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<article xml:lang="en" article-type="research-article" dtd-version="1.4"><?da-xref-anchor-style autodetect?><processing-meta base-tagset="archiving" mathml-version="3.0" table-model="xhtml" tagset-family="jats"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type="nlm-ta">J Psychopharmacol</journal-id><journal-id journal-id-type="iso-abbrev">J Psychopharmacol</journal-id><journal-id journal-id-type="pmc-domain-id">464</journal-id><journal-id journal-id-type="pmc-domain">sageopen</journal-id><journal-id journal-id-type="nlm-id">8907828</journal-id><journal-id journal-id-type="publisher-id">JOP</journal-id><journal-title-group><journal-title>Journal of Psychopharmacology (Oxford, England)</journal-title></journal-title-group><issn pub-type="ppub">0269-8811</issn><issn pub-type="epub">1461-7285</issn><?publisher_abbrev sage?><custom-meta-group><custom-meta><meta-name>pmc-is-collection-domain</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-collection-title</meta-name><meta-value>Sage Choice</meta-value></custom-meta></custom-meta-group></journal-meta><article-meta><article-id pub-id-type="pmcid">PMC12371141</article-id><article-id pub-id-type="pmcid-ver">PMC12371141.1</article-id><article-id pub-id-type="pmcaid">12371141</article-id><article-id pub-id-type="pmcaiid">12371141</article-id><article-id pub-id-type="pmid">40530602</article-id><article-id pub-id-type="doi">10.1177/02698811251346729</article-id><article-id pub-id-type="publisher-id">10.1177_02698811251346729</article-id><article-version article-version-type="pmc-version">1</article-version><article-categories><subj-group subj-group-type="heading"><subject>Original Papers</subject></subj-group></article-categories><title-group><article-title>Naturalistic use of psychedelics is associated with longitudinal improvements in anxiety and depression during global crisis times</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0002-0562-6139</contrib-id><name name-style="western"><surname>Bălăeţ</surname><given-names initials="M">Maria</given-names></name><xref rid="aff1-02698811251346729" ref-type="aff">1</xref><xref rid="aff2-02698811251346729" ref-type="aff">2</xref><xref rid="corresp1-02698811251346729" ref-type="corresp"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Trender</surname><given-names initials="W">William</given-names></name><xref rid="aff1-02698811251346729" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Lerede</surname><given-names initials="A">Annalaura</given-names></name><xref rid="aff1-02698811251346729" ref-type="aff">1</xref><xref rid="aff2-02698811251346729" ref-type="aff">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Hellyer</surname><given-names initials="PJ">Peter J</given-names></name><xref rid="aff2-02698811251346729" ref-type="aff">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Hampshire</surname><given-names initials="A">Adam</given-names></name><xref rid="aff1-02698811251346729" ref-type="aff">1</xref><xref rid="aff2-02698811251346729" ref-type="aff">2</xref></contrib></contrib-group><aff id="aff1-02698811251346729"><label>1</label>Department of Brain Sciences, Imperial College London, London, UK</aff><aff id="aff2-02698811251346729"><label>2</label>Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK</aff><author-notes><corresp id="corresp1-02698811251346729">Maria Bălăeţ, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK. Email: <email>m.balaet17@imperial.ac.uk</email></corresp></author-notes><pub-date pub-type="epub"><day>18</day><month>6</month><year>2025</year></pub-date><pub-date pub-type="collection"><month>9</month><year>2025</year></pub-date><volume>39</volume><issue>9</issue><issue-id pub-id-type="pmc-issue-id">495395</issue-id><fpage>957</fpage><lpage>967</lpage><pub-history><event event-type="pmc-release"><date><day>22</day><month>08</month><year>2025</year></date></event><event event-type="pmc-live"><date><day>26</day><month>08</month><year>2025</year></date></event><event event-type="pmc-last-change"><date iso-8601-date="2026-04-03 16:25:14.013"><day>03</day><month>04</month><year>2026</year></date></event></pub-history><permissions><copyright-statement>© The Author(s) 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder content-type="sage">British Association for Psychopharmacology</copyright-holder><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/" specific-use="textmining" content-type="ccbylicense">https://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This article is distributed under the terms of the Creative Commons Attribution 4.0 License (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://us.sagepub.com/en-us/nam/open-access-at-sage">https://us.sagepub.com/en-us/nam/open-access-at-sage</ext-link>).</license-p></license></permissions><self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pmc-pdf" xlink:href="10.1177_02698811251346729.pdf"><?pdf-name 10.1177_02698811251346729.pdf?><?pdf-size 1326706?><?pdf-md5 c217c09f1571b0af2f728122c9d57db2?><?pdf-image-server-status NEVER_LOAD?><?pdf-cloudpmc-urn urn:app:2c35/12371141/c217c09f1571/10.1177_02698811251346729.pdf?></self-uri><abstract><sec id="section1-02698811251346729"><title>Background:</title><p>Mental health implications of COVID-19 drug use patterns are still unclear.</p></sec><sec id="section2-02698811251346729"><title>Methods:</title><p>We used data-driven clustering in a large citizen science cohort recruited agnostically to an interest in drug-use to categorise people according to common patterns of drug use and analysed their mental health symptoms (GAD-7 and PHQ-9 items), from recruitment prior to COVID-19 restrictions in 2020 (<italic toggle="yes">N</italic> = 242,260) to three follow-ups in 2020-2022 (<italic toggle="yes">N</italic> = 68,416). Mixed effects modelling examined how mental health scores related to drug-use clusters cross-sectionally and how changes in those scores longitudinally related to changes in consumption frequencies.</p></sec><sec id="section3-02698811251346729"><title>Results:</title><p>We identified six common patterns of drug use during the COVID-19 pandemic, with cannabis cross cutting most of them. The majority of drug use clusters had worse average mental health scores relative to drug-naive individuals at all timepoints. The average mental health scores of those who used more drugs during the pandemic worsened over time relative to individual baselines. However, psychedelics and cannabis users showed average improvements in depression (β = −0.26 SD, 95% CI: −0.44, −0.08, <italic toggle="yes">p</italic> = 0.003), anxiety (β = −0.24 SD, 95% CI: −0.41, −0.06, <italic toggle="yes">p</italic> = 0.007) and overall mental health (β = −0.2 SD, 95% CI: −0.35, −0.04, <italic toggle="yes">p</italic> = 0.01) from pre-pandemic to January 2022, becoming on par with the drug-naive group. This was not the case for cannabis-only users, whose worse mental health scores persisted.</p></sec><sec id="section4-02698811251346729"><title>Conclusion:</title><p>Those who used psychedelics may have experienced some improvements in mental health across the pandemic timeframe, which supports the idea that beneficial effects on mood and anxiety associated with these substances may extend beyond controlled conditions.</p></sec></abstract><kwd-group><kwd>COVID-19</kwd><kwd>mental health</kwd><kwd>psychedelics</kwd><kwd>cannabis</kwd><kwd>depression</kwd><kwd>anxiety</kwd></kwd-group><funding-group specific-use="FundRef"><award-group id="award1-02698811251346729"><funding-source id="funding1-02698811251346729">
<institution-wrap><institution>Medical Research Council</institution><institution-id institution-id-type="FundRef">https://doi.org/10.13039/501100000265</institution-id></institution-wrap>
</funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>pmc-status-qastatus</meta-name><meta-value>0</meta-value></custom-meta><custom-meta><meta-name>pmc-status-live</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-status-embargo</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-status-released</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-open-access</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-olf</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-manuscript</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-legally-suppressed</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-has-pdf</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-has-supplement</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-pdf-only</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-suppress-copyright</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-is-real-version</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-is-scanned-article</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-preprint</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>pmc-prop-in-epmc</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>pmc-license-ref</meta-name><meta-value>CC BY</meta-value></custom-meta><custom-meta><meta-name>typesetter</meta-name><meta-value>ts1</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type="intro" id="section5-02698811251346729"><title>Introduction</title><p>The COVID-19 pandemic significantly disrupted daily life, profoundly affecting mental health (<xref rid="bibr23-02698811251346729" ref-type="bibr">Hampshire et al., 2021a</xref>). Although overall rates of illicit drug use during the pandemic reduced, concurrently, there was a rise in their use as a coping mechanism (<xref rid="bibr6-02698811251346729" ref-type="bibr">Bălăeţ et al., 2025</xref>; <xref rid="bibr40-02698811251346729" ref-type="bibr">Rogers et al., 2020</xref>). However, longitudinal studies on the mental health of people who used illicit drugs spanning from before to after the pandemic restrictions are limited. In the UK, the need for such research is underscored by statistics indicating that 1 in 11 individuals aged 16–59 used drugs other than alcohol or tobacco in the past year (<xref rid="bibr37-02698811251346729" ref-type="bibr">Office for National Statistics, 2023</xref>).</p><p>Of the few studies that specifically examined the relationship of substance use with mental health during the pandemic, most have focused on alcohol, tobacco or cannabis, often grouping other psychoactive substances and overlooking their unique effects (<xref rid="bibr46-02698811251346729" ref-type="bibr">Zolopa et al., 2022</xref>). However, recent efforts have targeted the psychedelics and entactogens associations with mental health outcomes such as mood and resilience, distinct from other drugs (<xref rid="bibr28-02698811251346729" ref-type="bibr">Kopra et al., 2023</xref>). This is timely given the increased interest in the potential use of psychedelics for therapeutic purposes (<xref rid="bibr13-02698811251346729" ref-type="bibr">Carhart-Harris et al., 2021</xref>) and their use among the general population to try and self-manage well-being, including those without severe mental health issues (<xref rid="bibr27-02698811251346729" ref-type="bibr">Keyes and Patrick, 2023</xref>; <xref rid="bibr33-02698811251346729" ref-type="bibr">Livne et al., 2022</xref>). This interest is driven by the therapeutic potential of psychedelics like psilocybin for severe depression (<xref rid="bibr13-02698811251346729" ref-type="bibr">Carhart-Harris et al., 2021</xref>), as well as research suggesting mood-enhancing effects in ‘naturalistic settings’, that is, as they are commonly used in the real world (<xref rid="bibr19-02698811251346729" ref-type="bibr">Forstmann et al., 2020</xref>).</p><p>Research on the relationship between psychedelics use during the COVID-19 pandemic and mental health has produced mixed findings. Some studies, like those by <xref rid="bibr33-02698811251346729" ref-type="bibr">Livne et al. (2022)</xref> and <xref rid="bibr27-02698811251346729" ref-type="bibr">Keyes and Patrick (2023)</xref>, cautioned against potential harms from increasing use, but have received criticisms based on reports of positive associations between psychedelics use during the pandemic with well-being (<xref rid="bibr35-02698811251346729" ref-type="bibr">Morgan, 2023</xref>). The evidence for these positive associations comes from social media-based studies (<xref rid="bibr11-02698811251346729" ref-type="bibr">Bouso et al., 2023</xref>; <xref rid="bibr15-02698811251346729" ref-type="bibr">Cavanna et al., 2021</xref>; <xref rid="bibr38-02698811251346729" ref-type="bibr">Révész et al., 2021</xref>), but contrasts with findings from large-scale surveys in the United Kingdom and the United States (<xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeț et al., 2023b</xref>; <xref rid="bibr34-02698811251346729" ref-type="bibr">Matzopoulos et al., 2021</xref>). These larger surveys indicate negative mental health correlations with psychedelics but a positive association for 3,4-methylenedioxymethamphetamine (MDMA; <xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeț et al., 2023b</xref>), suggesting that assessing MDMA and psychedelics together, often employed by historic surveys, may contribute to inconsistent results. Discrepancies might also stem from recruitment bias, methodological differences and cultural factors. This highlights a need for more detailed longitudinal analyses to better understand mental health dynamics among psychedelic users, non-users, and users of other illicit drugs in the context of real-world stressors like the pandemic.</p><p>Studying drug use in the general population presents several challenges, particularly due to the prevalence of polydrug use; specifically, individuals often use multiple substances across time and may consume them simultaneously (<xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeț et al., 2023b</xref>; <xref rid="bibr17-02698811251346729" ref-type="bibr">de Jonge et al., 2022</xref>). This complicates efforts to isolate and model the effects of individual drugs, as it diverges from common patterns of drug use in the general population. Furthermore, drug use behaviour is dynamic, changing over time, adding another layer of complexity. Addressing these issues requires large-scale population sampling, which gathers data from real-world drug users in the context of environmental stressors and accounts for the varied and evolving drug use patterns observed in everyday life.</p><p>Here, we aim to advance our understanding of the relationship between mental health symptoms and common patterns of illicit drug use in the UK general population during crisis times. Specifically, we analyse online survey data collected from a large citizen science cohort spanning the timeframe from just prior to COVID-19 restrictions being put in place in the UK until early 2022. We test the hypothesis that those who use psychedelics had less frequent depression and anxiety symptoms across timeframe relative to their baselines, and relative to those who used other illicit drugs or who were drug naive.</p></sec><sec sec-type="methods" id="section6-02698811251346729"><title>Methods</title><sec id="section7-02698811251346729"><title>Participant’s recruitment</title><p>Individuals were enrolled through the Great British Intelligence Test (<xref rid="bibr25-02698811251346729" ref-type="bibr">Hampshire, 2020</xref>) via two distinct phases, with outreach conducted via the British Broadcasting Corporation (BBC)’s online platform and a BBC2 Horizon documentary. Initial enrolment spanned from December 2019 to January 2020, followed by a subsequent phase in May 2020; however, the portal for enrolment remained accessible continuously during this interval. Those who opted to share their email addresses at the time of enrolment were approached for follow-up in December 2020 and again in June 2021 and January 2022.