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• Published: 22 April 2024

Genetic insights into agronomic and morphological traits of drug-type cannabis revealed by genome-wide association studies

• Maxime de Ronne 1 , 2 , 3 , 4 ,
• Éliana Lapierre 1 , 2 , 3 , 4 &
• Davoud Torkamaneh 1 , 2 , 3 , 4  

Scientific Reports volume  14 , Article number:  9162 ( 2024 ) Cite this article

Metrics details

• Biotechnology
• Computational biology and bioinformatics
• Plant sciences

Cannabis sativa L., previously concealed by prohibition, is now a versatile and promising plant, thanks to recent legalization, opening doors for medical research and industry growth. However, years of prohibition have left the Cannabis research community lagging behind in understanding Cannabis genetics and trait inheritance compared to other major crops. To address this gap, we conducted a comprehensive genome-wide association study (GWAS) of nine key agronomic and morphological traits, using a panel of 176 drug-type Cannabis accessions from the Canadian legal market. Utilizing high-density genotyping-by-sequencing (HD-GBS), we successfully generated dense genotyping data in Cannabis , resulting in a catalog of 800 K genetic variants, of which 282 K common variants were retained for GWAS analysis. Through GWAS analysis, we identified 18 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These findings lay a solid foundation for an innovative cannabis research, leveraging genetic markers to inform breeding programs aimed at meeting diverse needs in the industry.

Introduction

Cannabis ( Cannabis sativa L.), an annual and dioecious plant species belonging to the Cannabaceae family, stands as one of the earliest domesticated plants. Its rich history is intertwined with the socioeconomic and cultural development of human societies 1 , 2 . This versatile crop has served a multitude of purposes, offering valuable fibers for ropes and nets, abundant production of protein- and oil-rich seeds, applications in traditional medicine dating back to approximately 8000 BCE, and psychoactive properties 3 . Here, when referring to the plant, we will use its scientific genus name, Cannabis . In Canada, the trajectory of Cannabis cultivation took a significant turn, transitioning from a 1920s prohibition to the legalization of hemp cultivation in 1998, followed by the authorization of medical use in 2001 and recreational use in 2018 4 , 5 . Despite the fact that Cannabis is known to produce over 545 potentially bioactive secondary metabolites 6 , in Canada, the USA and the Europe, it is legally categorised based on the concentration of a single cannabinoid, the Δ 9 -tetrahydrocannabinol (THC), present in the trichomes of female flowers 7 . Cannabis plants with less than 0.3% total THC are classified as hemp-type, while those with greater than 0.3% total THC (calculated as (Tetrahydrocannabinolic acid × 0.877) + THC) are labeled as drug-type Cannabis . The shift in legislation has fueled the development of diverse industries, significantly contributing to Canada's gross domestic product (GDP) and job market, injecting approximately $43.5 billion into the economy and creating over 151,000 jobs in four years (2018–2022) 8 . The historical and societal significance of Cannabis is undeniable, and recent changes in legislation worldwide have propelled it into the forefront of scientific investigation, research and development 9 . Since the discovery of THC in 1964, extensive efforts have been made to characterize the metabolome of hundreds of Cannabis plants, leading to discovery of over 150 terpenoids, 120 cannabinoids and various flavonoids 10 , 11 . Likewise, there have been substantial strides in unraveling the Cannabis genome and creating a worldwide C. sativa genomics resource 3 , 12 , 13 , 14 . Notably, significant progress in Cannabis genome assembly has been achieved through the utilization of long-read sequencing technologies (i.e., PacBio and Oxford Nanopore Technologies) coupled with scaffold anchoring with genetic linkage maps and the integration of Hi-C data. These advancements have led to the development of four chromosome-level assemblies 15 , 16 . Among them, the cs10 v2 assembly (GenBank acc. no. GCA_900626175.2 ) is considered as the most complete and has been proposed as the reference genome for Cannabis by the International Cannabis Research Consortium (ICGRC) 17 . In this assembly, the C. sativa has been estimated to be around 875.7 Mb, characterized by a pair of sex chromosomes and nine autosomes, comprising 31,170 annotated genes 13 . The de novo assembly of Cannabis genomes was fraught with challenges due to a substantial level of heterozygosity (ranging from approximately 12.5–40.5%), and a remarkable abundance of repetitive elements, accounting for roughly 70% of the genome 3 . The in-depth characterization of the metabolome and genome of C. sativa provided new opportunities for medical research, industrial growth and the development of modern agronomic practices.

Despite progress such as increasing cannabinoid concentration, the twentieth century prohibition of Cannabis has hindered its cultivation from fully benefiting from the tools introduced during the Green Revolution 5 . For many years, Cannabis breeding occurred in clandestine operations, relying on undocumented methods and a dearth of modern technologies. Similar to other high-value crops, modern breeding technologies hold the promise of enhancing Cannabis traits to meet diverse needs, spanning manufacturing, medicinal, recreational, and culinary uses 18 . The cannabis research community is hugely undersized and suffers from a scarcity of understanding of Cannabis genetics and how key traits are expressed or inherited 19 . Thus, a better understanding of the genetic basis of agronomic and morphological traits of drug-type Cannabis appears to be a prerequisite for the development of improved Cannabis varieties, optimizing cultivation practices, and conserving valuable genetic resources 3 .

The advent of next-generation sequencing technology (NGS) 20 , which offers cost-effective high-throughput sequencing, coupled with the availability of powerful bioinformatic tools 21 , 22 , have facilitated the widespread adoption of genotype–phenotype association studies to investigate the relationship between genetic variation and phenotypic traits for a wide range of crops 23 . Recent classic quantitative trait loci (QTL) mapping studies have enabled identification of maturity-related QTL in both hemp 24 and drug-type Cannabis 25 . Classic QTL mapping analysis defines molecular markers linked to a phenotype segregating within parental lines, in contrast to modern genome-wide association studies (GWAS) which identify loci related to phenotypes within large populations of unrelated individuals 23 . GWAS use the information of linkage disequilibrium (LD) between a QTL and neighboring genetic markers to identify the regions on the genome that influence traits. However, when applied to a large set of individuals, the sequencing cost remains the most limiting factor, especially in heterozygous organisms like Cannabis where a high sequencing depth per sample is needed to accurately determine genotypes 26 . To address this challenge, cost-effective high-throughput genotyping methods (e.g., restriction-site associated DNA sequencing (RAD-Seq) 27 , genotyping-by-sequencing (GBS) 28 and High-Density GBS (HD-GBS) 29 , based on reduced-representation sequencing approaches (RRS) 30 , have been developed. Recent GWAS studies in hemp-type Cannabis 31 , 32 , 33 to investigate fiber quality, flowering time and sex determination and drug-type Cannabis 34 to investigate genetic basis of terpenes have enabled identification of significant genetic markers. The newly identified QTL will enable the early selection of promising individuals through marker-assisted selection (MAS) 35 , thereby reducing the labor and costs associated with development of improved varieties. Genetic association studies are, therefore, of significant value in advancing breeding programs towards molecular approaches 23 .

While flowering time and sex determination have been focal points in Cannabis breeding, the genetic basis of other important agronomic traits (e.g., yield, height, days to maturity, etc.) remain largely unexplored. Morphological traits should be duly considered due to their established intercorrelations with yield, maturity and cannabinoid profiles 36 . For instance, Cannabis plants cultivated for medicinal and recreational application exhibit shorter stature, have thinner stems, more nodes, higher floral density, and a different cannabinoid profiles compared to industrial hemp plants 37 . On the other hand, genetic backgrounds that prioritize yield may negatively impact THC production, and vice versa 36 . Investigating genetic variations associated with agronomic and morphological traits is essential for establishing the genetic groundwork for developing tailor-made Cannabis varieties, along with breeding tools such as MAS and genomic selection (GS) 38 .

To facilitate the development of molecular tools for Cannabis breeders and researchers, the present study provides high-value markers linked to essential agronomic and morphological traits, identified through GWAS conducted on 176 drug-type Cannabis accessions from the Canadian legal market. Markers associated with essential traits were identified using the multi-locus statistical method Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) 39 . In summary, this study lays the groundwork for a comprehensive understanding of the genetic foundations underpinning the agronomic and morphological traits in Cannabis . The markers identified through this research promise to significantly expedite breeding efforts, empowering us to cultivate Cannabis varieties optimized for various purposes and applications.

Experimental procedures

Plant material and phenotyping data.

All research activities, including the procurement and cultivation of Cannabis plants, were executed in accordance with our Cannabis research license (LIC-QX0ZJC7SIP-2021) and in full compliance with Health Canada’s regulations. In total, in this study, we used 176 drug-type accessions each accompanied by phenotyping data sourced from Lapierre et al . 1 . These accessions were selected from diverse sources to ensure representation of the broad spectrum of the drug-type Cannabis varieties available in the legal market of Canada (Supplementary Table S1 ).

In this study, we used four key productivity-related traits, including fresh biomass (FB; whole Cannabis plant excluding the roots), dried flower weight (DFW; representing yield), sexual maturity (SM; defined as the stage at which the first floral bud could be observed at the base of an axillary stem prior to the initiation of flowering) and harvest maturity (HM; days to maturity). Additionally, we included five morphological traits, namely stem diameter (SD), canopy diameter (CD), height, internode length index (ILI) and node counts (NC). It is worth noting that values were originally recorded in inches and were converted to centimeter for consistency. Histograms representing the distribution of each trait for the 176 accessions were generated using R v4.2.1 40 with the ‘ hist ’ function. Furthermore, a t -test was performed to determine whether the minimum and maximum values of each trait significantly differed from the overall population mean.

Sequencing and genotyping

Dna isolation, library preparation and sequencing.

Approximately 50 mg of young leaf tissue from each accession was collected for DNA extraction. The collected leaf tissues were air-dried for four days using a desiccating agent (Drierite; Xenia, OH, USA) and then ground with metallic beads in a RETSCH MM 400 mixer mill (Fisher Scientific, MA, USA). DNA extraction was carried out using the CTAB-chloroform protocol 41 . In brief, the powdered tissue was treated with a CTAB buffer solution, followed by a phenol–chloroform extraction procedure. The resulting DNA pellet underwent ethanol washing and was subsequently re-suspended in water. DNA quantification was carried out using a Qubit fluorometer with the dsDNA HS assay kit (Thermo Fisher Scientific, MA, USA), and concentrations were adjusted to 10 ng/μl for all samples. Final DNA samples were used to prepare HD-GBS libraries with Bfa I as described in Torkamaneh et al . 29 at the Institut de biologie intégrative et des systèmes (IBIS), Université Laval, QC, Canada. Sequencing was conducted on an Illumina NovaSeq 6000 (Illumina, CA, USA) with 150 paired-end reads at the Genome Quebec Service and Expertise Center (CESGQ), Montreal, QC, Canada.

SNP calling and filtration

Sequencing data were processed with the Fast-GBS v2.0 42 using the C. sativa cs10 v2 reference genome (GenBank acc. no. GCA_900626175.2 ) 15 . For variant calling a prerequisite of a minimum of 6 reads to call a single nucleotide polymorphism (SNP) was opted. Raw SNP data were filtered with VCFtools v0.1.16 43 to remove low-quality SNPs (QUAL < 10 and MQ < 30) and variants with proportion of missing data exceeding 80%. Missing data imputation was performed with BEAGLE 4.1 44 , followed by a second round of filtration, retaining only biallelic variants with heterozygosity less than 50% and a minor allele frequency (MAF) of > 0.06. Additionally, variants residing on unassembled scaffolds were removed. The resulting catalog of ~ 282 K SNPs was used to conducted genetic analysis, population structure assessment and GWAS (Supplementary Tables S2 ).

Genetic analysis

Marker description.

Read counts and coverage were calculated with SAMtools “coverage” parameter 45 . Proportion of heterozygous variants and MAF were estimated using TASSEL5 46 . The proportion of SNPs located within annotated genes was determined with BEDTools 47 by analyzing the number of SNPs overlapping with gene regions 48 (Supplementary Table S3 ). To visualize the distribution of SNP density, a plot was produced with rMVP 22 using ‘ plot.type  =  ”d” ’ parameter, in combination with the gene density distribution. The nucleotide diversity (π) 49 was measured in a sliding windows of 1000 bp across the genome using—window‐pi option of VCFtools 43 . Similarly, the pairwise π was calculated among different clusters.

