA users with regards to the setting (RQ1) and intentions (RQ2) of their MDMA consumption, and to investigate whether these types of users report systematically different long-term social-emotional benefits and harms (RQ3). Our analyses identified three distinct clusters of recreational MDMA users based on the physical and social settings in which they typically consume MDMA: Party Setting With Friends, Private Home Settings, and Mixed Settings. We further identified three distinct clusters based on users' intentions for consuming MDMA: Euphoria and Energy, Self-Insight, and Mixed Intentions. These findings are consistent with previous research that has identified a variety of typical settings in which MDMA is consumed, with a large proportion of individuals consuming MDMA in physical settings such as nightclubs, outdoor festivals, or private homes and with hedonic, introspective, and social motives. Regarding the long-term social and emotional outcomes, participants generally reported high levels of benefits and very low levels of perceived harm. Our results further indicate that the intentions participants described were strongly associated with the long-term social-emotional benefits they reported, but not with the harms. Specifically, individuals in the Self-Insight and Mixed Intentions clusters reported proportionally higher longterm benefits attributed to MDMA consumption, such as improved social relationships, empathy, and emotional experiences, compared to individuals in the Euphoria and Energy intention cluster. With an average standardized β of 0.30 (SD = 0.11), these effect sizes were generally medium to large. By contrast, the physical and social settings in which participants consumed MDMA were not associated with perceived long-term social-emotional benefits or harms. These findings indicate that consuming MDMA with particular intentions (e.g. using MDMA to gain self-insight) rather than consuming it in particular settings may be associated with relatively more beneficial social and emotional experiences in the long-term. Overall, these findings suggest that recreational MDMA users have different motivations and intentions when consuming this substance. Long-term social-emotional effects, following MDMA consumption, also differ among these distinct groups. These results provide support for growing literature emphasizing the importance of considering the complexity of MDMA consumption, instead of oversimplifying it as either positive or negative. Our findings also suggest that individuals who use MDMA with the intention of gaining self-insight or for mixed purposes may experience more favorable long-term outcomes, such as enhanced social functioning and emotional experiences, compared to those who primarily use it for hedonistic reasons (i.e., to feel euphoric or energetic). This information could be used to inform harm reduction strategies for MDMA use, as well as to help individuals make informed decisions about their use of psychoactive substances. For example, interventions aimed at individuals in the Euphoria and Energy cluster could focus on improving their experiences through psycho-education about the importance of set and setting factors. Although the setting clusters did not differ in long-term outcomes, they may help to provide information on where such interventions could be effective: The individuals who consumed MDMA mainly with Euphoria and Energy intentions-associated with less positive long-term effects-mainly consumed MDMA in a Party Setting With Friends (see Supplementary Materials Tablefor details). Existing programs to inform and care for MDMA consumers at festivals and parties may therefore represent effective locations for such psycho-education interventions. These findings add to our understanding of the complex interplay between MDMA consumption, set and setting factors, and social-emotional functioning. The importance of intentions has been emphasized in therapeutic contexts (e.g. Ref.), and this study suggests that one's intentions may play a crucial role in perceived long-term psychosocial benefits. According to Greer and Tolbert, it is important that MDMA is used with the explicit goal of learning from the experience: "Taking MDMA with an intention to learn, with an attitude of acceptance, and in a safely structured setting enabled people to experience their true nature which is essentially loving and forgiving" (p. 34). The importance of intentions has also been emphasized by findings of Phase III clinical trials on applying MDMA together with (self-insight focused) pre-and post-session psychotherapy to treat PTSD. In the latest trial, 67% of MDMA recipients no longer met PTSD diagnosis criteria after the 18-week trial. Unique about this study is its depiction of how people typically consume MDMA across a variety of settings and with various intentions. Johnstadargued that the research community needs better knowledge about these contextual factors in order to better understand the consumption of psychedelics and related substances in (Western) societies. Learning about typical consumption patterns across a variety of settings helps researchers to know when (not) to generalize across different types of MDMA