</p><p>The main purpose of the Great British Intelligence Test was to understand human cognition and how cognitive performance relates to various health and lifestyle factors. This study was hosted on the Cognitron cognitive testing platform (<xref rid="bibr5-02698811251346729" ref-type="bibr">Bălăeţ et al., 2024</xref>; <xref rid="bibr18-02698811251346729" ref-type="bibr">Del Giovane et al., 2023</xref>; <xref rid="bibr24-02698811251346729" ref-type="bibr">Hampshire et al., 2021b</xref>). The participants were informed that the cognitive testing battery would be paired with a questionnaire surveying health and lifestyle factors. However, no direct mention of the survey containing questions about drug use was made in the recruitment advertisement materials, thus minimising the potential bias of participants volunteering to take part in drug-focused research.</p><p>At recruitment (in 2019–2020) <italic toggle="yes">N</italic> = 95,441 participants made accounts on the Cognitron platform and agreed to be re-contacted for future research participation, with this number growing continuously to <italic toggle="yes">N</italic> = 124,496 by January 2022. Up to one in five of those who consented to be recontacted engaged with at least a single follow-up. No attempt has been made to mitigate the lack of responses from other members of the cohort.</p><p>This study was run in accordance with the Helsinki Declaration of 1975, as revised in 2008. All procedures were approved by the Imperial College Research Ethics Committee (17IC4009). All participants provided informed consent prior to completing the survey.</p></sec><sec id="section8-02698811251346729"><title>Mood self-assessment</title><p>Selected items from the PHQ-9 (<xref rid="bibr29-02698811251346729" ref-type="bibr">Kroenke and Spitzer, 2002</xref>) and the GAD-7 (<xref rid="bibr43-02698811251346729" ref-type="bibr">Spitzer et al., 2006</xref>) were included in each of the analysis timepoints. To reflect mood fluctuations more comprehensively, we requested participants to reflect on their mood over the past month leading up to the survey, extending beyond the original 2-week timeframe of the standard scales. To gain a finer resolution of mood variations, we expanded the scoring scale to capture a broader spectrum of frequency (<xref rid="bibr4-02698811251346729" ref-type="bibr">Bălăeț et al., 2023a</xref>, <xref rid="bibr3-02698811251346729" ref-type="bibr">2023b</xref>; <xref rid="bibr23-02698811251346729" ref-type="bibr">Hampshire et al., 2021a</xref>). Participants rated the occurrence of symptoms over the previous month on a scale ranging from 0 to 6, with the points defined as follows: ‘0-Never’, ‘1-Almost never’, ‘2-Once or twice a week’, ‘3-Several times a week’, ‘4-Daily’, ‘5-Hourly’ and ‘6-More often’.</p><p>These are the items:</p><list list-type="order" id="list1-02698811251346729"><list-item><p>Feeling nervous, anxious or on edge – from GAD-7.</p></list-item><list-item><p>Feeling down or depressed – from PHQ.</p></list-item><list-item><p>Feeling tired or having little energy – from PHQ.</p></list-item><list-item><p>Trouble concentrating on things, such as reading the newspaper or watching television – from PHQ.</p></list-item><list-item><p>Not being able to get to sleep or stay asleep – from PHQ.</p></list-item></list></sec><sec id="section9-02698811251346729"><title>Drug use assessment</title><p>During the months of December 2020, June 2021 and January 2022, participants had the opportunity to provide information regarding their use of drugs classified as illegal within the UK. Depending on their willingness to engage with this aspect of the survey, respondents were categorised into groups:</p><list list-type="simple" id="list2-02698811251346729"><list-item><p>A. Undisclosed: This group includes those who chose not to reveal their past drug use and were not included in subsequent analyses.</p></list-item><list-item><p>B. Drug naive: Participants who indicated that they have never taken recreational drugs other than alcohol and tobacco.</p></list-item><list-item><p>C. Users: This group consists of individuals who acknowledged the use of illicit drugs beyond alcohol and tobacco at some point in their lifetime (specifically, we surveyed use of cannabis, cocaine, heroin/opioids, ayahuasca, psilocybin (magic mushrooms), MDMA/Ecstasy, lysergic acid diethylamide (LSD), 5-methoxy-N,N-dimethyltryptamine, mescaline, N,N-dimethyltryptamine, ketamine and ‘other’). Those who indicated using drugs at some point in their lifetime, but not in 2019 or during the pandemic, were classed as historic users. Those who used drugs up until the pandemic but not during the pandemic were classed as users who stopped using drugs during the pandemic. Further subdivision within this category was executed based on their specific choices of drugs used during the pandemic (2020–2022), utilising k-modes clustering to delineate specific clusters (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Figure S2</ext-link>).</p></list-item></list></sec><sec id="section10-02698811251346729"><title>K-modes clustering</title><p>K-modes clustering was utilised to classify individuals based on their self-reported drug use, offering a data-driven method tailored for categorical data. Each individual is represented by a set of <italic toggle="yes">N</italic> features, encapsulating the types of drugs used during the pandemic and their historical usage patterns. The algorithm assigns each individual to the cluster whose mode is most similar to them, based on a predefined distance metric. In our analysis, the individual drug user serves as the data point, and the features include various drug class choices used in the year prior to the cognitive assessment, coded in binary terms.</p><p>A five-fold cross-validation method was used on an 80% train, 20% test split (<xref rid="bibr21-02698811251346729" ref-type="bibr">Gholamy et al., 2018</xref>) of the drug survey responses across the three follow-ups to identify the ideal number of clusters. The optimal number for the data was identified as six clusters (see <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Materials Figures S1 and S2</ext-link>), and the trained model was used to assign cluster labels to all data points. The clusters were then used as groups in the statistical analysis.</p></sec><sec id="section11-02698811251346729"><title>Statistical assessment</title><p>A factor analysis with one factor was used to define the mental health composite score. This was done with the factor-analyser package in Python (<xref rid="bibr10-02698811251346729" ref-type="bibr">Biggs, 2019</xref>).</p><p>All mental health self-assessment scores were adjusted for the effects of relevant covariates (sociodemographic characteristics – age decade, sex, ethnicity, residence, occupation) and lifestyle choices (reading, meditation and exercise frequency) using linear regression models. Residuals from these models are used for all further analysis. This is consistent with data analysed in previous publications (<xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeţ et al., 2023b</xref>, <xref rid="bibr6-02698811251346729" ref-type="bibr">2025</xref>).</p><p>Mixed effects linear models with cluster as a between-subjects factor and time as a within-subjects factor were used to assess each set of changes in mental health symptom scores from baseline to follow-up independently.</p></sec><sec id="section12-02698811251346729"><title>Permutation analysis description</title><p>Two-way permutational ANOVA was applied to evaluate the effects of cluster, changes in drug use and their interaction on changes in all mental health scores. Changes in drug use and individual scores were evaluated between each follow-up and baseline. To avoid permuting individuals with themselves at another timepoint, changes in mental health scores were permuted across individuals with the same number of follow-up timepoints available. For example, both observations from individuals with two follow-up time points available were permuted only with individuals with two follow-up timepoints available. This approach was similar to what was applied in <xref rid="bibr31-02698811251346729" ref-type="bibr">Lerede et al. (2023)</xref> and enabled controlling for the level of engagement as a potential confounder as well as respecting the hypothesis of independence among observations to permute.