LD decay and Haplotype block

Pairwise-LD was calculated with PLINK v1.9 50 using ‘ –r2 –ld-window-r2 0 ’ parameters . Long-range LD, measured as the allele frequency correlation (r 2 ), was determined for all pairwise SNPs within each chromosome independently (Supplementary Table S3 ). The LD decay curve line was fitted on the scatterplot using the smoothing spline regression following the procedure of Remington et al. 51 in the R environment (Fig.  2 b). The point of intersection between the LD curve and the predefined r 2 threshold determined the LD decay. Estimation of haplotype blocks (HBs) was performed with PLINK v1.9 using ‘ –blocks no-pheno-req –ld-window-kb 999 ’. A t -test was conducted in R to assess whether the LD decay of the chromosome X significantly differed from that of other chromosomes.

Population structure analysis

Population structure and admixture.

Population admixture was determined using a variational Bayesian inference algorithm implemented in fastStructure v1.0 52 for a number of subpopulations (K) set from 1 to 10. The optimal number of K (i.e., 3) explaining the population complexity was estimated using the ChooseK tool from fastStructure and admixture proportions were visualized using Distruct v2.3 (Fig.  2 c, Supplementary Fig. S1 ). The kinship matrix (K*) was generated using TASSEL5 with the Centered_IBS method and plotted with GAPIT v3 21 (Supplementary Fig. S3 ).

Discriminant analyses of principal components (DAPC) for population structure

Population structure was further investigated using discriminant analyses of principal components (DAPC) 53 using the R package ‘ adegenet ’ version 2.1.10. The number of cluster was estimated using ‘ find.cluster ’ function with a maximum limit set to 40 clusters and 200 principal components (PCs) (Fig.  2 d). Optimal number of clusters (i.e., K = 3) was determined by the minimal Bayesian Information Criterion (BIC) value for different numbers of K (Supplemental Fig. S2 ab). To visualize the DAPC using the ‘ scatter ’ function, the optimal number of PCs was estimated with two cross-validation procedures using ‘ optim.a.score ’ (i.e., PCs = 20, Supplemental Fig. S2 c) and ‘ xvalDapc ’ (i.e., PCs ≤ 20, Supplemental Fig. S2 d).

Comparison of population assignments and trait analysis

Cluster assignments from both fastStructure and DAPC were compared using the ‘ table ’ function for a K value of 3 and 6 (Supplementary Fig. S4 ). An analysis of variance (ANOVA) and permutational ANOVA (PERMANOVA) were performed for traits following and deviating from the normal distribution, respectively, using the ‘ adonis2 ’ function from R package ‘ vegan ’. Cluster assignments obtained from fastStructure were used as covariate. In cases where ANOVA/PERMANOVA indicated a significant difference, the post-hoc Tukey honestly significant difference (HSD) test was performed to determine which pairs were significantly different. Violin plots were generated with ‘ ggplot2 ’ in R and Tukey significant differences were represented by letter (Supplementary Fig. S5 ).

Genome-wide association analysis

Marker-trait association analysis was performed using the method BLINK 39 in GAPIT v3 21 , using the 282 K high-quality SNPs and the phenotyping data for nine different traits. The identification of false positive was minimized by incorporating population structure (i.e., P matrix generated with fastStructure for K = 3) and kinship (i.e., K* matrix generated with TASSEL5) for the analysis. The threshold of significance for marker-trait associations in both methods was set to ensure a false discovery rate < 0.05, adjusted with a Benjamini–Hochberg correction. Markers with a phenotypic variance explained (PVE) less than 3% were excluded from the analysis as they were considered uninformative and of limited interest. Manhattan plots showing –log 10 ( p ) distribution of markers by chromosome were generated with rMVP 22 using ‘ plot.type  =  ”m” ’ and quantile–quantile (QQ) plots were created with GAPIT v3 21 (Supplementary Fig. S6 ). Boxplot of the allelic classes of significant markers were generated with ‘ ggplot2 ’ in R (Supplementary Fig. S7 ).

Preliminary candidate gene identification

Due to the substantial genetic diversity present in Cannabis , only a limited number of SNPs exhibited a strong LD (r 2  ≥ 0.95). Therefore, to pinpoint genetic regions of interest, only markers in high LD (r 2  ≥ 0.75) with significant markers were retained to define haplotype blocks (HBs). Markers failing to form HB and residing outside of genetic regions were removed from the candidate gene investigation. Genes located in the HBs (defined by the 5ʹ-most and 3ʹ-most marker of the HB) were considered as putative candidate genes. The gene ontology (GO) annotations of these candidate genes were examined based on the description provided by the NCBI Cannabis sativa Annotation Release 100. To further confirm and provide a more detailed functional annotation of candidate genes, phylogenetic ortholog inferences were performed using OrthoFinder 54 with the Arabidopsis thaliana transcriptome (TAIR 11) 55 .

Results and discussion

A broad range of phenotypic variation among the 176 drug-type accessions.

The population displayed significant phenotypic diversity ( p  < 0.001) across the nine examined traits (Fig.  1 , Supplemental Table S1 ). For instance, FB exhibited a substantial variation, ranging from 90 to 1260 g, while plant height varied between 22 and 109 cm. SM also showed a significant diversity, with individuals initiating the first flower bud between 20 and 68 days. With the exception of SM, all other traits displayed a unimodal distribution, suggesting a complex genetic control involving multiple QTL. Furthermore, these traits exhibited highly skewed distributions, indicating that some accessions may carry specific alleles or combinations of alleles exerting a substantial impact on these traits. This phenotypic diversity within the Cannabis accessions provides a robust foundation for GWAS, aligning with established criteria for successful GWAS outcomes 23 .

Frequency distribution of phenotypic data for 176 drug-type accessions used in this study. For each trait, the minimum and maximum values significantly differed ( t -test p  < 0.001) from the overall population mean.

Genetic diversity in the GWAS-panel revealed by dense genotyping

To achieve comprehensive marker coverage across the Cannabis genome, an HD-GBS approach was used. Sequencing of HD-GBS libraries generated 486 M reads, averaging 2.8 M reads per sample. This extensive sequencing effort resulted in an average per-sample coverage of 7.7% of the cs10 v2 assembly, achieving a cumulative coverage of 34.1% across the entire genome for the entire population. The analysis of variant calling from our sequencing data initially yielded a substantial dataset of 2.7 M raw variants that met the quality criteria. Following filtering for missing data and minor allele frequency (MAF of 1%), we successfully identified ~ 800 K polymorphic variants, with an overall proportion of missing data reaching 61% before imputation step. This SNP catalog meets the criteria required to perform a relevant missing data imputation 56 . Subsequently, we performed a secondary round of filtering, primarily aimed at retaining common variants, as defined by a MAF of 6%, retaining approximately 39% of the raw data. While this filtering step may exclude rare variants that could potentially influence complex traits, it is essential to reduce the risk of false-positive associations and ensure that a minimum of 10 accessions carries the significant allele, thereby preventing overfitting in GWAS models 23 . The HD-GBS approach and the filtering procedures resulted in a catalog of 282 K high-quality SNPs (all details in Supplemental Table S2 ). Within this catalog, 25.5% of the genotypes were found to be heterozygous and the SNPs exhibited an average MAF of 21.7%. For a detailed overview of filtering steps and the number of variants retained at each stage, refer to Supplementary Table S3 . Overall, this SNP catalog represents an extensive genetic resource for the subsequent GWAS and underscores the robustness of the genotyping strategy used in this study.

Markers were exceptionally well distributed across the genome, ensuring coverage of gene-rich regions. On average, there was one marker per every ~ 3 kb of the genome, which significantly enhances the likelihood of identifying markers in strong LD with putative candidate genes or regions (Fig.  2 a). Across the entire physical map, only 12 gaps exceeding 1 Mb, with the largest being 1.2 Mb, were identified. Comparing our dataset with the RAD-Seq method used in the study of Petit et al. 32 , 33 , by employing comparable filtration criteria, the HD-GBS approach yielded a comparable number of markers while utilizing only one-tenth of the sequencing efforts (averaging 2.8 M vs. 29.7 M reads per sample). Therefore, the density and genomic distribution of SNPs provided by the HD-GBS approach make it a cost-effective option for conducting GWAS on large Cannabis panel. Furthermore, this approach is compatible with the miniaturization of sequencing libraries using the NanoGBS procedure, which further contributes to substantial cost reduction in genotyping 57 .

The average extent of LD decay to its half ranged from 22.6 to 89.0 kb across different chromosomes (Fig.  2 b). It is important to note that LD decay is a relative value and does not precisely reflect to reality recombination rates throughout the entire genome, particularly between heterochromatic and euchromatic regions 58 . However, this measure proved valuable for comparing the impact of domestication and selection on recombination rates among different populations. In this context, the LD observed in the GWAS-panel showed rapid decay compared to modern cultivars of comparable genome size, such as soybean (where LD may extend over 100 kb 59 ) and tomato (where LD can extend over 1 Mb 60 ). Nevertheless, LD decayed to its half more slowly compared to a recent study of 110 domesticated and landrace Cannabis accessions from various worldwide origins, where LD decayed over approximately 10 kb 61 . This resulted in a large number of small HBs with an average size of ~ 4 kb (Supplemental Table S3 ). It is worth noting that the LD decay on the sex chromosome was almost twice slower ( p  < 0.001) compared to autosomes. These observations were consistent with the recent history of Cannabis cultivation in Canada, characterized by extensive hybridization efforts by breeders with a particular focus on sexual characteristics, such as the production of female flowers 2 .

Low level of population structure

The population structure within the GWAS-panel was assessed using the 282 K high-quality SNPs. Initially, the degree of admixture of individuals and clustering inference was estimated by fastStructure (Supplemental Table S4 ). While the model maximizing the marginal likelihood suggested a K value of 6, the optimal number of principal components (PCs) to explain the structure of the population was determined to be 3. The K value of 3 revealed two clusters (clusters 1 and 3, Fig.  2 c) with low admixture compared to a K value of 6 (Supplemental Fig. S1 ), indicating a more robust assignments with more homogeneous individuals within each cluster. Using the BIC criterion, DAPC inferred three clusters (Fig.  2 d, Supplemental Fig. S2 ab, Supplemental Table S4 ). The minimal BIC values were obtained with K values ranging from 3 to 6, consistent with the optimal number of clusters determined by fastStructure, where 3 represents the minimum value. Thus, a K of 3 was chosen to explain the structure of the GWAS panel. Comparing both methods, 94.3% and 90.0% concordant assignment were observed for K values of 3 and 6, respectively (Supplemental Fig. S4 ).

Genome-wide distribution of markers, linkage disequilibrium (LD) and population structure analysis. ( a ) Density plot of markers and genes across the genome. Colors represent the number of SNPs within 1 Mb window size. ( b ) LD decay in each chromosome where LD values of intra-chromosomal pairwise markers were plotted against physical distance. ( c ) Admixture plot for K = 3 using fastStructure. The vertical lines represent the accessions, and the y-axis represents the probability that an individual belongs to a subgroup. ( d ) Discriminant analysis of principal components (DAPC) scatter plot showing population structure.

Nucleotide diversity ( θ π  ) across the three clusters varied from 8.44 × 10 −4 to 1.20 × 10 −3 . A lower level of genome-wide genetic diversity was observed here in drug-type cannabis (mean θ π  = 1.05 × 10 −3 ) compared to broader cannabis populations worldwide ( θ π  = 3.0 × 10 −3 ) 61 . This level of diversity is also lower than that found in other major crops such as soybean (mean  θ π  = 1.36 × 10 −3 ) 62 , rice ( θ π  = 4.0 × 10 −3 ) 63 and corn ( θ π  = 6.6 × 10 −3 ) 64 . Relatedness analysis among individuals revealed low intra- and inter-cluster genetic diversity, with accessions appearing neither significantly similar nor significantly distant (Supplemental Fig. S3 ). This is consistent with the cumulative variance explaining genetic variation in the population, showing gradual increase with number of retained PCs up to 176 PCs (number of accessions in the GWAS panel) rather than reaching a plateau (Supplemental Fig. S2 a). Despite the overall genetic homogeneity, significative differences were observed between clusters for traits such as SM, HM, height, NC and ILI (Supplemental Fig. S5 ). In particular, cluster K3 exhibited significant differences from the cluster K1 for these five traits, while the cluster K2 displayed intermediate trait values between K1 and K3. In different studies, similar clustering patterns related to drug-type and hemp-type accessions 61 , 65 , 66 , 67 or geographic origins 68 were documented, where each clusters grouped independently, albeit with low intra- and inter-cluster genetic diversity. Due to limited information on the pedigree of the GWAS panel, no correlation was observed between cluster assignment and geographic or germplasm origins. Additionally, no correlation was observed between the clustering and cannabinoid composition (data not shown) of these accessions.