users. For example, studies that recruit participants at a festival or rave (e.g. Ref.) may end up not including individuals who consume MDMA mainly in private settings. The findings of this study align well with the existing findings on the perceived benefits of recreational consumers of classic psychedelics (e.g. LSD, psilocybin, mescaline, ayahuasca; excluding MDMA). For example, Wolff et al.found that individuals with high levels of therapeutic intentions are likely to show shifts in psychological flexibility (which is argued to be a key factor for therapeutic success), whereas individuals with high levels of hedonic intentions or escapist intentions do not. Similarly, Haijen et al.showed that having clear intentions when consuming the classic psychedelics was associated with more mystical-type experiences, which in turn, was related to more positive long-term effects on well-being. Finally, it is important to consider this study's limitations. First, the study may have oversampled individuals who had relatively more positive experiences with MDMA, which could have biased the results towards more positive outcomes. For this reason, our findings should be interpreted cautiously, as they are not generalizable to wider populations. Future research should prioritize the collection of a more representative sample of the diverse population engaging in MDMA use to enhance the external validity of research findings. This approach would provide a more comprehensive understanding of the varied experiences associated with MDMA consumption across different user profiles. Second, unobserved confounding variables might have also biased our results. For example, individuals who report generally higher happiness may be more likely to consume MDMA for insight and also to see it as a more positive experience. At the same time, it is possible that individuals who already believe in the potential positive effects of MDMA are more likely to report using it for self-insight, and to perceive positive changes as a result. Furthermore, it is conceivable that individuals who have experienced positive changes from MDMA use in the past subsequently use it for the purpose of inducing yet more positive changes. Finally, it may be that individuals who consume MDMA in private settings for self-insight may do so less frequently than those who consume it in party settings for euphoria and energy (although we observed no significant differences in frequency of MDMA use between cluster variables, see Supplementary Materials Table). By controlling for a number of important and potential confounding factors (age, gender, frequency of MDMA use, MDMA addiction, and mental health conditions), we aimed to reduce the effects of such confounding factors. Third, this study relied solely on self-reported measures of setting, intention, and long-term effects, which may be subject to memory biases, schemas, stereotypes, and preconceptions. The cross-sectional design of the study also precludes any causal inferences about the relationship between MDMA use and long-term outcomes: It may be that participants are biased towards (wrongly) attributing perceived social-emotional developments to their MDMA experiences, while misattributing harms to external factors. Future work using different methodologies (e.g. longitudinal) should be considered to assess the replicability of these findings, given this is the first study that assesses long-term benefits/harms based on setting and intentions. A fourth limitation is that the study did not measure how many times participants had used MDMA, which could have affected their categorization into different clusters. However, our proxy for the overall use of MDMA -MDMA consumption over the past 6 months -did not moderate the associations between setting/intention and long-term outcomes (see R-Markdown analysis on. io/ t7p59). Hence, these data suggest no additional differences between the clusters for individuals who consumed more MDMA within the past 6 months compared to those who consumed less MDMA in the past 6 months. One potential avenue for future research is to develop interventions that aim at changing people's intentions for consuming MDMA and test whether such interventions produce positive long-term social-emotional effects. Additionally, future investigators could examine the potential moderating factors that may influence the longterm effects of MDMA use, such as its dosage, frequency of use, and other features of set and setting. Moreover, it would be worthwhile to explore the mechanisms underlying the differences in long-term effects between different types of recreational MDMA users. For example, people who have insight intentions may have a higher need for integration after the experience, which leads to more positive long-term effects. Finally, this study did not find any meaningful effects on long-term outcomes based on the type of setting in which MDMA is consumed. The role of setting in influencing long term socio-emotional effects following MDMA use requires closer investigation. Future research could explore whether setting plays a role in individuals' comfort levels and sense of control in the setting rather than the setting itself.