</p><p>All analyses were performed using the Python package ‘statsmodel’ (<xref rid="bibr41-02698811251346729" ref-type="bibr">Seabold and Perktold, 2010</xref>).</p></sec></sec><sec sec-type="results" id="section13-02698811251346729"><title>Results</title><sec id="section14-02698811251346729"><title>Study timeline and participation</title><p>Overall, the study engaged <italic toggle="yes">N</italic> = 377,678 unique individuals between December 2019 and March 2022, spanning five distinct phases: two initial advertisement waves (December 2019–May 2020) and three subsequent recontact waves at 6-monthly timepoints following recruitment (December 2020, June 2021, January 2022; (<xref rid="fig1-02698811251346729" ref-type="fig">Figure 1</xref>). We recruited a total of <italic toggle="yes">N</italic> = 242,260 datasets pre-COVID-19 restrictions and <italic toggle="yes">N</italic> = 130,505 during the first months of restrictions initially being put in place. Subsequently, we collected <italic toggle="yes">N</italic> = 68,416 datasets at follow-ups across three timepoints: <italic toggle="yes">N</italic> = 22,533 datasets in December 2020, <italic toggle="yes">N</italic> = 17,172 in June 2021 and <italic toggle="yes">N</italic> = 28,711 in January 2022.</p><fig position="float" id="fig1-02698811251346729" orientation="portrait"><label>Figure 1.</label><caption><p>Study engagement timeline. The log scale histogram represents the number of individuals who completed our survey between late December 2019 and March 2022. The study website was made widely visible by the BBC at two time points from January to February 2020 and May 2020. On 23 March 2020, a lockdown due to the COVID-19 pandemic began in the UK. Participants continued to engage with the recruitment website in the absence of advertisement until the end of 2020, prior to our first follow-up of consenting individuals.</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="10.1177_02698811251346729-fig1.jpg"><?image-name 10.1177_02698811251346729-fig1.jpg?><?image-size 94524?><?image-md5 e5208aec67f4c47f3e00b045f4dba166?><?image-image-server-status LOAD_COMPLETED?><?image-original-height 1323?><?image-original-width 1915?><?image-scaled-height 529?><?image-scaled-width 766?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/e5208aec67f4/10.1177_02698811251346729-fig1.jpg?><?thumb-name 10.1177_02698811251346729-fig1.gif?><?thumb-size 14412?><?thumb-md5 289d7d18d26e6cbe164460ce996e84e1?><?thumb-image-server-status NEVER_LOAD?><?thumb-scaled-height 79?><?thumb-scaled-width 115?><?thumb-cloudpmc-urn urn:cdn:blobs/2c35/12371141/289d7d18d26e/10.1177_02698811251346729-fig1.gif?></graphic></fig></sec></sec><sec id="section15-02698811251346729"><title>Participants clustering</title><p>The K-modes clustering algorithm was used to cluster all recontacted individuals based on their reported patterns of drug use during the COVID-19 pandemic. One participant could only belong to one cluster. Six distinct clusters were identified comprising those who used: cannabis (<italic toggle="yes">N</italic> = 2827), cocaine and cannabis (<italic toggle="yes">N</italic> = 393), cocaine only (<italic toggle="yes">N</italic> = 629), psychedelics and cannabis (<italic toggle="yes">N</italic> = 430), polydrug (<italic toggle="yes">N</italic> = 274) and those indicating ‘other’ (<italic toggle="yes">N</italic> = 283; <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Figure S2</ext-link>). Users of ‘other’ drugs were excluded from subsequent analyses due to the inability to pinpoint what the substances were. The remaining datasets belonged to <italic toggle="yes">N</italic> = 32,491 drug-naive participants, <italic toggle="yes">N</italic> = 10,293 with a history of drug use and <italic toggle="yes">N</italic> = 1420 who stopped using drugs during the pandemic.</p><p>Here, only complete datasets from participants recruited prior to restrictions in the UK and who responded to at least one recontact timepoint (thus completing another set of mental health measures alongside indicating whether they have used drugs during the COVID-19 pandemic) were used in our analyses. A total of <italic toggle="yes">N</italic> = 48,757 follow-up entries were retained, belonging to 30,711 unique individuals who completed at least one recontact. Among them, <italic toggle="yes">N</italic> = 17,354 participants engaged in just one recontact, while <italic toggle="yes">N</italic> = 8668 and 4689 individuals completed two and three recontacts, respectively. These included <italic toggle="yes">N</italic> = 15,591 drug-naive individuals, <italic toggle="yes">N</italic> = 5738 historic users, <italic toggle="yes">N</italic> = 857 individuals who stopped using drugs during the pandemic, <italic toggle="yes">N</italic> = 1515 cannabis users, <italic toggle="yes">N</italic> = 306 cocaine users, <italic toggle="yes">N</italic> = 228 psychedelics and cannabis users, <italic toggle="yes">N</italic> = 210 cocaine and cannabis users and <italic toggle="yes">N</italic> = 136 polydrug users. Sociodemographic characteristics and lifestyle choices of the participants who entered the longitudinal mental health analysis are reported in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Table S1</ext-link>, whereas the cohort sociodemographics at recruitment and cross-sectionally at follow-up have been reported in previous publications (<xref rid="bibr4-02698811251346729" ref-type="bibr">Bălăeț et al., 2023a</xref>, <xref rid="bibr3-02698811251346729" ref-type="bibr">2023b</xref>; <xref rid="bibr23-02698811251346729" ref-type="bibr">Hampshire et al., 2021a</xref>).</p></sec><sec id="section16-02698811251346729"><title>The bigger picture: Longitudinal changes in mental health for different clusters</title><p>Full mixed effects model outputs are reported in the supplement (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental File 1</ext-link>). In summary, at baseline, drug use clusters had generally worse mental health scores relative to drug-naive individuals for all mental health symptoms and across all timepoints. This included historic users and those who stopped using drugs during the pandemic (<xref rid="fig2-02698811251346729" ref-type="fig">Figure 2</xref>).</p><fig position="float" id="fig2-02698811251346729" orientation="portrait"><label>Figure 2.</label><caption><p>Changes in mood self-assessment scores from pre-COVID-19 restrictions to follow-up. Error bars are the standard error of the mean. A full breakdown of numbers per cluster at each recontact is available in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Table S2</ext-link>. Significance for cluster effects is annotated based on cluster predictors in the mixed effects linear model. The significance annotation for cluster effects is black and represents whether there were baseline differences between clusters relative to drug-naïve individuals. Significance for changes over time is annotated based on the significant interactions between time and cluster from the mixed effects linear model results. The significance annotation for interactions is in red and represents whether there have been significant longitudinal changes over time from baseline to follow-up. The significance annotation for group differences at follow-up is based on post hoc Tukey tests where ANOVA indicated an effect of cluster and is represented in blue. This represents differences relative to drug-naïve individuals unless otherwise indicated on the figure. Baseline and follow-up differences are between clusters. Longitudinal differences are within clusters.