The limited genetic diversity observed in cultivated drug-type Cannabis has historically been attributed to intensive clandestine breeding practices since the 1970s 2 , coupled with the impact of the war on drugs, which led to the destruction of many plants and seeds, effectively reducing the gene pool 5 , 69 . Despite the limited genetic diversity, Cannabis exhibits a remarkable phenotypic variation that are highly desirable for breeding programs. Hence, it could be hypothesized that a portion of the observed phenotypic variations in Cannabis may be attributed to transcriptional variations, along with potential contributions from epigenetic factors. In both plants and animals, factors such as variation in the number of gene copies (CNVs) 70 , epigenetic elements 71 , and the insertion/deletion of transposable elements (TEs) in gene control regions 72 , impact phenotypic diversity, especially those crucial in domestication and breeding 73 . Therefore, an associated SNP may be in strong LD with either a candidate gene, where an allelic variant alters the phenotype, or with a regulatory region that either enhances or suppresses the expression of the phenotype 74 .

The constrained availability of germplasm resources and low genetic diversity observed in Cannabis pose significant limitations for breeding, which, in turn, hinder innovation and the long-term sustainability of the crop 7 . In contrast to other crops where wild-type or landrace varieties are promising genetic pools to enrich genetic diversity in breeding programs 75 , the situation in Cannabis is more complex. Although hemp-type and drug-type Cannabis genetically diverged 76 , they still share a considerable common pool of genetic variation, limiting the ability to mine rare alleles 65 . Given the growing demand for cannabis products, there is a critical necessity to pinpoint suitable genetic resources that can not only support production but also serve as a source of genetic diversity to help ongoing breeding efforts 7 .

Identification of genomic regions controlling key agronomic and morphological traits

The GWAS analysis was performed using the method BLINK with the incorporation of population structure (P) and cryptic relatedness (K*) as covariates to minimize the risk of false-positive associations. In total, 18 markers associated with the nine traits were identified (Fig.  3 , Table 1 ). For all significant markers identified, the three genotypes were observed (Supplemental Fig. S7 ). Six of these SNPs (SNP_1, 4, 7, 8, 9 and 11; Table 1 ) demonstrated significant phenotypic impact, with the proportion of phenotypic variance explained (PVE) ranging from 18 to 45% while the remaining identified markers have a modest influence on the phenotype (PVE < 10%). Interestingly, several SNPs associated with different traits were located in close proximity to each other. For instance, SNP_9and _17 were situated within a region of about 38 kb on chromosome 1 (Chr01: 87456694–87494979) and were associated with ILI and height. The identification of 2 SNPs associated with correlated traits is consistent and suggests that this region of chromosome 1 plays a crucial role in modulating plant size in the GWAS panel. These markers are associated with key characteristics for Cannabis cultivation and are therefore of particular interest to breeders and growers. For instance, markers associated with smaller size can be advantageous for maximizing indoor cultivation, where smaller plants are preferred. Regarding the markers associated with a shorter flowering or maturation, they are advantageous for cultivators aiming for a quicker crop turnover. Similarly, the allele T at Chr09:59690286 (SNP_4) is associated with reduces canopy size and slightly the height, which can help maximize plant density in cultivation.

Genome-wide association studies (GWAS) for nine agronomic and morphological traits in drug-type cannabis. Manhattan plot for productivity-related traits ( a ) and morphological traits ( b ). Each circle indicates the degree of association for a marker with a trait (y axis), while the x axis shows the physical position of each marker on a given chromosome across the genome. The horizontal grey line indicates the significance threshold ( p -value = 1.77 × 10 −7 , false discovery rate < 0.05). Marker-trait associations were performed with the Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) method.

Given that the traits under study appear to be governed by a complex genetic control involving multiple QTL, BLINK appeared to be the most suitable method as it can capture intricate interactions among several loci through multi-locus analysis 39 . Furthermore, this method has proven its effectiveness with large catalog of SNPs 77 , 79 , 79 and was ranked as the most statistically powerful method for multi-locus analyzes for GWAS in plants 21 , 39 , 80 . As the identification of high-value markers for Cannabis is in its early stages, the practical implementation of these markers by breeding programs will nevertheless require preliminary cross-validation. This can be achieved through meta-GWAS 81 , QTL mapping with biparental population and BSA. Additionally, comprehensive functional analyses of the candidate genes will be crucial. .

Investigation of putative candidate genes

Among the 18 associated SNPs, 11 were in high LD (r 2  ≥ 0.75) with other SNPs, forming HBs (Table 2 ). Notably, SNP_9 and _17 were part of the same HB, spanning ~ 97 kb on chromosome 1. The SNP _26 was located within LOC115699444 without forming HBs. The 11 HBs spanned ~ 250 kb, within which 21 annotated genes were identified. Consequently, these genes were considered as putative candidates genes associated with different traits. Recent genome annotation of cs10 48 facilitated the investigation of the functions of candidate genes (Table 2 ). An orthology analysis was conducted by comparing the protein sequences of candidate genes with the Arabidopsis proteome 55 . Functional annotations were similar for the majority of candidate genes and their respective orthologs, confirming the robustness of the functional annotation of the cs10 transcriptome.

The SNP_4, which showed associations with DFW, CD, and height, was found to be in high LD with LOC115722258 , associated with chloroplast metabolism and mechanisms. This suggests a potential link between the genetic variation of SNP_4 and the observed variations in these morphological traits through their impact on chloroplast-related processesIn addition to structural genes, regulatory genes, such as transcription factors, were identified among the potential candidate genes (e.g., LOC115706624 ). Approximately one-third of the associated SNPs were not in high LD with putative candidate gene, but they might more likely linked to gene regulatory regions. The in-silico identification of regulatory regions and their interaction with a gene is challenging and complex to link associated SNPs and the phenotype. However, this does not diminish their importance, especially for markers SNP_7 and _8, which were associated with a substantial impact on HM (PVE > 30%). These findings suggest that regulatory elements, such as transcription factors, may play a role in shaping the phenotypic variation in cultivated Cannabis . However, confirming the relevance of these candidate genes will still require further analysis.

In conclusion, this study marks a pioneering exploration of the genetic landscape of Canadian drug-type Cannabis through a comprehensive GWAS analysis, enriched by high-throughput genotyping and precise agronomic phenotyping data. Our findings open new avenues for advancing Cannabis breeding programs and addressing the diverse needs of emerging industries. The application of a high-density genotyping approach yielded an extensive catalog of high-quality SNPs, effectively capturing the genomic diversity of drug-type Cannabis . The distribution of these markers across different chromosomes, coupled with high quality phenotypic data, facilitated the identification of molecular markers associated with complex agronomic and morphological traits. These markers hold great promise for further investigations to elucidate their functional links with phenotype variations, making them valuable assets for precision breeding efforts.

As we move forward, this research paves the way for in-depth studies to uncover the biological mechanisms governing these traits, potentially uncovering hidden genetic potential within Cannabis populations. Furthermore, the implications of our work extend beyond immediate applications, as the identified markers are poised to play a pivotal role in the development of tailor-made Cannabis cultivars, spanning both medicinal and recreational sectors, capable of meeting the dynamic demands of rapidly evolving industries.

Future perspectives in this domain encompass a deeper exploration of the candidate genes associated with the identified markers, seeking to unravel the intricate genetic and molecular underpinnings of these key traits. Additionally, functional validation experiments and expression profiling could elucidate the precise mechanisms through which these markers exert their effects. Collaborative efforts between academia and industry are essential to harness this newfound genetic knowledge and translate it into practical breeding strategies, ensuring the continued innovation and sustainability of the Cannabis crop.

Data availability

The VCF files generated from the sequencing data and used for the analyzes of this study are on FigShare.com and will be accessible after acceptance of the manuscript. This includes the raw SNP data set for the 176 accessions, the 282 K imputed and filtered SNPs and the subdivision of the population by K clusters.

Abbreviations

Δ 9 -Tetrahydrocannabinol

International Cannabis Research Consortium

Next-generation sequencing

Genome-Wide Association Study

Quantitative trait loci

Linkage disequilibrium

Restriction-site associated DNA sequencing

Genotyping-by-sequencing

Marker-assisted selection

Genomic selection

Whole-genome sequencing

High-density GBS

Fresh biomass

Dried flower weight

Sexual maturity

Stem diameter

Canopy diameter

Internode Length Index

Node counts

Minor allele frequency

Haplotype block

Discriminant analyses of principal components

Settlement of MLM under progressively exclusive relationship

Bayesian-information and linkage-disequilibrium iteratively nested keyway

Analyse of variance

Permutational ANOVA

Gene ontology

Quantile–Quantile

Phenotypic variance explained

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Acknowledgements

The authors wish to thank Fuga Group Inc. for supporting this project. The authors also extend their sincere appreciation to Justine Richard-Giroux, Rosemarie Boulanger and Sean Kyne for their valuable contributions to the tedious phenotyping of the GWAS-panel.

This work was conducted as part of a collaborative research project funded by Fuga Group Inc. and NSERC Alliance [#ALLRP 568653-21 to D.T.].

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Maxime de Ronne, Éliana Lapierre & Davoud Torkamaneh

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Maxime de Ronne: Conceptualization and Methodology; Data curation and analysis; Investigation; Visualization; Writing-original draft. Éliana Lapierre: Preparation of phenotypic data. Davoud Torkamaneh: Funding acquisition; Conceptualization and Methodology; Supervision; Writing-review. All authors have reviewed and approved the manuscript.

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de Ronne, M., Lapierre, É. & Torkamaneh, D. Genetic insights into agronomic and morphological traits of drug-type cannabis revealed by genome-wide association studies. Sci Rep 14 , 9162 (2024). https://doi.org/10.1038/s41598-024-58931-w

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• Published: 10 December 2019

Benefits and harms of medical cannabis: a scoping review of systematic reviews

• Misty Pratt 1 ,
• Adrienne Stevens 1 , 2 ,
• Micere Thuku 1 ,
• Claire Butler 1 , 3 ,
• Becky Skidmore 4 ,
• L. Susan Wieland 5 ,
• Mark Clemons 6 , 7 ,
• Salmaan Kanji 6 , 8 , 9 &
• Brian Hutton   ORCID: orcid.org/0000-0001-5662-8647 1 , 6  

Systematic Reviews volume  8 , Article number:  320 ( 2019 ) Cite this article

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There has been increased interest in the role of cannabis for treating medical conditions. The availability of different cannabis-based products can make the side effects of exposure unpredictable. We sought to conduct a scoping review of systematic reviews assessing benefits and harms of cannabis-based medicines for any condition.

A protocol was followed throughout the conduct of this scoping review. A protocol-guided scoping review conduct. Searches of bibliographic databases (e.g., MEDLINE®, Embase, PsycINFO, the Cochrane Library) and gray literature were performed. Two people selected and charted data from systematic reviews. Categorizations emerged during data synthesis. The reporting of results from systematic reviews was performed at a high level appropriate for a scoping review.

After screening 1975 citations, 72 systematic reviews were included. The reviews covered many conditions, the most common being pain management. Several reviews focused on management of pain as a symptom of conditions such as multiple sclerosis (MS), injury, and cancer. After pain, the most common symptoms treated were spasticity in MS, movement disturbances, nausea/vomiting, and mental health symptoms. An assessment of review findings lends to the understanding that, although in a small number of reviews results showed a benefit for reducing pain, the analysis approach and reporting in other reviews was sub-optimal, making it difficult to know how consistent findings are when considering pain in general. Adverse effects were reported in most reviews comparing cannabis with placebo (49/59, 83%) and in 20/24 (83%) of the reviews comparing cannabis to active drugs. Minor adverse effects (e.g., drowsiness, dizziness) were common and reported in over half of the reviews. Serious harms were not as common, but were reported in 21/59 (36%) reviews that reported on adverse effects. Overall, safety data was generally reported study-by-study, with few reviews synthesizing data. Only one review was rated as high quality, while the remaining were rated of moderate ( n = 36) or low/critically low ( n = 35) quality.