"We know that some people take psychedelics infrequently in carefully planned sessions for spiritual, therapeutic and developmental reasons, while others perhaps use psychedelics very frequently for entertainment or escapist purposes, and we should not be surprised if these usage patterns are associated with very different long-term consequences. " (Johnstad, 2021, p. 36). While much research has examined the short-term socio-emotional effects of MDMA administration (during the experience or in the days thereafter), as well as the long-term effects and benefits of classic psychedelics in recreational consumers, e.g. Refs., little work has been devoted to the long-term socio-emotional effects of MDMA consumption. In this study, we identified different types of MDMA users and showed that differences in the intentions with which these individuals consume MDMA are associated with different self-reported longterm psychological benefits. While a great deal still needs to be learned about typical patterns of recreational consumption of MDMA-and its long-term impacts-this study provides further evidence highlighting the importance of intentions.
All materials, data, and analysis script are available on OSF (. io/ t7p59). More details on the data collection can be found in the preprint by Elsey et al..
Participants were recruited through various online communities with interest in psychedelics, among students of the University of Amsterdam, and via word of mouth. Students could receive research credits for their participation, while all other participants received 50€ vouchers through random lottery draws. Nine-hundredtwenty-four individuals completed the online survey. Thirty-six participants were removed because they either failed to pass the attention checks (n = 25) or indicated using MDMA in therapeutic settings (n = 13). Additionally, we removed participants who did not complete all relevant parts of the survey (see Materials for a list of relevant variables; n = 120). The final sample consisted of 766 individuals, of which 363 (47.4%) identified as male, 384 (50.1%) as female, and 19 (2.5%) as other or undisclosed. The mean age was 29.4 years (SD = 9.14). The majority of participants indicated that they lived in the U.S. (n = 249, 32.5%), the Netherlands (n = 230, 30.0%), Canada (n = 81, 10.6%), or the U.K. (n = 68, 8.9%). The remaining participants were from other countries around the globe (n = 138, 18.0%). MDMA was last used within the past 6 months by 500 participants (65.3%), within 6 to 24 months by 161 (19.7%), and more than 24 months ago by 115 (15.0%). Of those who had used MDMA within the past 6 months, the majority indicated using it rarely or occasionally (n = 392; 63.4%), whereas the remaining 127 (24.5%) indicated using it regularly, very regularly, or on most days.
Individuals who were at least 18 years old and fluent in either English or Dutch were eligible to participate in this study. Upon checking these inclusion criteria and signing the informed consent form, which all participants did, participants provided demographic information. They were then asked a set of items pertaining to their past use of MDMA, including intentions for using it, as well as immediate and long-term positive and negative experiences. Next, participants responded to questions about the physical and social contexts of their use, as well as on the consumption of MDMA mixed with other drugs within the past 6 months and during their highest period of consumption. The questionnaire ended with an MDMA-specific and a general drug addiction scale. All items used in the survey can be retrieved at OSF. io/ t7p59. Items relevant to this analysis are described in this manuscript and in the Supplementary Materials Tables. The data collection and survey materials were approved by the University of Amsterdam institutional ethics review board (2020-COP-11936). All methods were carried out in accordance with the guidelines provided by the ethics review board of the University of Amsterdam.
The following blocks of survey items were used in the subsequent analyses.
The physical settings in which MDMA had been consumed was measured with a relative percentage score assigned to seven physical settings [1 = At clubs or raves, 2 = At festivals, 3 = At pubs or bars, 4 = At home (not as part of a house party), 5 = When out in nature (not at an outdoor festival / rave), 6 = At house parties or similar social gatherings, 7 = Other situation (please specify)]. Participants were instructed to assign a percentage to each physical setting in which MDMA has been used in the past. The percentages had to sum up to 100%. Relative percentage assignments were also used to measure the social settings in which MDMA had been consumed. There were six social settings, which included 1 = Just with my partner/a date (e.g. when at home with your partner), 2 = With friends/people I know, at a place where there are many other people (e.g. going with friends to a club), 3 = Just with group of friends/people I know (e.g. taking it with a group of friends in a private place, or somewhere you won't be interacting much with people you don't already know), 4 = By myself at a place where there are many other people (e.g. going to an event without friends to meet new people or to dance by yourself, 5 = By myself (e.g. taking the drug at home, or when out in nature alone), and 6 = Other situation (please specify).