</p><p>All <italic toggle="yes">p</italic>-values are annotated as follows: *<italic toggle="yes">p</italic> &lt; 0.05. **<italic toggle="yes">p</italic> &lt; 0.01. ***<italic toggle="yes">p</italic> &lt; 0.001.</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="10.1177_02698811251346729-fig2.jpg"><?image-name 10.1177_02698811251346729-fig2.jpg?><?image-size 152042?><?image-md5 d7ed3d282f9b2649336124641b6f0c87?><?image-image-server-status LOAD_COMPLETED?><?image-original-height 2234?><?image-original-width 1996?><?image-scaled-height 893?><?image-scaled-width 798?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/d7ed3d282f9b/10.1177_02698811251346729-fig2.jpg?><?thumb-name 10.1177_02698811251346729-fig2.gif?><?thumb-size 17406?><?thumb-md5 3e2bceb3a074ac67b861164aaa35712e?><?thumb-image-server-status NEVER_LOAD?><?thumb-scaled-height 112?><?thumb-scaled-width 100?><?thumb-cloudpmc-urn urn:cdn:blobs/2c35/12371141/3e2bceb3a074/10.1177_02698811251346729-fig2.gif?></graphic></fig><p>Regarding the psychedelics and cannabis cluster specifically, there was an effect of cluster indicative of worse baseline depression scores (β = 0.23 SD, 95% CI: 0.003, 0.46, <italic toggle="yes">p</italic> = 0.04) and worse baseline concentration scores (β = 0.34 SD, 95% CI: 0.10, 0.57, <italic toggle="yes">p</italic> = 0.003) for those who responded in December 2020 relative to drug-naive individuals. Psychedelics and cannabis users who responded in June 2021 had better baseline insomnia scores (β = −0.28 SD, 95% CI: −0.56, −0.004, <italic toggle="yes">p</italic> = 0.04) than drug-naive individuals, while psychedelics and cannabis users who responded in January 2022 had worse depression scores (β = 0.35 SD, 95% CI: 0.16, 0.54, <italic toggle="yes">p</italic> &lt; 0.001) and worse mental health composite scores (β = 0.29 SD, 95% CI: 0.10, 0.48, <italic toggle="yes">p</italic> = 0.002) than drug-naive individuals. Cannabis-only users, irrespective of the timepoint they engaged with a follow-up, had on average worse baseline depression, concentration, anxiety and mental health composite scores relative to drug-naïve individuals. All average mental health scores of cannabis-only users who responded in January 2022 were worse at baseline relative to drug-naïve individuals.</p><p>For depression scores, there was a significant interaction for historic users (β = 0.06 SD, 95% CI: 0.011, 0.014, <italic toggle="yes">p</italic> = 0.01) who had worse depression scores in January 2022 and psychedelics and cannabis users who had better depression scores in January 2022 (β = −0.26 SD, 95% CI: −0.44, −0.08, <italic toggle="yes">p</italic> = 0.003). In January 2022, there was also a significant interaction for psychedelics and cannabis users who had better anxiety scores (β = −0.24 SD, 95% CI: −0.41, −0.06, <italic toggle="yes">p</italic> = 0.007). For the composite mental health score, in January 2022, there was a significant interaction between timepoint and the psychedelics and cannabis cluster (β = −0.2 SD, 95% CI: −0.35, −0.04, <italic toggle="yes">p</italic> = 0.01). This was not the case for those who only used cannabis during the same period of time, who presented with no longitudinal changes in any of the variables.</p><p>ANOVA and Tukey post hoc tests were carried out to investigate differences between clusters at follow-up. The main cluster of interest, psychedelics and cannabis, showed worse concentration scores (mean difference = 0.48 SD, 95% CI: 0.11, 0.85, <italic toggle="yes">p</italic> = 0.002) and worse composite mental health scores (mean difference = 0.39 SD, 95% CI: 0.02, 0.76, <italic toggle="yes">p</italic> = 0.02) relative to drug-naive individuals in December 2020. They were no different relative to drug-naive individuals in June 2021 or January 2022 in any mental health symptoms. However, in January 2022, cannabis-only users were significantly worse than drug-naive individuals for every mental health symptom. Full results are reported in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental File 2</ext-link>.</p></sec><sec id="section17-02698811251346729"><title>Changes in frequency of use across drug use clusters</title><p>At all timepoints, most individuals who used drugs during the pandemic reported no change in frequency of use from their pre-restriction levels. Approximately one in four individuals reported using drugs less frequently, and one in five reported using them more frequently. Distinct patterns were observed for different drug use clusters at different timepoints, which differed from one another. Just above half of psychedelics and cannabis users reported no change from their baseline levels, with the rest being almost evenly split between using less and using more, a pattern observed at all follow-up timepoints.</p></sec><sec id="section18-02698811251346729"><title>Relating changes in mental health symptoms to changes in the frequency of drug use</title><p>Finally, we examined whether there were differences in the changes from pre-restrictions to follow-up in the different mental health symptoms deltas (difference in scores between pre-restrictions to follow-up) based on users’ self-reported change in frequency of drug use from pre-pandemic times to follow-up. Positive deltas indicate a worsening of scores, whereas negative deltas indicate an improvement in scores (<xref rid="fig3-02698811251346729" ref-type="fig">Figure 3</xref>). Segregation by drug cluster is reported in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Materials</ext-link>.</p><fig position="float" id="fig3-02698811251346729" orientation="portrait"><label>Figure 3.</label><caption><p>Self-reported change in drug use frequency from pre-restrictions to follow-up. Proportions of respondents who indicated using less drugs, more drugs or no change in their drug use frequency are represented for each of the clusters at follow-up (a–c) and overall (d).</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="10.1177_02698811251346729-fig3.jpg"><?image-name 10.1177_02698811251346729-fig3.jpg?><?image-size 50959?><?image-md5 36919ef9c4c8cc9c9bc56c99ab5a8e60?><?image-image-server-status LOAD_COMPLETED?><?image-original-height 568?><?image-original-width 1920?><?image-scaled-height 227?><?image-scaled-width 768?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/36919ef9c4c8/10.1177_02698811251346729-fig3.jpg?><?thumb-name 10.1177_02698811251346729-fig3.gif?><?thumb-size 12830?><?thumb-md5 42eae3a5187a30b0d401b62520f28fd4?><?thumb-image-server-status NEVER_LOAD?><?thumb-scaled-height 59?><?thumb-scaled-width 200?><?thumb-cloudpmc-urn urn:cdn:blobs/2c35/12371141/42eae3a5187a/10.1177_02698811251346729-fig3.gif?></graphic></fig><p>Results from the two-way permutational ANOVA (<xref rid="fig4-02698811251346729" ref-type="fig">Figure 4</xref>) showed that changes in composite mental health were associated with changes in drug use, regardless of cluster, evidenced by a significant main effect of change (<italic toggle="yes">F</italic> = 7.03, <italic toggle="yes">p</italic> = 0.001), but not of cluster or an interaction between change and cluster. For depression, there was a significant main effect of change (<italic toggle="yes">F</italic> = 4.78, <italic toggle="yes">p</italic> = 0.01), but not of cluster or interaction. For anxiety, there was a significant main effect of change (<italic toggle="yes">F</italic> = 3.54, <italic toggle="yes">p</italic> = 0.04), but the main effect of cluster and the interaction term were not significant. For concentration, the main effect of change was significant (<italic toggle="yes">F</italic> = 6.56, <italic toggle="yes">p</italic> = 0.001), but not of cluster or interaction. For tiredness, there was a significant main effect of change (<italic toggle="yes">F</italic> = 9.07, <italic toggle="yes">p</italic> &lt; 0.001) and a significant interaction (<italic toggle="yes">F</italic> = 2.49, <italic toggle="yes">p</italic> = 0.01), but not of cluster. For insomnia, there were no significant main effects of change, cluster or interaction.