Conclusions

Results from the included reviews were mixed, with most reporting an inability to draw conclusions due to inconsistent findings and a lack of rigorous evidence. Mild harms were frequently reported, and it is possible the harms of cannabis-based medicines may outweigh benefits.

Systematic review registration

The protocol for this scoping review was posted in the Open Access ( https://ruor.uottawa.ca/handle/10393/37247 ).

Peer Review reports

Interest in medical applications of marijuana ( Cannabis sativa ) has increased dramatically during the past 20 years. A 1999 report from the National Academies of Sciences, Engineering, and Medicine supported the use of marijuana in medicine, leading to a number of regulatory medical colleges providing recommendations for its prescription to patients [ 1 ]. An updated report in 2017 called for a national research agenda, improvement of research quality, improvement in data collection and surveillance efforts, and strategies for addressing barriers in advancing the cannabis agenda [ 2 ].

Proponents of medical cannabis support its use for a highly varied range of medical conditions, most notably in the fields of pain management [ 3 ] and multiple sclerosis [ 4 ]. Marijuana can be consumed by patients in a variety of ways including smoking, vaporizing, ingesting, or administering sublingually or rectally. The plant consists of more than 100 known cannabinoids, the main ones of relevance to medical applications being tetrahydrocannabinol (THC) and cannabidiol (CBD) [ 5 ]. Synthetic forms of marijuana such as dronabinol and nabilone are also available as prescriptions in the USA and Canada [ 6 ].

Over the last decade, there has been an increased interest in the use of medical cannabis products in North America. It is estimated that over 3.5 million people in the USA are legally using medical marijuana, and a total of USD$6.7 billion was spent in North America on legal marijuana in 2016 [ 7 ]. The number of Canadian residents with prescriptions to purchase medical marijuana from Health Canada–approved growers tripled from 30,537 in 2015 to near 100,000 in 2016 [ 8 ]. With the legalization of recreational-use marijuana in parts of the USA and in Canada in October 2018, the number of patients using marijuana for therapeutic purposes may become more difficult to track. The likely increase in the numbers of individuals consuming cannabis also necessitates a greater awareness of its potential benefits and harms.

Plant-based and plant-derived cannabis products are not monitored as more traditional medicines are, thereby increasing the uncertainty regarding its potential health risks to patients [ 3 ]. While synthetic forms of cannabis are available by prescription, different cannabis plants and products contain varied concentrations of THC and CBD, making the effects of exposure unpredictable [ 9 ]. While short-lasting side effects including drowsiness, loss of short-term memory, and dizziness are relatively well known and may be considered minor, other possible effects (e.g., psychosis, paranoia, anxiety, infection, withdrawal) may be more harmful to patients.

There remains a considerable degree of clinical equipoise as to the benefits and harms of marijuana use for medical purposes [ 10 , 11 , 12 , 13 ]. To understand the extent of synthesized evidence underlying this issue, we conducted a scoping review [ 14 ] of systematic reviews evaluating the benefits and/or harms of cannabis (plant-based, plant-derived, and synthetic forms) for any medical condition. We located and mapped systematic reviews to summarize research that is available for consideration for practice or policy questions in relation to medical marijuana.

A scoping review protocol was prepared and posted to the University of Ottawa Health Sciences Library’s online repository ( https://ruor.uottawa.ca/handle/10393/37247 ). We used the PRISMA for Scoping Reviews checklist to guide the reporting of this report (see Additional file 1 ) [ 15 ].

Literature search and process of study selection

An experienced medical information specialist developed and tested the search strategy using an iterative process in consultation with the review team. Another senior information specialist peer-reviewed the strategy prior to execution using the PRESS Checklist [ 16 ]. We searched seven Ovid databases: MEDLINE®, including Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Embase, Allied and Complementary Medicine Database, PsycINFO, the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects, and the Health Technology Assessment Database. The final peer-reviewed search strategy for MEDLINE was translated to the other databases (see Additional file 2 ). We performed the searches on November 3, 2017.

The search strategy incorporated controlled vocabulary (e.g., “Cannabis,” “Cannabinoids,” “Medical Marijuana”) and keywords (e.g., “marijuana,” “hashish,” “tetrahydrocannabinol”) and applied a broad systematic review filter where applicable. Vocabulary and syntax were adjusted across the databases and where possible animal-only and opinion pieces were removed, from the search results.

Gray literature searching was limited to relevant drug and mental health databases, as well as HTA (Health Technology Assessment) and systematic review databases. Searching was guided by the Canadian Agency for Drugs and Technologies in Health’s (CADTH) checklist for health-related gray literature (see Additional file 3 ). We performed searches between January and February 2018. Reference lists of overviews were searched for relevant systematic reviews, and we searched for full-text publications of abstracts or protocols.

Management of all screening was performed using Distiller SR Software ® (Evidence Partners Inc., Ottawa, Canada). Citations from the literature search were collated and de-duplicated in Reference Manager (Thomson Reuters: Reference Manager 12 [Computer Program]. New York: Thomson Reuters 2011), and then uploaded to Distiller. The review team used Distiller for Levels 1 (titles and abstracts) and 2 (full-text) screening. Pilot testing of screening questions for both levels were completed prior to implementation. All titles and abstracts were screened in duplicate by two independent reviewers (MT and MP) using the liberal accelerated method [ 17 ]. This method requires only one reviewer to assess an abstract as eligible for full-text screening, and requires two reviewers to deem the abstract irrelevant. Two independent reviewers (MT and MP) assessed full-text reports for eligibility. Disagreements during full-text screening were resolved through consensus, or by a third team member (AS). The process of review selection was summarized using a PRISMA flow diagram (Fig. 1 ) [ 18 ].

PRISMA-style flow diagram of the review selection process

Review selection criteria

English-language systematic reviews were included if they reported that they investigated harms and/or benefits of medical or therapeutic use of cannabis for adults and children for any indication. Definitions related to medical cannabis/marijuana are provided in Table 1 . We also included synthetic cannabis products, which are prescribed medicines with specified doses of THC and CBD. Reviews of solely observational designs were included only in relation to adverse effects data, in order to focus on the most robust evidence available. We considered studies to be systematic reviews if at least one database was searched with search dates reported, at least one eligibility criterion was reported, the authors had assessed the quality of included studies, and there was a narrative or quantitative synthesis of the evidence. Reviews assessing multiple interventions (both pharmacological and complementary and alternative medicine (CAM) interventions) were included if the data for marijuana studies was reported separately. Published and unpublished guidelines were included if they conducted a systematic review encompassing the criteria listed above.

We excluded overviews of systematic reviews, reviews in abstract form only, and review protocols. We further excluded systematic reviews focusing on recreational, accidental, acute, or general cannabis use/abuse and interventions such as synthetic cannabinoids not approved for therapeutic use (e.g., K2 or Spice).

Data collection and quality assessment

All data were collected electronically in a pre-developed form using Microsoft Excel software (Microsoft Corporation, Seattle, USA). The form was pilot tested on three included reviews by three people. One reviewer (MP or CB) independently extracted all data, and a second reviewer (MT) verified all of the items collected and checked for any omitted data. Disagreements were resolved by consensus and consultation with a third reviewer if necessary. A data extraction form with the list of included variables is provided in Additional file 4 . All collected data has also been made available in the online supplemental materials associated with this report.

Quality assessment of systematic reviews was performed using the AMSTAR-2 [ 20 ] tool. One reviewer (MP or CB) independently assessed quality, while a second reviewer (MT) verified the assessments. Disagreements were resolved by consensus and consultation with a third reviewer if necessary. The tool consists of 16 items in total, with four critical domains and 12 non-critical domains. The AMSTAR-2 tool is not intended to generate an overall score, and instead allows for an overall rating based on weaknesses in critical domains. Reviews were rated as high (no critical flaws with zero or one non-critical flaw), moderate (no critical flaws with ≥ 1 non-critical flaw), low (one critical flaw with/without non-critical weakness), or critically low (> 1 critical flaw with/without non-critical weakness) quality.

Evidence synthesis

We used a directed content analytic approach [ 21 ] with an initial deductive framework [ 22 ] that allowed flexibility for inductive analysis if refinement or development of new categorization was needed. The framework used to categorize outcome data results is outlined in Table 2 . Where reviews had a mix of narrative and quantitative data, results from meta-analyses were prioritized over count data or study-by-study data. The extraction and reporting of data results was performed at a high level and did not involve an in-depth evaluation, which is appropriate for a scoping review [ 14 ]. Review authors’ conclusions and/or recommendations were extracted and reported narratively.

Changes from the study protocol

For feasibility, we decided to limit the inclusion of systematic reviews of only observational study designs to those that addressed adverse events data. All other steps of the review were performed as planned.

Search findings

The PRISMA flow diagram describing the process of review selection is presented in Fig. 1 . After duplicates were removed, the search identified a total of 1925 titles and abstracts, of which 47 references were located through the gray literature search. Of the total 1925 citations assessed during Level 1 screening, 1285 were deemed irrelevant. We reviewed full-text reports for the 640 reviews of potential relevance, and of these, 567 were subsequently excluded, leaving a total of 72 systematic reviews that were included; the associated data collected are provided in Additional file 5 . A listing of the reports excluded during full-text review is provided in Additional file 6 .

Characteristics of included reviews

There were 63 systematic reviews [ 4 , 19 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ] and nine guidelines with systematic reviews [ 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ]. Overall, 27 reviews were performed by researchers in Europe, 16 in the USA, 15 in Canada, eight in Australia, two in Brazil, and one each in Israel, Singapore, South Africa, and China. Funding was not reported in 29 (40%) of the reviews, and the remaining reviews received funding from non-profit or academic ( n = 20; 28%), government ( n = 14; 19%), industry ( n = 3; 4%), and mixed ( n = 1; 1%) sources. Five reviews reported that they did not receive any funding for the systematic review. Tables 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , and 13 provide an overview of the characteristics of the 72 included systematic reviews.

The reviews were published between 2000 and 2018 (median year 2014), and almost half (47%) were focused solely on medical cannabis. Four (6%) reviews covered both medical and other cannabis use (recreational and substance abuse), 19 (26%) reported multiple pharmaceutical interventions (cannabis being one), six (8%) reported various CAM interventions (cannabis being one), and nine (13%) were mixed pharmaceutical and CAM interventions (cannabis being one). Multiple databases were searched by almost all of the reviews (97%), with Medline/PubMed or Embase common to all.

Cannabis use

Figure 2 illustrates the different cannabis-based interventions covered by the included reviews. Plant-based cannabis consists of whole plant products such as marijuana or hashish. Plant-derived cannabinoids are active constituents of the cannabis plant, such as tetrahydrocannabinol (THC), cannabidiol (CBD), or a combination of THC:CBD (also called nabiximols, under the brand name Sativex) [ 3 ]. Synthetic cannabinoids are manufactured rather than extracted from the plant and include drugs such as nabilone and dronabinol.

Review coverage of the various cannabis-based interventions

Twenty-seven reviews included solely interventions from plant-derived cannabinoids, 10 studied solely synthetic cannabinoids, and eight included solely studies on plant-based cannabis products. Twenty-four reviews covered a combination of different types of cannabis, and the remaining three systematic reviews did not report which type of cannabinoid was administered in the included studies.

The systematic reviews covered a wide range of conditions and illnesses, the most notable being pain management. Seventeen reviews looked at specific types of pain including neuropathic [ 31 , 42 , 62 , 69 , 85 , 90 ], chronic [ 26 , 32 , 52 , 58 , 80 ], cancer [ 84 , 87 ], non-cancer [ 41 , 68 ], and acute [ 38 ] types of pain (one review covered all types of pain) [ 65 ]. Twenty-seven reviews (38%) also focused on management of pain as a symptom of conditions such as multiple sclerosis (MS) [ 6 , 23 , 27 , 43 , 46 , 52 , 63 , 85 , 92 ], injury [ 29 , 35 , 36 , 69 ], cancer [ 37 , 43 , 65 , 88 ], inflammatory bowel disease (IBD) [ 28 ], rheumatic disease (RD) [ 49 , 51 , 73 ], diabetes [ 68 , 69 , 70 ], and HIV [ 48 , 53 , 67 ]. In Fig. 3 , the types of illnesses addressed by the set of included reviews are graphically represented, with overlap between various conditions and pain. Some systematic reviews covered multiple diseases, and therefore the total number of conditions represented in Fig. 3 is greater than the total number of included reviews.