Intentions were measured with eight items, rated on a scale from 0 (Not at all a motivation for me) to 100 (A very strong motivation for me). These eight items represent the following constructs: Energy, Euphoria, Insight, Sociability, Flirtatious, Sex, Coping, and Fit In. Tablein the Supplementary Materials shows the precise formulation of these items. The eight response options for intentions were derived from prior research exploring reported motivations for MDMA usage c.f.. www.nature.com/scientificreports/ Long-term benefits Positive long-term effects of recreational MDMA use were measured with 12 items describing different social and emotional aspects. For each item, participants were asked how the drug may have affected [them] more generally over the long term, at times when [they] are not on the drug or recovering from taking the drug. Each of the 12 items represented one construct: Positive experience, positive memories, aesthetic experiences, friendship quality, social confidence, positivity, letting go, compassion, reflecting emotions, deeper emotions, positive world view, and less problematic drug use. An example item -representing friendship quality -is: Ecstasy/MDMA has helped me develop new or deeper long-term friendships with people. All items were measured on a scale from 0 (Not at all true) to 100 (Completely true). Tablein Supplementary Materials shows the precise wording of these items. The set of long-term effects variables -benefits and harms -was drawn from diverse sources c.f., including reported therapeutic outcomes effects of classic psychedelic drugs, extrapolations from acute effects studies prior MDMA research, anecdotal reports, and other psychologically relevant factors.
Negative long-term effects of recreational MDMA use were also measured with 12 items. These items partially mirror the items representing the positive long-term outcomes. For example, the Worsened Friendship Quality construct is measured with the item "My long-term friendships have been ruined or worsened by my use of ecstasy/MDMA", mirroring the friendship item shown above. The items on the long-term harm items are represented by the following constructs: Worst Experience, Troubled Memories, Dull Aesthetic Experiences, Decreased Friendships Quality, Social Anxiety, Hopelessness, Paranoia, Concentration, Worse Emotional Reflection, Shallow Emotions, Negative World View, and Problematic Drug Use. Each item was measured on a scale from 0 (Not at all true) to 100 (Completely true). Tablein Supplementary Materials shows the precise wording of these items.
In our linear regression analyses for RQ3 (see Analytical Strategy section for details), we included a number of control variables. Specifically, we controlled for the effects of gender, age, the frequency of MDMA use within the past 6 months, the number of months since the last MDMA use (with a maximum value of 24), MDMA addiction, and whether the participant reported having been diagnosed with a mental health problem. The frequency of MDMA use within the past 6 months consisted of five levels [1 = rarely (once in the past 6 months), 2 = occasionally (about two or three times in the past 6 months), 3 = regularly (about once or twice a month in the past 6 months), 4 = very regularly (about once a week in the past 6 months), 5 = most days (used the drug more days than not in the past 6 months)]. MDMA addiction was measured with an adapted version of the CAGE-AID questionnairemodified for MDMA use. The CAGE-AID was originally designed to evaluate indicators of addiction to alcohol or other substances. It consists of four questions that are answered using a Yes/No format. These questions pertain to attempts to reduce use, criticism received for one's use, feelings of guilt related to use, and using drugs/alcohol first thing in the morning. In the case of the MDMA CAGE, the question about using drugs first thing in the morning was replaced with one that asks about the respondent's history of bingeing on MDMA, specifically taking the drug repeatedly over a 48-h period without sleeping. For further details on the MDMA-adapted CAGE questionnaire, see Ref.. See Tablein the Supplementary Materials for further details on the distribution of these control variables across the clusters.