</p><fig position="float" id="fig4-02698811251346729" orientation="portrait"><label>Figure 4.</label><caption><p>Differences in changes from pre-restrictions to post-restrictions based on changes in drug use. Error bars are the 95% confidence interval. A breakdown of numbers per cluster per change is available in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Materials Table S3</ext-link>.</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="10.1177_02698811251346729-fig4.jpg"><?image-name 10.1177_02698811251346729-fig4.jpg?><?image-size 55821?><?image-md5 93a1d17dd688a48210481ab9d060f589?><?image-image-server-status LOAD_COMPLETED?><?image-original-height 993?><?image-original-width 1667?><?image-scaled-height 397?><?image-scaled-width 666?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/93a1d17dd688/10.1177_02698811251346729-fig4.jpg?><?thumb-name 10.1177_02698811251346729-fig4.gif?><?thumb-size 13477?><?thumb-md5 07be35b0e01f625b8f1be057b68f800f?><?thumb-image-server-status NEVER_LOAD?><?thumb-scaled-height 80?><?thumb-scaled-width 134?><?thumb-cloudpmc-urn urn:cdn:blobs/2c35/12371141/07be35b0e01f/10.1177_02698811251346729-fig4.gif?></graphic></fig><p><ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journals.sagepub.com/doi/suppl/10.1177/02698811251346729" ext-link-type="uri">Supplemental Figure S4</ext-link> illustrates the split per cluster.</p></sec><sec id="section19-02698811251346729"><title>Zooming in on the psychedelics and cannabis cluster changes in drug use levels and mental health symptoms</title><p>There was no statistically significant difference in the number of participants who used psychedelics and cannabis during the COVID-19 pandemic and indicated a different type of change relative to their self-perceived baseline before the pandemic (<xref rid="fig5-02698811251346729" ref-type="fig">Figure 5(a)</xref>). A two-way ANOVA analysis revealed a main effect of change in June 2021 (<italic toggle="yes">F</italic>(2, 288) = 20.54, <italic toggle="yes">p</italic> &lt; 0.001) and a main effect of change in January 2022 (<italic toggle="yes">F</italic>(2, 624) = 6.72, <italic toggle="yes">p</italic> &lt; 0.001) but no effect of mental health score, nor an interaction between the two at any timepoints. Those who used more by June 2021 had worse mental health scores, whereas those who used less by January 2022 had better scores at that time (<xref rid="fig5-02698811251346729" ref-type="fig">Figure 5(b)</xref>). Longitudinally, those who used more drugs during the pandemic had worse mental health scores in December 2020 and June 2021 relative to their baseline scores, whereas those who used less had better scores in January 2022 relative to their baseline scores (<xref rid="fig5-02698811251346729" ref-type="fig">Figure 5(c)</xref>).</p><fig position="float" id="fig5-02698811251346729" orientation="portrait"><label>Figure 5.</label><caption><p>Changes in use and mental health scores of psychedelics and cannabis users. (a) Distribution of changes in use in the participants who used psychedelics and cannabis during the COVID-19 pandemic. (b) Mental health score by changes at each timepoint for those who used psychedelics and cannabis during the COVID-19 pandemic. (c) Changes from baseline to follow-up within different change groups for psychedelics and cannabis users. All error bars are standard errors of the mean.</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="10.1177_02698811251346729-fig5.jpg"><?image-name 10.1177_02698811251346729-fig5.jpg?><?image-size 117833?><?image-md5 d7aad542e6c39662ce93838a4413c947?><?image-image-server-status LOAD_COMPLETED?><?image-original-height 1233?><?image-original-width 1920?><?image-scaled-height 493?><?image-scaled-width 768?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/d7aad542e6c3/10.1177_02698811251346729-fig5.jpg?><?thumb-name 10.1177_02698811251346729-fig5.gif?><?thumb-size 16973?><?thumb-md5 a89b16caf8a8a8bcee84923f314b9e3b?><?thumb-image-server-status NEVER_LOAD?><?thumb-scaled-height 80?><?thumb-scaled-width 124?><?thumb-cloudpmc-urn urn:cdn:blobs/2c35/12371141/a89b16caf8a8/10.1177_02698811251346729-fig5.gif?></graphic></fig></sec><sec sec-type="discussion" id="section20-02698811251346729"><title>Discussion</title><p>This study examined how mental health evolved over time in relation to different patterns of illicit drug use during the COVID-19 pandemic within a large citizen science cohort. We identified six distinct clusters of drug users, with cannabis only emerging as the most frequent pattern, followed by cocaine, cocaine and cannabis, psychedelics and cannabis and polydrug use. These clusters are consistent with established naturalistic drug user behaviours (<xref rid="bibr22-02698811251346729" ref-type="bibr">Hakkarainen et al., 2019</xref>) and have been replicated using different algorithms (<xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeț et al., 2023b</xref>). They also match the official order of prevalence of substances being used in the UK during the period of time the study ran (<xref rid="bibr37-02698811251346729" ref-type="bibr">Office for National Statistics, 2023</xref>). Together, these aspects underscore the ecological validity of using a data-driven clustering approach for studying associations between drug use and other variables.</p><p>Significant differences in mental health scores were observed before the pandemic between those who went on to use drugs during the pandemic and drug-naive individuals. These findings are consistent with the literature suggesting drug users have generally worse mental health than non-users (<xref rid="bibr44-02698811251346729" ref-type="bibr">Torrens et al., 2011</xref>). We also found that those who increased their drug use frequency during the pandemic, irrespective of what drugs they used more of, had worse mental health scores during the pandemic. Previous research has shown a direct relationship between the quantity of drug use and more severe mental health issues (<xref rid="bibr39-02698811251346729" ref-type="bibr">Ringen et al., 2008</xref>).</p><p>Mental health in most drug use clusters remained stable over time, except for the psychedelics and cannabis cluster. At follow-up, this cluster showed significant within-subject improvements. Comparing pre-restrictions data to January 2022, individuals in this group had significantly worse depression and mental health composite scores than drug-naive individuals at baseline, but these differences diminished over time, with no significant differences relative to drug-naive individuals remaining at follow-up. Anxiety scores also dropped significantly in this cluster, though the differences from drug-naive individuals did not reach statistical significance at either baseline or follow-up. Further analyses suggest this might be due to individuals in this cluster generally using fewer drugs by January 2022 relative to their pre-pandemic baseline. By contrast, cannabis-only users consistently showed poorer mental health across all symptoms compared to drug-naive individuals, suggesting the change in mental health scores might be related to the additional use of psychedelics within that cluster.