Conditions or symptoms across reviews that were treated with cannabis. IBD inflammatory bowel disease, MS multiple sclerosis, RD rheumatic disease

One review included a pediatric-only population, in the evaluation of marijuana for nausea and vomiting following chemotherapy [ 54 ]. Although trials in both adult and child populations were eligible for thirteen (18%) reviews, only two additional reviews included studies in children; these reviews evaluated cannabis in cancer [ 60 ] and a variety of conditions [ 25 ]. Many of the reviews ( n = 25, 35%) included only adults ≥ 18 years of age. Almost half of the reviews ( n = 33, 46%) did not report a specific population for inclusion.

Cannabis was prescribed for a wide range of medical issues. The indication for cannabis use is illustrated in Fig. 4 . Pain management ( n = 27) was the most common indication for cannabis use. A number of reviews sought to address multiple disease symptoms ( n = 12) or explored a more holistic treatment for the disease itself ( n = 11). After pain, the most common symptoms being treated with cannabis were spasticity in MS, movement disturbances (such as dyskinesia, tics, and spasms), weight or nausea/vomiting, and mental health symptoms.

Indications for cannabis use across included reviews

Figure 5 summarizes the breadth of outcomes analyzed in the included reviews. The most commonly addressed outcomes were withdrawal due to adverse effects, “other pain,” neuropathic pain, spasticity, and the global impression of the change in clinical status. Many outcomes were reported using a variety of measures across reviews. For example, spasticity was measured both objectively (using the Ashworth scale) and subjectively (using a visual analog scale [VAS] or numerical rating scale [NRS]). Similarily, outcomes for pain included VAS or NRS scales, reduction in pain, pain relief, analgesia, pain intensity, and patient assessment of change in pain.

Quality of the systematic reviews

Quality assessments of the included reviews based upon AMSTAR-2 are detailed in Additional file 7 and Additional file 8 . Only one review was rated as high quality [ 45 ]. All other reviews were deemed to be of moderate ( n = 36) or low/critically low ( n = 35) methodological quality. Assessments for the domains deemed of critical importance for determining quality ratings are described below.

Only 20% of reviews used a comprehensive search strategy; another 47% were given a partial score because they had not searched the reference lists of the included reviews, trial registries, gray literature, and/or the search date was older than 2 years. The remaining reviews did not report a comprehensive search strategy.

Over half of the reviews (51%) used a satisfactory technique for assessing risk of bias (ROB) of the individual included studies, while 35% were partially satisfactory because they had not reported whether allocation sequence was truly random and/or they had not assessed selective reporting. The remaining reviews did not report a satisfactory technique for assessing ROB.

Most reviews (71%) could not be assessed for an appropriate statistical method for combining results in a meta-analysis, as they synthesized study data narratively. Approximately 19% of reviews used an appropriate meta-analytical approach, leaving 10% that used inappropriate methods.

The final critical domain for the AMSTAR-2 determines whether review authors accounted for ROB in individual studies when discussing or interpreting the results of the review. The majority of reviews (83%) did so in some capacity.

Mapping results of included systematic reviews

We mapped reviews according to authors’ comparisons, the conditions or symptoms they were evaluating, and the categorization of the results (see Table 2 ). In some cases, reviews contributed to more than one comparison (e.g., cannabis versus placebo or active drug). As pain was the most commonly addressed outcome, we mapped this outcome separately from all other endpoints. This information is shown for all reviews and then restricted to reviews of moderate-to-high quality (as determined using the AMSTAR-2 criteria): cannabis versus placebo (Figs. 6 and 7 ), cannabis versus active drugs (Figs. 8 and 9 ), cannabis versus a combination of placebo and active drug (Figs. 10 and 11 ), one cannabis formulation versus other (Figs. 12 and 13 ), and cannabis analyzed against all other comparators (Fig. 14 ). Details on how to read the figures are provided in the corresponding figure legends. The median number of included studies across reviews was four, and ranged from one to seventy-nine (not shown in figures).

Cannabis vs. placebo. Authors’ presentations of the findings were mapped using the categorization shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. active drugs. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. active drugs, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo + active drug. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo + active drug, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

One cannabis formulation vs. other. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

One cannabis formulation vs. other, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. all comparators combined. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis versus placebo

Most reviews (59/72, 82%) compared cannabis with placebo. Of these reviews, 34 (58%) addressed pain outcomes and 47 (80%) addressed non-pain outcomes, with most outcomes addressed by three reviews or fewer (Fig. 6 ). Some reviews had a mix of quantitative syntheses and study-by-study data reported (13/59, 22%), while another group of reviews (14/59, 24%) only reported results study-by-study. Overall, 24% (14/59) of the cannabis versus placebo reviews had only one included study.

Pain outcomes

Reviews focused on addressing pain across conditions. In most cases, findings were discordant across reviews for the pain outcomes measured. For chronic non-cancer pain, however, two reviews favored cannabis over placebo for decreasing pain. One review assessing acute pain for postoperative pain relief found no difference between various cannabinoid medications and placebo. The distribution of findings was similar when restricting to moderate-to-high-quality reviews.

Reviews focused on treating a condition or family of related conditions . Various results were observed for pain. For MS and HIV/AIDS, one review each reported quantitative results favoring cannabis for decreased pain but with other reviews reporting results study-by-study, it is difficult to know, broadly, how consistent those findings are. For cancer, two reviews reported results favoring cannabis for decreased pain. For rheumatic disease, findings are discordant between two reviews, and another two reviews reported results study-by-study. One review that included studies of MS or paraplegia found no difference in pain between groups. For treating injury, one review showed that the placebo group had less pain and one review reported data study-by-study. No reviews addressed pain in movement disorders, neurological conditions, and IBD.

For those reviews assessing pain as part of a focus on treating a range of conditions, two showed cannabis reduced pain [ 43 , 52 ], but one showed mixed results depending on how pain was measured [ 43 ]. These reviews covered several different conditions, including injury, chronic pain, rheumatoid arthritis, osteoarthritis, fibromyalgia, HIV/AIDS, cancer, and MS or paraplegia.

When restricting to moderate-to-high-quality reviews, only one review each in multiple sclerosis and HIV/AIDS with a study-by-study analysis on pain remained. One review on cancer favored cannabis for pain reduction. Findings remained the same for MS or paraplegia and rheumatic disease. No review for injury and paint outcomes was of higher quality.

Non-pain outcomes

The types of non-pain outcomes included in the reviews varied by condition/illness. The most commonly reported outcomes (see Fig. 5 for overall outcomes) when comparing cannabis to placebo included muscle- or movement-related outcomes ( n = 20), quality of life ( n = 14), and sleep outcomes ( n = 10).

There was no consistent pattern for non-pain outcomes either within or across medical conditions. Many ( n = 24, 33%) reviews assessing non-pain outcomes reported the results of those analyses study-by-study. Conflicting results are observed in some cases due to the use of different measures, such as different ways of quantifying spasticity in patients with multiple sclerosis [ 56 , 91 ]. One review each addressing neurological conditions [ 50 ] (outcome: muscle cramps) and MS/paraplegia [ 27 ] (outcomes: spasticity, spasm, cognitive function, daily activities, motricity, and bladder function) showed no difference between groups.

Adverse effects

Adverse effects were reported in most reviews comparing cannabis with placebo (49/59, 83%). Most adverse events were reported study-by-study, with few reviews ( n = 16/59, 27%) conducting a narrative or quantitative synthesis. Serious adverse effects were reported in 21/59 (36%) reviews, and minor adverse effects were reported in 30/59 (51%) reviews. The remaining reviews did not define the difference between serious and minor adverse events. The most commonly reported serious adverse events included psychotic symptoms ( n = 6), severe dysphoric reactions ( n = 3), seizure ( n = 3), and urinary tract infection ( n = 2). The most commonly reported minor adverse events included somnolence/drowsiness ( n = 28), dizziness ( n = 27), dry mouth ( n = 20), and nausea ( n = 18). Many reviews ( n = 37/59, 63%) comparing cannabis to placebo reported both neurocognitive and non-cognitive adverse effects. Withdrawals due to adverse events were reported in 22 (37%) reviews.

Of the moderate-/high-quality reviews, adverse effect analyses were reported in reviews on pain, multiple sclerosis, cancer, HIV/AIDS, movement disorders, rheumatic disease, and several other conditions. Two reviews on pain showed fewer adverse events with cannabis for euphoria, events linked to alternations in perception, motor function, and cognitive function, withdrawal due to adverse events, sleep, and dizziness or vertigo [ 58 , 90 ]. One review on MS showed that there was no statistically significant difference between cannabis and placebo for adverse effects such as nausea, weakness, somnolence, and fatigue [ 91 ], while another review on MS/paraplegia reported fewer events in the placebo group for dizziness, somnolence, nausea, and dry mouth [ 27 ]. Within cancer reviews, one review found no statistically significant difference between cannabis and placebo for dysphoria or sedation but reported fewer events with placebo for “feeling high,” and fewer events with cannabis for withdrawal due to adverse effects [ 40 ]. In rheumatic disease, one review reported fewer total adverse events with cannabis and found no statistically significant difference between cannabis and placebo for withdrawal due to adverse events [ 51 ].

Cannabis versus other drugs

Relatively fewer reviews compared cannabis with active drugs ( n = 23/72, 32%) (Fig. 8 ). Many of the reviews did not synthesize studies quantitatively, and results were reported study-by-study. The most common conditions in reviews comparing cannabis to active drugs were pain, cancer, and rheumatic disease. Comparators included ibuprofen, codeine, diphenhydramine, amitriptyline, secobarbital, prochlorperazine, domperidone, metoclopramide, amisulpride, neuroleptics, isoproterenol, megestrol acetate, pregabalin, gabapentin, and opioids.

Reviews focused on addressing pain across conditions. When comparing across reviews, a mix of results are observed (see Fig. 8 ), and some were reported study-by-study. One review found no statistically significant difference between cannabinoids and codeine for nociceptive pain, postoperative pain, and cancer pain [ 65 ]. Another review favored “other drugs” (amitriptyline and pregabalin) over cannabinoids for neuropathic pain [ 90 ]. The distribution of findings was similar when restricting to moderate-to-high-quality reviews.

Reviews focused on treating a condition or family of related conditions. One review on cancer compared cannabinoids and codeine or secobarbital and reported pain results study-by-study. Another review on fibromyalgia comparing synthetic cannabinoids with amitriptyline also reported pain data study-by-study [ 39 ].

Two reviews on cancer favored cannabinoids over active drugs (prochlorperazine, domperidone, metoclopramide, and neuroleptics) for patient preference and anti-emetic efficacy [ 40 , 60 ]. Non-pain outcomes were reported study-by-study for the outcome of sleep in neuropathic pain [ 90 ] and rheumatic disease [ 39 , 49 ]. In a review covering various conditions (pain, MS, anorexia, cancer, and immune deficiency), results were unclear or indeterminate for subjective measures of sleep [ 46 ].

Adverse effects were reported in 20/24 (83%) of the reviews comparing cannabis to active drugs, and only 6/20 (30%) reported a narrative or quantitative synthesis. Many reviews that reported narrative data did not specify whether adverse effects could be attributed to a placebo or active drug comparator.

Of the moderate-to-high-quality reviews, two pain reviews found no statistically significant difference for cannabis compared to codeine or amitriptyline for withdrawals due to adverse events [ 65 , 90 ]. Results from one cancer review were mixed, with fewer adverse events for cannabis (compared to prochlorperazine, domperidone, or metoclopramide) or no difference between groups, depending on the type of subgroup analysis that was conducted [ 40 ].