In this section, we describe the analytic strategy for each of the three research questions. RQ1: Which types of recreational MDMA users exist with regards to the settings in which they experience MDMA? We used a clustering algorithm to detect distinct types of MDMA users based on the physical and social setting variables. This analysis was conducted in three steps according to common guidelines for performing a clustering analysis. First, we conducted a principal component analysis (PCA) on the 13 variables (seven physical setting variables and six social setting variables, excluding the Other category) to reduce the noise in the data and improve the performance of the clustering algorithm. We used a fuzzy coding approach for the PCA, which is applicable to percentage-type data. Components with eigenvalues greater than one are considered unique components for subsequent analyses. In the second step, we determined the optimal number of clusters (k) by computing the average silhouette width S for various k values and selecting the k with the highest average silhouette width S. In the final step, we used a K-medoids clustering algorithm) to identify distinct types of MDMA consumers based on the components determined by the PCA. For this, we used the pam function from the cluster R-package (Version: 2.1.4), which uses the original Partition Around Medoids (PAM) algorithm to perform K-medoids clustering. We applied this K-medoids algorithm, instead of the frequently used k-means algorithm, as it provided a more robust result and is less affected by outliers. Using this procedure, each participant was assigned a number corresponding to their type of MDMA use with regards to the setting in which it was consumed. The performance of this procedure was determined by the level of the average silhouette width S of the chosen k and by visual inspections of the type-assignment with the initial items. RQ2: Which types of recreational MDMA users exist with regards to the intentions with which they consume MDMA? The procedure of RQ1 was repeated to assess RQ2. The only difference was that a non-fuzzy coding approach was used, as the intentions variables consisted of independent rating scales, instead of relative percentages. RQ3: are there differences in the perceived long-term social-emotional effects of MDMA use between the identified types of MDMA consumers (in terms of settings and intentions)? To answer this research question, we estimated a series of multivariable linear regression models. We treated each long-term outcome variable as a separate dependent variable. Independent variables were categorized into three groups: (1) Type of MDMA user based on setting, as identified in RQ1 (dummy coded variables), (2) type of MDMA user based on intention, as identified in RQ2 (dummy coded variables), and (3) control variables, as described above. In addition, for each of the dependent variables, we estimated a model without the variables indicating setting and intention type (while controlling for all other covariates). This approach allowed us to evaluate the contribution of these variables to the explained variance R 2 of the models. To address the potential for Type I errors due to the large number of statistical tests conducted, we applied a Benjamini-Hochberg correction to the p-value for the within-research-question related tests.
Create a free account to open full-text PDFs.
Hartogsohn, I. · Drug Science Policy and Law (2017)
Leary, T., Litwin, G. H., Metzner, R. · Journal of Nervous and Mental Disease (1977)
Liechti, M. E., Gamma, A., Vollenweider, F. X. · Psychopharmacology (2001)
Studerus, E., Vizeli, P., Harder, S. et al. · Journal of Psychopharmacology (2021)
Wolff, M., Evens, R., Mertens, L. J. et al. · Frontiers in Psychiatry (2020)
Mitchell, J., Bogenschutz, M. P., Lilienstein, A. et al. · Nature Medicine (2021)
Haijen, E. C. H. M., Kaelen, M., Roseman, L. et al. · Frontiers in Pharmacology (2018)
Griffiths, R. R. · Journal of Psychopharmacology (2008)
Griffiths, R. R., Johnson, M. W., Richards, W. A. et al. · Psychopharmacology (2011)
Mithoefer, M. C., Feduccia, A. A., Jerome, L. et al. · Psychopharmacology (2019)
Wagner, M. T., Mithoefer, M. C., Mithoefer, A. T. et al. · Journal of Psychopharmacology (2017)
Bershad, A. K., Miller, M. A., Baggot, M. J. et al. · Journal of Psychopharmacology (2016)
Kamilar-Britt, P., Bedi, G. · Neuroscience and Biobehavioral Reviews (2015)
Elsey, J. W. B. · Drug Science Policy and Law (2017)
Papers in Blossom that reference this study
Low, F., Earleywine, M. · Journal of Psychoactive Drugs (2026)
Kvam, T. M., Goksøyr, I. W., Rog, J. et al. · Journal of Psychiatric Research (2026)