</p><p>This observation accords with previous findings that link naturalistic use of psychedelics to improved mental health (<xref rid="bibr19-02698811251346729" ref-type="bibr">Forstmann et al., 2020</xref>), and is consistent with <xref rid="bibr11-02698811251346729" ref-type="bibr">Bouso et al. (2023)</xref>, a longitudinal analysis carried out during the pandemic on psychedelics users. Improvements in mental health have also been reported in within-subject design studies of psychedelics users in naturalistic settings (<xref rid="bibr36-02698811251346729" ref-type="bibr">Nygart et al., 2022</xref>). Our analysis also adds further nuance to the findings previously presented in <xref rid="bibr3-02698811251346729" ref-type="bibr">Bălăeț et al. (2023b)</xref>; specifically, in that study, we found that those who went on to use drugs during the peak of the COVID-19 pandemic, including those in the psychedelics and cannabis cluster, had worse mental health scores than drug-naive individuals during that time. Here, we illustrate that while cross-sectionally psychedelics and cannabis users indeed had worse mental health before and during the peak of the pandemic, in a within-subjects design, there were improvements, and an eventual normalisation of mental health scores to be on par with drug-naive individuals by 2022, when most restrictions were already lifted in the UK.</p><p>There are several pertinent explanations for this result. At the population level, drug users have worse mental health than drug-naive individuals – and it could be that (novel) use of psychedelics, albeit less drug use more generally, during crisis times, normalises those differences. Another possible explanation is that the context is more influential in driving the effects of psychedelics than it is in driving the effects of other drugs. We note that related to the early stages of the pandemic, when restrictions and uncertainty were more severe in the UK, the participants in the psychedelics and cannabis cluster who used more drugs had worse mental health scores at those timepoints relative to their pre-restrictions baselines. This contrasts with participants surveyed later on, when restrictions were more relaxed and a lot of things started returning to normal, in January 2022, who had similar mental health scores to their pre-restrictions baselines, despite using more drugs. This explanation is in line with previous work discussing increased suggestibility during the psychedelic experiences (<xref rid="bibr14-02698811251346729" ref-type="bibr">Carhart-Harris et al., 2018</xref>), the importance of set and setting (<xref rid="bibr26-02698811251346729" ref-type="bibr">Hartogsohn, 2017</xref>) and how the general societal discourse could affect users (<xref rid="bibr1-02698811251346729" ref-type="bibr">Bălăeţ, 2024</xref>; <xref rid="bibr12-02698811251346729" ref-type="bibr">Bunce, 1979</xref>). A potential mechanistic explanation for the improvements observed in the psychedelics and cannabis cluster may also relate to the longer interval between baseline and follow-up when these improvements were noted (approximately 2 years, compared to 1 or 1.5 years in the other analyses). This extended timeframe could imply either that psychedelic use occurred more proximally to the point of survey, coinciding with a relatively stable post-pandemic context conducive to more positive outcomes, or that sufficient time had elapsed since earlier use to allow for psychological integration, thereby supporting longer-term improvements in mental health.</p><p>Alternative explanations should also be considered. One possibility is regression to the mean – a statistical phenomenon in which extreme scores at one timepoint tend to move closer to the population average upon subsequent measurement. Given the relatively poorer mental health at baseline in the psychedelics and cannabis cluster, and the apparent normalisation of these measures at follow-up, it is plausible that some of the observed improvement reflects this artefact rather than a specific effect of psychedelic use. Another non-causal interpretation is that individuals whose mental health was already improving in the aftermath of the COVID-19 pandemic may have been more inclined to experiment with psychedelics nearer to the time of the final survey. However, as the timing of psychedelic use was not recorded, it is not possible to determine whether improvements in mental health preceded or followed psychedelic use. Additional explanations include selective engagement with research, whereby participants with less positive experiences may have been less likely to remain engaged with the study, or the influence of unmeasured third variables that independently contributed to improved outcomes.</p><p>As our data-driven clustering reveals, virtually all people who used psychedelics also used cannabis during the pandemic, making it impractical to study the association of naturalistic psychedelics use with mental health in isolation. Looking at the cannabis cluster during the pandemic’s peak revealed notably worse mental health scores compared to drug-naive individuals across multiple symptoms, including anxiety, concentration, insomnia and tiredness, though the average mental health of this group did not significantly change over time. The associations between cannabis use and mental health are widely debated – most notably, there is evidence that it can amplify anxiety and paranoia in users while it is also being clinically prescribed for treating anxiety (<xref rid="bibr45-02698811251346729" ref-type="bibr">Van Ameringen et al., 2020</xref>). Furthermore, there is evidence to suggest that heavy cannabis use may increase the risk of developing depression (<xref rid="bibr32-02698811251346729" ref-type="bibr">Lev-Ran et al., 2014</xref>). Conversely, experimental findings show conflictual evidence that cannabis users typically exhibit blunted reactivity to negative emotions (<xref rid="bibr42-02698811251346729" ref-type="bibr">Somaini et al., 2012</xref>) and stress (<xref rid="bibr16-02698811251346729" ref-type="bibr">Cuttler et al., 2017</xref>), which, in turn, might help them cope with such symptoms. Evidence suggests consuming cannabis at the same time as psychedelics may make psychedelic experiences more intense in a dose-dependent fashion (<xref rid="bibr30-02698811251346729" ref-type="bibr">Kuc et al., 2022</xref>), which may explain potential negative associations between combined use and worse mental health. However, since we do not have information on whether the use of psychedelics and cannabis occurred simultaneously or asynchronously across our cohort, it is not possible to firmly draw such conclusions. Intense experiences may provide grounds for improved mental health with adequate integration (<xref rid="bibr20-02698811251346729" ref-type="bibr">Gashi et al., 2021</xref>), or benefits from psychedelics use may be independent of cannabis use during the same timeframe. Given the prevalence of combined psychedelics and cannabis use, a timely challenge for future studies is to determine how these drugs interact with each other and their effects on mental health, which may only be achievable under prospective study designs with sufficient power to map out nuanced drug use patterns.