Cannabis + active drugs versus placebo + active drugs

Two reviews compared cannabis with placebo cannabis in combination with an active drug (opioids and gabapentin) (Figs. 10 and 11 ). Both were scored to be of moderate quality. Although one review showed that cannabis plus opioids decreased chronic pain [ 80 ], another review on pain in MS included only a single study [ 81 ], precluding the ability to determine concordance of results. Cannabis displayed varied effects on non-pain outcomes, including superiority of placebo over cannabis for some outcomes. One review reported withdrawal due to adverse events study-by-study and also reported that side effects such as nausea, drowsiness, and dizziness were more frequent with higher doses of cannabinoids (data from two included studies) [ 80 ].

Cannabis versus other cannabis comparisons

Six (8%) reviews compared different cannabis formulations or doses (Figs. 12 and 13 ). Almost all were reported as study-by-study results, with two reviews including only one RCT. One review for PTSD found only observational data [ 33 ] and another review on anxiety and depression combined data from one RCT with cross-sectional study data [ 19 ]. A single review on MS reported a narrative synthesis that found a benefit for spasticity. However, it was unclear if the comparator was placebo or THC alone [ 56 ]. Four reviews reported adverse effects study-by-study, with a single review comparing side effects from different dosages; in this review, combined extracts of THC and CBD were better tolerated than extracts of THC alone [ 56 ].

Cannabis versus all comparators

One review combined all comparators for the evaluation (Fig. 14 ). The review (combining non-users, placebo and ibuprofen) covered a range of medical conditions and was rated as low quality [ 30 ]. No adverse effects were evaluated for this comparison.

Mapping the use of quality assessment and frameworks to interpret the strength of evidence

Although 83% of reviews incorporated risk of bias assessments in their interpretation of the evidence, only 11 (15%) reviews used a framework such as GRADE to evaluate important domains other than risk of bias that would inform the strength of the evidence.

Mapping authors’ conclusions or recommendations

Most reviews (43/72 60%) indicated an inability to draw conclusions, whether due to uncertainty, inconsistent findings, lack of (high quality) evidence, or focusing their conclusion statement on the need for more research. Almost 15% of reviews (10/72) reported recommendations or conclusions that included some uncertainty. One review (1%) provided a statement of the extent of the strength of the evidence, which differed according to outcome.

Eleven reviews provided clearer conclusions (14%). Four indicated that cannabis was not effective or not cost-effective compared to placebo in relation to multiple sclerosis, acute pain, cancer, and injury. Three reviews addressing various conditions provided varying conclusions: one stated cannabis was not effective, one indicated it was modestly safe and effective, and one concluded that cannabis was safe and efficacious as short-term treatment; all reviews were of low quality. The three remaining reviews stated moderate or modest effects for improving chronic pain, compared with placebo or other analgesia; two of those reviews were of medium AMSTAR-2 quality, and one used the GRADE framework for interpreting the strength of the evidence.

The eight remaining included reviews (11%) did not provide a clear conclusion statement or reported only limitations.

Mapping authors’ limitations of the research

Several of the reviews indicated that few studies, small sample sizes, short duration of treatment, and issues related to outcomes (e.g., definition, timing, and types) were drawbacks to the literature. Some reviews noted methodological issues with and heterogeneity among studies as limitations. A few authors stated that restricting eligibility to randomized trials, English-language studies, or full publications may have affected their review results.

With the increasing use of medical cannabis, an understanding of the landscape of available evidence syntheses is needed to support evidence-informed decision-making, policy development, and to inform a research agenda. In this scoping review, we identified 72 systematic reviews evaluating medical cannabis for a range of conditions and illnesses. Half of the reviews were evaluated as being of moderate quality, with only one review scoring high on the AMSTAR-2 assessment tool.

There was disparity in the reported results across reviews, including non-synthesized (study-by-study) data, and many were unable to provide a definitive statement regarding the effectiveness of cannabis (as measured by pain reduction or other relevant outcomes), nor the extent of increased side effects and harms. This is consistent with the limitations declared in general across reviews, such as the small numbers of relevant studies, small sample sizes of individual studies, and methodological weaknesses of available studies. This common theme in review conclusions suggests that while systematic reviews may have been conducted with moderate or high methodological quality, the strength of their conclusions are driven by the availability and quality of the relevant underlying evidence, which was often found to be limited.

Relatively fewer reviews addressed adverse effects associated with cannabis, except to narratively summarize study level data. Although information was provided for placebo-controlled comparisons, none of the comparative effectiveness reviews quantitatively assessed adverse effects data. For the placebo-controlled data, although the majority of adverse effects were mild, the number of reviews reporting serious adverse effects such as psychotic symptoms [ 25 , 42 ] and suicidal ideation [ 68 , 85 ] warrants caution.

A mix of reviews supporting and not supporting the use of cannabis, according to authors’ conclusions, was identified. Readers may wish to consider the quality of the reviews, the use of differing quality assessment tools, additional considerations covered by the GRADE framework, and the potential for spin as possible reasons for these inconsistencies. It is also possible that cannabis has differing effects depending on its type (e.g., synthetic), dose, indication, the type of pain being evaluated (e.g., neuropathic), and the tools used for outcome assessment, which can be dependent on variations in condition. Of potential interest to readers may be a closer examination of the reviews evaluating chronic pain, in order to locate the source(s) of discordance. For example, one review was deemed of moderate quality, used the GRADE framework, and rated the quality of evidence for the effectiveness of cannabis for reducing neuropathic pain as moderate, suggesting that further investigation of cannabis for neuropathic pain may be warranted [ 80 ]. The exploration aspects outlined in this paragraph are beyond the purview of scoping review methodology; a detailed assessment of the reviews, including determining the overlap of included studies among similar reviews, potential reasons for the observed discordance of findings, what re-analysis of study-by-study analyses would yield, and an undertaking of missing GRADE assessments would fall outside the bounds of a scoping review and require the use of overview methodology [ 14 ].

Our findings are consistent with a recently published summary of cannabis-based medicines for chronic pain management [ 3 ]. This report found inconsistent results in systematic reviews of cannabis-based medicines compared to placebo for chronic neuropathic pain, pain management in rheumatic diseases and painful spasms in MS. The authors also concluded that cannabis was not superior to placebo in reducing cancer pain. Four out of eight included reviews scored high on the original AMSTAR tool. The variations between the two tools can be attributed to the differences in our overall assessments. Lastly, the summary report included two reviews that were not located in our original search due to language [ 93 ] and the full-text [ 94 ] of an abstract [ 95 ] that was not located in our search.

This scoping review has identified a plethora of synthesized evidence in relation to medical cannabis. For some conditions, the extent of review replication may be wasteful. Many reviews have stated that additional trials of methodologically robust design and, where possible, of sufficient sample size for precision, are needed to add to the evidence base. This undertaking may require the coordination of multi-center studies to ensure adequate power. Future trials may also help to elucidate the effect of cannabis on different outcomes.

Given authors’ reporting of issues in relation to outcomes, future prospective trials should be guided by a standardized, “core” set of outcomes to strive for consistency across studies and ensure relevance to patient-centered care. Development of those core outcomes should be developed using the Core Outcome Measures in Effectiveness Trials (COMET) methodology [ 96 ], and further consideration will need to be made in relation to what outcomes may be common across all cannabis research and which outcomes are condition-specific. With maturity of the evidence base, future systematic reviews should seek and include non-journal-published (gray literature) reports and ideally evaluate any non-English-language papers; authors should also adequately assess risk of bias and undertake appropriate syntheses of the literature.

The strengths of this scoping review include the use of an a priori protocol, peer-reviewed search strategies, a comprehensive search for reviews, and consideration of observational designs for adverse effects data. For feasibility, we restricted to English-language reviews, and it is unknown how many of the 39 reviews in other languages that we screened would have met our eligibility criteria. The decision to limit the inclusion of reviews of observational data to adverse effects data was made during the process of full-text screening and for pragmatic reasons. We also did not consider a search of the PROSPERO database for ongoing systematic reviews; however, in preparing this report, we performed a search and found that any completed reviews were already considered for eligibility or were not available at the time of our literature search. When charting results, we took a broad perspective, which may be different than if these reviews were more formally assessed during an overview of systematic reviews.

Cannabis-based medicine is a rapidly emerging field of study, with implications for both healthcare practitioners and patients. This scoping review is intended to map and collate evidence on the harms and benefits of medical cannabis. Many reviews were unable to provide firm conclusions on the effectiveness of medical cannabis, and results of reviews were mixed. Mild adverse effects were frequently but inconsistently reported, and it is possible that harms may outweigh benefits. Evidence from longer-term, adequately powered, and methodologically sound RCTs exploring different types of cannabis-based medicines is required for conclusive recommendations.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Abbreviations

Canadian Agency for Drugs and Technologies in Health

Complementary and alternative medicine

Cannabidiol

Grading of Recommendations Assessment, Development and Evaluation

Human immunodeficiency virus

Inflammatory bowel disease

Multiple sclerosis

Numeric rating scale

Randomized controlled trial

Rheumatic disease

Risk of bias

Tetrahydrocannabinol

Visual analog scale

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MP, AS, and BH drafted the initial version of the report. BS designed and implemented the literature search. MP, MT, and CB contributed to review of abstracts and full texts as well as data collection. MP, AS, and BH were responsible for analyses. All authors (MP, AS, MT, CB, BS, SW, MC, SK, BH) contributed to interpretation of findings and revision of drafts and approved the final version of the manuscript.

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PRISMA Scoping Review Extension Completed Checklist.

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Literature Search Strategies.

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Grey Literature Sources.

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Listing of Data Extraction Items.

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Data extractions from included studies.

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Pratt, M., Stevens, A., Thuku, M. et al. Benefits and harms of medical cannabis: a scoping review of systematic reviews. Syst Rev 8 , 320 (2019). https://doi.org/10.1186/s13643-019-1243-x

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Whilst access to cannabis-based medicinal products (CBMPs) has increased globally subject to relaxation of scheduling laws globally, one of the main barriers to appropriate patient access remains a paucity of high-quality evidence surrounding their clinical effects.

Whilst randomised controlled trials (RCTs) remain the gold-standard for clinical evaluation, there are notable barriers to their implementation. Development of CBMPs requires novel approaches of evidence collection to address these challenges. Real world evidence (RWE) presents a solution to not only both provide immediate impact on clinical care, but also inform well-conducted RCTs. RWE is defined as evidence derived from health data sourced from non-interventional studies, registries, electronic health records and insurance data. Currently it is used mostly to monitor post-approval safety requirements allowing for long-term pharmacovigilance. However, RWE has the potential to be used in conjunction or as an extension to RCTs to both broaden and streamline the process of evidence generation.

Novel approaches of data collection and analysis will be integral to improving clinical evidence on CBMPs. RWE can be used in conjunction or as an extension to RCTs to increase the speed of evidence generation, as well as reduce costs. Currently, there is an abundance of potential data however, whilst a number of platforms now exist to capture real world data it is important the right tools and analysis are utilised to unlock potential insights from these.

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Cannabis-based medicinal products (CBMPs) are a collective term to describe a preparation or other product that contains cannabis or its derivatives for medicinal use in humans [ 1 ]. There are significant barriers to the integration of CBMPs within treatment pathways including ongoing stigma, cost, education, complex pharmacology and a paucity of evidence to inform international and national guidelines [ 2 , 3 ]. Limited evidence, does, however, support the role of CBMPs in conditions such as chronic pain, neurological disorders, and psychiatric disease [ 4 ]. There is also growing evidence of side effects and how the severity and incidence of side effects may differ between patients [ 4 ]. The quality of evidence, however, is often insufficient in the opinion of insurers, regulators, and guideline bodies [ 5 ].

The National Institute for Health and Care Excellence in the UK has only recommended licensed CBMPs for a limited range of indications [ 6 ]. Changes to scheduling as recommended by the World Health Organisation, and within individual countries, recognises the potential medicinal value of cannabis and removes barriers for clinical and research use [ 1 , 7 ]. However, widespread stigma, complex pharmacology, funding, and challenges in sustaining adequate supply of consistent products continue to act as barriers for clinical research.

Randomised controlled trials (RCTs) are necessary and should continue to be the standard against which medical evidence is upheld. However, they are expensive, time consuming and subject to their own limitations [ 8 ]. Whilst these are awaited, there is a requirement to generate evidence of potential benefits and harms to inform policy and clinical practice.

Barriers to Controlled Clinical Trials for Medical Cannabis

RCTs are not infallible—they are expensive and time consuming. Globally $100 billion USD is spent on biomedical research [ 9 ]. In the UK, the National Institute for Health Research (NIHR) provides £80 million GBP in funding for clinical trials [ 10 ]. Yet, their narrow scope can lack ecological validity to real-world circumstances and therefore lack generalisability in more diverse populations. There are also specific barriers to conducting RCTs using CBMPs.

Complex Pharmacology

In addition to cannabidiol (CBD) and (−)-trans-Δ 9 -tetrahydrocannabinol (THC) there are over 140 cannabinoids, as well as flavonoids, terpenes, and other compounds within the flower of different cannabis plants [ 8 ]. These can each potentially affect the clinical outcomes observed between CBMPs due to their individual and collective effects [ 11 ]. The concentrations of each compound are influenced by the genetics and environment each plant is grown in producing a distinct chemical profile. The result of a clinical trial for one finished pharmaceutical product, therefore, cannot be extrapolated to all CBMPs, due to their heterogeneity. However, current evidence reviews often fail to account for this [ 12 , 13 ].

The route of administration further affects the pharmacokinetics of CBMPs and the associated outcome of any trial. CBMPs can be administered sublingually, trans-dermally, via inhalation, or orally [ 14 ]. This subsequently affects the distribution, biotransformation and elimination of active compounds. Heat exposure and vaporisation of dried flower or extracted oils changes the underlying phytocannabinoid composition compared to the original unprocessed dry flowers, increasing the proportion of decarboxylated cannabinoids [ 15 , 16 ]. Assessment of efficacy using RCTs in isolation will therefore ultimately fail to identify the most appropriate CBMP for each clinical scenario [ 17 ].

Placebo-control

An appropriately blinded assessment against placebo or active therapy is the optimal design for RCTs. It has been difficult to identify a placebo that cannot be distinguished against an active CBMP according to absence of both vasoactive and psychoactive effects, as well as the typical aroma associated with cannabis [ 15 ]. This presents a challenge to adequate blinding.

Production methods and import costs mean that CBMPs are typically expensive, adding further to high research costs [ 18 ]. Research has therefore focused on compounds under patent as opposed to generic CBMPs where research outcomes fail to provide a similar return on investment for licensed producers and pharmaceutical companies. Historically, clinical trials on CBMPs were funded privately, which may be associated with potential reporting biases [ 19 ].

RCTs are possible with CBMPs; however, the above issues present legitimate challenges. In many chronic diseases there is a need for novel therapeutics and CBMPs are therefore being utilised based on best available evidence. Due to the challenges in developing CBMPs through a traditional drug development pipeline, the exploration of its utility should not be limited to traditional methods. It is important that we capture a suite of real-world evidence (RWE) to inform prescribing guidelines, regulations, and clinical trials. By leaning on RWE there is an opportunity to improve the quality and design of RCTs and clinical evidence in general, via a top-down approach [ 20 ].

Real World Evidence

RWE is defined as evidence derived from health data sourced from non-interventional studies, registries, electronic health records and insurance data as opposed to the highly controlled setting of RCTs [ 21 ]. There is an abundance of this unstructured data, however, the necessary frameworks and governance are needed for the application of this data [ 22 ]. It is currently used extensively to monitor post-approval pharmacovigilence [ 23 ]. There is clear evidence of benefit in using population-based data to detect safety events associated with specific medications to implement restrictions to reduce harm [ 21 ].

Consistent use of RWE to aid regulatory decision making is yet to be normalised, but the promise is apparent [ 21 ]. Recently, regulator-supported initiatives have highlighted the desire to incorporate RWE into licensing and guidelines, developing a framework which can incorporate its insights into decisions regarding safety and effectiveness [ 21 , 22 ]. It is important that studies standardise their methodology according to those set out by regulatory authorities to ensure research has the greatest impact [ 21 , 22 ]. Moreover, they should seek to directly address questions set out by governing bodies as areas where there is insufficient research [ 24 ].

Types of Real-World Evidence for Medical Cannabis

NHS England and NHS improvement published a review on the barriers to accessing CBMPs in the UK [ 3 ]. Their recommendations included the need for the collection of structured data, and the development of methods to further support the generation of new evidence, for patients who cannot enrol onto relevant RCTs.

RWE is already being incorporated into the scientific literature on cannabis (Table 1 ). Early examples utilised state-level records to examine the effects of cannabis laws on opioid misuse. Subsequently there have been examples of online and self-administered survey tools analysing national outcomes. More recently there has been a focus on collecting evidence from clinical registries and databases with evidence generated from patient-reported outcome measures and long-term pharmacovigilance.

Comparison of Real-World Evidence and Controlled Clinical Trials

Between these study designs it is important to be aware of potential divergence in reported outcomes. RWE has broader inclusion criteria, accounting for factors like non-standard dosing, and is not limited by scope of disease, thereby improving ecological validity [ 25 ]. However, some studies have concluded there is little difference between results obtained via RCTs and observational studies [ 26 ]. RWE typically has longer patient follow-up and may consequently capture rare but important adverse effects that are not detected within RCTs. Pharmacovigilance is therefore widely accepted as one of the most important roles of RWE.

RWE can bring further clarity on questions that remain unanswered in RCTs. A recent study utilised anonymised surveys of patients with fibromyalgia who consumed cannabis flower [ 27 ]. In addition to reporting positive outcomes on depression and pain the study also reported negative aspects of cannabis consumption, for example driving under the influence (72% of patients) [ 27 ]. These are findings which are unlikely to be reported by patients in controlled clinical trials for fear of repercussions, or strict inclusion criteria. It can also be useful in collecting data in rare conditions whereby recruitment to RCTs can be limited by the need for defined trial sites.

RWE can improve the efficiency of clinical trials by generating hypotheses, refining eligibility criteria, and exploring drug development tools. Registries can be used to form an infrastructure to conduct a clinical trial, lowering costs whilst maintaining high evidence quality [ 28 ]. In supplemented single arm trials the controls are derived from RWE-data sets, providing the opportunity for patient centric study designs. RCTs can also be augmented with real-world data to increase the size of the control group to increase the power of the study. These study designs are particularly useful for rare diseases where participant recruitment is challenging [ 29 ].

Limitations of Real-World Evidence

RWE, however, does have limits to its utility. There is variation in the quality and provenance of the data stored in electronic medical records [ 5 ]. Furthermore, insurance records typically use coding specific for reimbursement purposes and may not provide all clinically relevant information. RWE can require complex statistical expertise to deduce valid conclusions.

Another limitation is the lack of randomisation, controlled variables and internal validity. This can make it more difficult to derive causative mechanisms behind clinical outcomes. However, this is also one of the strengths of these studies, allowing for generalisability to true clinical practice [ 22 ]. Treatment assignment based on the physician as opposed to randomisation, creates selection bias and more specifically stigma biases. RCTs, therefore, are still necessary to establish a strong causal relationship between medication and outcomes [ 30 ].

CBMPs are a complex range of pharmaceuticals which pose challenges to traditional pathways of drug development and translation. Development of CBMPs requires novel approaches of evidence collection to address these challenges. RWE can be used in conjunction or as an extension to RCTs to both broaden and streamline the process of evidence generation. Currently, there is an abundance of potential data, however, it is important the right tools and analysis are utilised to unlock potential insights from these.

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Abbreviations

• Cannabidiol
• Cannabis-based medicinal products

Food and Drug Administration

National Health Service

National Institute for Health Research

Randomised Controlled Trials

• Real world evidence

Supplemented single arm trials

Trans-Δ 9 -tetrahydrocannabinol

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Acknowledgements

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Department of Surgery and Cancer, Imperial College London, London, UK

Rishi Banerjee, Simon Erridge, Oliver Salazar, Nagina Mangal & Mikael Hans Sodergren

Sapphire Medical Clinics, UK Medical Cannabis Registry, London, UK

Simon Erridge & Mikael Hans Sodergren

The Centre for Medicinal Cannabis, 18 Hanway Street, London, W1T 1UF, UK

Daniel Couch

Curaleaf International, London, UK

Barbara Pacchetti & Mikael Hans Sodergren

Division of Surgery, Department of Surgery & Cancer, Imperial College London, St Mary’s Hospital, Academic Surgical Unit, 10th Floor QEQM, South Wharf Road, London, W2 1NY, UK

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RB and SE prepared the manuscript. OS, NM, DC, BP, MS read and approved the final manuscript.

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SE: Sapphire Medical Clinics. DC: Medical Lead Centre for Medicinal Cannabis. BP: Chief Scientific Officer at Emmac Life Sciences. MHS: Sapphire Medical Clinics Managing Director and Research lead at Emmac Life Sciences.

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Banerjee, R., Erridge, S., Salazar, O. et al. Real World Evidence in Medical Cannabis Research. Ther Innov Regul Sci 56 , 8–14 (2022). https://doi.org/10.1007/s43441-021-00346-0

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• What is Public Health?

The Evidence—and Lack Thereof—About Cannabis

Research is still needed on cannabis’s risks and benefits. 

Lindsay Smith Rogers

Although the use and possession of cannabis is illegal under federal law, medicinal and recreational cannabis use has become increasingly widespread.

Thirty-eight states and Washington, D.C., have legalized medical cannabis, while 23 states and D.C. have legalized recreational use. Cannabis legalization has benefits, such as removing the product from the illegal market so it can be taxed and regulated, but science is still trying to catch up as social norms evolve and different products become available. 

In this Q&A, adapted from the August 25 episode of Public Health On Call , Lindsay Smith Rogers talks with Johannes Thrul, PhD, MS , associate professor of Mental Health , about cannabis as medicine, potential risks involved with its use, and what research is showing about its safety and efficacy. 

Do you think medicinal cannabis paved the way for legalization of recreational use?

The momentum has been clear for a few years now. California was the first to legalize it for medical reasons [in 1996]. Washington and Colorado were the first states to legalize recreational use back in 2012. You see one state after another changing their laws, and over time, you see a change in social norms. It's clear from the national surveys that people are becoming more and more in favor of cannabis legalization. That started with medical use, and has now continued into recreational use.

But there is a murky differentiation between medical and recreational cannabis. I think a lot of people are using cannabis to self-medicate. It's not like a medication you get prescribed for a very narrow symptom or a specific disease. Anyone with a medical cannabis prescription, or who meets the age limit for recreational cannabis, can purchase it. Then what they use it for is really all over the place—maybe because it makes them feel good, or because it helps them deal with certain symptoms, diseases, and disorders.

Does cannabis have viable medicinal uses?

The evidence is mixed at this point. There hasn’t been a lot of funding going into testing cannabis in a rigorous way. There is more evidence for certain indications than for others, like CBD for seizures—one of the first indications that cannabis was approved for. And THC has been used effectively for things like nausea and appetite for people with cancer.

There are other indications where the evidence is a lot more mixed. For example, pain—one of the main reasons that people report for using cannabis. When we talk to patients, they say cannabis improved their quality of life. In the big studies that have been done so far, there are some indications from animal models that cannabis might help [with pain]. When we look at human studies, it's very much a mixed bag. 

And, when we say cannabis, in a way it's a misnomer because cannabis is so many things. We have different cannabinoids and different concentrations of different cannabinoids. The main cannabinoids that are being studied are THC and CBD, but there are dozens of other minor cannabinoids and terpenes in cannabis products, all of varying concentrations. And then you also have a lot of different routes of administration available. You can smoke, vape, take edibles, use tinctures and topicals. When you think about the explosion of all of the different combinations of different products and different routes of administration, it tells you how complicated it gets to study this in a rigorous way. You almost need a randomized trial for every single one of those and then for every single indication.

What do we know about the risks of marijuana use?  

Cannabis use disorder is a legitimate disorder in the DSM. There are, unfortunately, a lot of people who develop a problematic use of cannabis. We know there are risks for mental health consequences. The evidence is probably the strongest that if you have a family history of psychosis or schizophrenia, using cannabis early in adolescence is not the best idea. We know cannabis can trigger psychotic symptoms and potentially longer lasting problems with psychosis and schizophrenia. 