</p><p>Observational studies of naturalistic drug use present a cost-effective opportunity for understanding the associations between drug use and mental health outcomes and could help inform studies aimed at developing these substances as therapies, which are otherwise logistically challenging to carry out (<xref rid="bibr2-02698811251346729" ref-type="bibr">Bălăeţ, 2025</xref>; <xref rid="bibr7-02698811251346729" ref-type="bibr">Barnett, 2025</xref>; <xref rid="bibr8-02698811251346729" ref-type="bibr">Barnett et al., 2022</xref>, <xref rid="bibr9-02698811251346729" ref-type="bibr">2025</xref>). Strengths of our study include that we addressed the relationship between recreational drug use and mental health in unsupervised settings while minimising the selection bias common when recruiting in drug-related social media forums. This was achieved through a recruitment approach that did not mention drug-related questions and avoided promotion on drug-centric social media channels. The participant group was diverse, including drug-naive individuals, historic users, and those who stopped using drugs during the COVID-19 pandemic; although diversity in sociodemographic characteristics, expectedly, dwindled with repeated follow-up. The methodology was adapted to recognise the complexity of drug use behaviours, including the concurrent use of cannabis with other substances, and to reflect the dynamic nature of drug use patterns where participants increased or decreased their use. Our study also has limitations. As data collection was fully automated online, we did not conduct the sorts of interviews that are sometimes used to provide comprehensive baseline data on participants’ drug use histories, which limits our ability to assess the influence of prior drug use on mental health. For instance, we did not collect data pertaining to dosage, frequency or context of drug use, which are likely important in determining mental health outcomes, nor have we gathered specific information on other drugs individuals may use in the UK, such as amphetamines. We also did not control for potential confounding effects of psychiatric medications that participants might have taken. Furthermore, participants who engaged in the study at multiple timepoints may be less likely to represent all patterns of naturalistic drug use as some sub-populations, for example, those who have addictions or are otherwise marginalised, may be less likely to engage in research. The focus of the analysis was solely on illicit drugs (in the UK), omitting licit substances like alcohol and tobacco – those data will form the basis of future analyses. Finally, the sample predominantly comprised British participants, which may limit the generalisability of the findings.</p><p>Taken together, our findings demonstrate that, on average, individuals who use drugs report poorer mental health compared to drug-naïve individuals, and these average differences remained relatively stable from before the COVID-19 restrictions to the years following their implementation. Notably, the cluster comprising psychedelics and cannabis use exhibited distinct temporal changes, with average anxiety and depression scores improving from pre-restriction baselines to the final follow-up. Additionally, we observed that increases in drug use were associated with declines in mental health over time, regardless of the specific substances involved. It is important to recognise that these results reflect population-level trends and do not account for individual variations in mental health trajectories. Future research should investigate whether the observed changes in mental health within the psychedelics and cannabis cluster are driven by alterations in the use of cannabis, psychedelics or their combined effects, particularly given their prevalent concurrent use; or whether they are a product of other synergistic or independent factors (such as the quality of interpersonal relationships, concurrent treatment for mood disorders or lifestyle changes).</p></sec><sec id="section21-02698811251346729" sec-type="supplementary-material"><title>Supplemental Material</title><supplementary-material id="suppl1-02698811251346729" position="float" content-type="local-data" orientation="portrait"><caption><title>sj-docx-1-jop-10.1177_02698811251346729 – Supplemental material for Naturalistic use of psychedelics is associated with longitudinal improvements in anxiety and depression during global crisis times</title></caption><media xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="sj-docx-1-jop-10.1177_02698811251346729.docx" position="float" orientation="portrait"><?suppdata-name sj-docx-1-jop-10.1177_02698811251346729.docx?><?suppdata-size 323996?><?suppdata-md5 187791b32474275166ac2f972502058d?><?suppdata-image-server-status NEVER_LOAD?><?suppdata-mime-type application?><?suppdata-mime-sub-type vnd.openxmlformats-officedocument.wordprocessingml.document?><?suppdata-cloudpmc-urn urn:app:2c35/12371141/187791b32474/sj-docx-1-jop-10.1177_02698811251346729.docx?></media><p>Supplemental material, sj-docx-1-jop-10.1177_02698811251346729 for Naturalistic use of psychedelics is associated with longitudinal improvements in anxiety and depression during global crisis times by Maria Bălăeţ, William Trender, Annalaura Lerede, Peter J Hellyer and Adam Hampshire in Journal of Psychopharmacology</p></supplementary-material><supplementary-material id="suppl2-02698811251346729" position="float" content-type="local-data" orientation="portrait"><caption><title>sj-txt-2-jop-10.1177_02698811251346729 – Supplemental material for Naturalistic use of psychedelics is associated with longitudinal improvements in anxiety and depression during global crisis times</title></caption><media xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="sj-txt-2-jop-10.1177_02698811251346729.txt" position="float" orientation="portrait"><?suppdata-name sj-txt-2-jop-10.1177_02698811251346729.txt?><?suppdata-size 70490?><?suppdata-md5 de4356c4e023c30d867b5259ae63c3c9?><?suppdata-image-server-status NEVER_LOAD?><?suppdata-mime-type text?><?suppdata-mime-sub-type plain?><?suppdata-cloudpmc-urn urn:app:2c35/12371141/de4356c4e023/sj-txt-2-jop-10.1177_02698811251346729.txt?></media><p>Supplemental material, sj-txt-2-jop-10.1177_02698811251346729 for Naturalistic use of psychedelics is associated with longitudinal improvements in anxiety and depression during global crisis times by Maria Bălăeţ, William Trender, Annalaura Lerede, Peter J Hellyer and Adam Hampshire in Journal of Psychopharmacology</p></supplementary-material></sec></body><back><ack><p>All authors would like to acknowledge our participants who contributed their valuable time to our study.</p></ack><fn-group><fn fn-type="con"><p><bold>Author contribution:</bold> Conceptualisation, Data preprocessing, Data Analysis, Writing original draft, Review: MB. Conceptualisation, Data preprocessing, Data collection, Review: WT. Data collection, Review: PJH. Conceptualisation, Data collection, Review: AH. Data analysis, Review: AL.</p></fn><fn fn-type="other"><p><bold>Data availability statement:</bold> Data sharing inquiries should be directed to Prof. Adam Hampshire: <email>adam.hampshire@kcl.ac.uk</email>.</p></fn><fn fn-type="COI-statement"><p>The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AH is the owner and director of Future Cognition LTD and H2 Cognitive Designs LTD, which support online studies and develop custom cognitive assessment software. PJH is co-owner and director of H2 Cognitive Designs LTD and reports personal fees from H2 Cognitive Designs LTD outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. WT is employed by H2 Cognitive Designs LTD.</p></fn><fn fn-type="financial-disclosure"><p><bold>Funding:</bold> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: MB was supported by the Medical Research Council Doctorate Training Programme at Imperial College London whilst carrying out this research project. WT was supported by the EPSRC Centre for Doctoral Training in Neurotechnology. PJH is, in part, supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.</p></fn><fn fn-type="other"><p><bold>ORCID iD:</bold> Maria Bălăeţ <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10.1177_02698811251346729-img1.jpg"><?image-name 10.1177_02698811251346729-img1.jpg?><?image-size 8220?><?image-md5 9397bb13c6e10494b2c8267029f0c77a?><?image-image-server-status NEVER_LOAD?><?image-cloudpmc-urn urn:cdn:blobs/2c35/12371141/9397bb13c6e1/10.1177_02698811251346729-img1.jpg?></inline-graphic>
<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://orcid.org/0000-0002-0562-6139" ext-link-type="uri">https://orcid.org/0000-0002-0562-6139</ext-link></p></fn><fn fn-type="supplementary-material"><p><bold>Supplemental material:</bold> Supplemental material for this article is available online.</p></fn></fn-group><ref-list><title>References</title><ref id="bibr1-02698811251346729"><mixed-citation publication-type="journal">
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