It is hard to study, because you also don't know if people are medicating early negative symptoms of schizophrenia. They wouldn't necessarily have a diagnosis yet, but maybe cannabis helps them to deal with negative symptoms, and then they develop psychosis. There is also some evidence that there could be something going on with the impact of cannabis on the developing brain that could prime you to be at greater risk of using other substances later down the road, or finding the use of other substances more reinforcing. 

What benefits do you see to legalization?

When we look at the public health landscape and the effect of legislation, in this case legalization, one of the big benefits is taking cannabis out of the underground illegal market. Taking cannabis out of that particular space is a great idea. You're taking it out of the illegal market and giving it to legitimate businesses where there is going to be oversight and testing of products, so you know what you're getting. And these products undergo quality control and are labeled. Those labels so far are a bit variable, but at least we're getting there. If you're picking up cannabis at the street corner, you have no idea what's in it. 

And we know that drug laws in general have been used to criminalize communities of color and minorities. Legalizing cannabis [can help] reduce the overpolicing of these populations.

What big questions about cannabis would you most like to see answered?

We know there are certain, most-often-mentioned conditions that people are already using medical cannabis for: pain, insomnia, anxiety, and PTSD. We really need to improve the evidence base for those. I think clinical trials for different cannabis products for those conditions are warranted.

Another question is, now that the states are getting more tax revenue from cannabis sales, what are they doing with that money? If you look at tobacco legislation, for example, certain states have required that those funds get used for research on those particular issues. To me, that would be a very good use of the tax revenue that is now coming in. We know, for example, that there’s a lot more tax revenue now that Maryland has legalized recreational use. Maryland could really step up here and help provide some of that evidence.

Are there studies looking into the risks you mentioned?

Large national studies are done every year or every other year to collect data, so we already have a pretty good sense of the prevalence of cannabis use disorder. Obviously, we'll keep tracking that to see if those numbers increase, for example, in states that are legalizing. But, you wouldn't necessarily expect to see an uptick in cannabis use disorder a month after legalization. The evidence from states that have legalized it has not demonstrated that we might all of a sudden see an increase in psychosis or in cannabis use disorder. This happens slowly over time with a change in social norms and availability, and potentially also with a change in marketing. And, with increasing use of an addictive substance, you will see over time a potential increase in problematic use and then also an increase in use disorder.

If you're interested in seeing if cannabis is right for you, is this something you can talk to your doctor about?

I think your mileage may vary there with how much your doctor is comfortable and knows about it. It's still relatively fringe. That will very much depend on who you talk to. But I think as providers and professionals, everybody needs to learn more about this, because patients are going to ask no matter what.

Lindsay Smith Rogers, MA, is the producer of the Public Health On Call podcast , an editor for Expert Insights , and the director of content strategy for the Johns Hopkins Bloomberg School of Public Health.

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Cannabis (Marijuana) DrugFacts

What is marijuana.

Marijuana refers to the dried leaves, flowers, stems, and seeds from the Cannabis sativa or Cannabis indica plant. The plant contains the mind-altering chemical THC and other similar compounds. Extracts can also be made from the cannabis plant (see " Marijuana Extracts ").

According to the National Survey on Drug Use and Health , cannabis (marijuana) is one of the most used drugs in the United States, and its use is widespread among young people. In 2021, 35.4% of young adults aged 18 to 25 (11.8 million people) reported using marijuana in the past year. 1 According to the Monitoring the Future survey , rates of past year marijuana use among middle and high school students have remained relatively steady since the late 1990s. In 2022, 30.7% of 12th graders reported using marijuana in the past year and 6.3% reported using marijuana daily. In addition, many young people also use vaping devices to consume cannabis products. In 2022, nearly 20.6% of 12th graders reported that they vaped marijuana in the past year and 2.1% reported that they did so daily. 2

Legalization of marijuana for medical use or adult recreational use in a growing number of states may affect these views. Read more about marijuana as medicine in our DrugFacts: Marijuana as Medicine .

How do people use marijuana?

People smoke marijuana in hand-rolled cigarettes (joints) or in pipes or water pipes (bongs). They also smoke it in blunts—emptied cigars that have been partly or completely refilled with marijuana. To avoid inhaling smoke, some people are using vaporizers. These devices pull the active ingredients (including THC) from the marijuana and collect their vapor in a storage unit. A person then inhales the vapor, not the smoke. Some vaporizers use a liquid marijuana extract.

People can mix marijuana in food ( edibles ), such as brownies, cookies, or candy, or brew it as a tea. A newly popular method of use is smoking or eating different forms of THC-rich resins (see " Marijuana Extracts ").

Marijuana Extracts

Smoking THC-rich resins extracted from the marijuana plant is on the rise. People call this practice dabbing . These extracts come in various forms, such as:

• hash oil or honey oil —a gooey liquid
• wax or budder —a soft solid with a texture like lip balm
• shatter —a hard, amber-colored solid

These extracts can deliver extremely large amounts of THC to the body, and their use has sent some people to the emergency room. Another danger is in preparing these extracts, which usually involves butane (lighter fluid). A number of people have caused fires and explosions and have been seriously burned from using butane to make extracts at home. 3,4

How does marijuana affect the brain?

Marijuana has both short-and long-term effects on the brain.

Short-Term Effects

When a person smokes marijuana, THC quickly passes from the lungs into the bloodstream. The blood carries the chemical to the brain and other organs throughout the body. The body absorbs THC more slowly when the person eats or drinks it. In that case, they generally feel the effects after 30 minutes to 1 hour.

THC acts on specific brain cell receptors that ordinarily react to natural THC-like chemicals. These natural chemicals play a role in normal brain development and function.

Marijuana over activates parts of the brain that contain the highest number of these receptors. This causes the "high" that people feel. Other effects include:

• altered senses (for example, seeing brighter colors)
• altered sense of time
• changes in mood
• impaired body movement
• difficulty with thinking and problem-solving
• impaired memory
• hallucinations (when taken in high doses)
• delusions (when taken in high doses)
• psychosis (risk is highest with regular use of high potency marijuana)

Long-Term Effects

Marijuana also affects brain development. When people begin using marijuana as teenagers, the drug may impair thinking, memory, and learning functions and affect how the brain builds connections between the areas necessary for these functions. Researchers are still studying how long marijuana's effects last and whether some changes may be permanent.

For example, a study from New Zealand conducted in part by researchers at Duke University showed that people who started smoking marijuana heavily in their teens and had an ongoing marijuana use disorder lost an average of 8 IQ points between ages 13 and 38. The lost mental abilities didn't fully return in those who quit marijuana as adults. Those who started smoking marijuana as adults didn't show notable IQ declines. 5

In another recent study on twins, those who used marijuana showed a significant decline in general knowledge and in verbal ability (equivalent to 4 IQ points) between the preteen years and early adulthood, but no predictable difference was found between twins when one used marijuana and the other didn't. This suggests that the IQ decline in marijuana users may be caused by something other than marijuana, such as shared familial factors (e.g., genetics, family environment). 6 NIDA’s Adolescent Brain Cognitive Development (ABCD) study, a major longitudinal study, is tracking a large sample of young Americans from late childhood to early adulthood to help clarify how and to what extent marijuana and other substances, alone and in combination, affect adolescent brain development. Read more about the ABCD study on our Longitudinal Study of Adolescent Brain and Cognitive Development (ABCD Study) webpage.

A Rise in Marijuana’s THC Levels

The amount of THC in marijuana has been increasing steadily over the past few decades. 7 For a person who's new to marijuana use, this may mean exposure to higher THC levels with a greater chance of a harmful reaction. Higher THC levels may explain the rise in emergency room visits involving marijuana use.

The popularity of edibles also increases the chance of harmful reactions. Edibles take longer to digest and produce a high. Therefore, people may consume more to feel the effects faster, leading to dangerous results.

Higher THC levels may also mean a greater risk for addiction if people are regularly exposing themselves to high doses.

What are the other health effects of marijuana?

Marijuana use may have a wide range of effects, both physical and mental.

Physical Effects

• Breathing problems. Marijuana smoke irritates the lungs, and people who smoke marijuana frequently can have the same breathing problems as those who smoke tobacco. These problems include daily cough and phlegm, more frequent lung illness, and a higher risk of lung infections. Researchers so far haven't found a higher risk for lung cancer in people who smoke marijuana. 8
• Increased heart rate. Marijuana raises heart rate for up to 3 hours after smoking. This effect may increase the chance of heart attack. Older people and those with heart problems may be at higher risk.
• Problems with child development during and after pregnancy. One study found that about 20% of pregnant women 24-years-old and younger screened positive for marijuana. However, this study also found that women were about twice as likely to screen positive for marijuana use via a drug test than they state in self-reported measures. 9 This suggests that self-reported rates of marijuana use in pregnant females is not an accurate measure of marijuana use and may be underreporting their use. Additionally, in one study of dispensaries, nonmedical personnel at marijuana dispensaries were recommending marijuana to pregnant women for nausea, but medical experts warn against it. This concerns medical experts because marijuana use during pregnancy is linked to lower birth weight 10 and increased risk of both brain and behavioral problems in babies. If a pregnant woman uses marijuana, the drug may affect certain developing parts of the fetus's brain. Children exposed to marijuana in the womb have an increased risk of problems with attention, 11 memory, and problem-solving compared to unexposed children. 12 Some research also suggests that moderate amounts of THC are excreted into the breast milk of nursing mothers. 13 With regular use, THC can reach amounts in breast milk that could affect the baby's developing brain. Other recent research suggests an increased risk of preterm births. 27 More research is needed. Read our Marijuana Research Report for more information about marijuana and pregnancy.
• Intense nausea and vomiting. Regular, long-term marijuana use can lead to some people to develop Cannabinoid Hyperemesis Syndrome. This causes users to experience regular cycles of severe nausea, vomiting, and dehydration, sometimes requiring emergency medical attention. 14

Reports of Deaths Related to Vaping

The Food and Drug Administration has alerted the public to hundreds of reports of serious lung illnesses associated with vaping, including several deaths. They are working with the Centers for Disease Control and Prevention (CDC) to investigate the cause of these illnesses. Many of the suspect products tested by the states or federal health officials have been identified as vaping products containing THC, the main psychotropic ingredient in marijuana. Some of the patients reported a mixture of THC and nicotine; and some reported vaping nicotine alone. No one substance has been identified in all of the samples tested, and it is unclear if the illnesses are related to one single compound. Until more details are known, FDA officials have warned people not to use any vaping products bought on the street, and they warn against modifying any products purchased in stores. They are also asking people and health professionals to report any adverse effects. The CDC has posted an information page for consumers.

Mental Effects

Long-term marijuana use has been linked to mental illness in some people, such as:

• temporary hallucinations
• temporary paranoia
• worsening symptoms in patients with schizophrenia —a severe mental disorder with symptoms such as hallucinations, paranoia, and disorganized thinking

Marijuana use has also been linked to other mental health problems, such as depression, anxiety, and suicidal thoughts among teens. However, study findings have been mixed.

Are there effects of inhaling secondhand marijuana smoke?

Failing a drug test.

While it's possible to fail a drug test after inhaling secondhand marijuana smoke, it's unlikely. Studies show that very little THC is released in the air when a person exhales. Research findings suggest that, unless people are in an enclosed room, breathing in lots of smoke for hours at close range, they aren't likely to fail a drug test. 15,16 Even if some THC was found in the blood, it wouldn't be enough to fail a test.

Getting High from Passive Exposure?

Similarly, it's unlikely that secondhand marijuana smoke would give nonsmoking people in a confined space a high from passive exposure. Studies have shown that people who don't use marijuana report only mild effects of the drug from a nearby smoker, under extreme conditions (breathing in lots of marijuana smoke for hours in an enclosed room). 17

Other Health Effects?

More research is needed to know if secondhand marijuana smoke has similar health risks as secondhand tobacco smoke. A recent study on rats suggests that secondhand marijuana smoke can do as much damage to the heart and blood vessels as secondhand tobacco smoke. 20 But researchers haven't fully explored the effect of secondhand marijuana smoke on humans. What they do know is that the toxins and tar found in marijuana smoke could affect vulnerable people, such as children or people with asthma.

How Does Marijuana Affect a Person's Life?

Compared to those who don't use marijuana, those who frequently use large amounts report the following:

• lower life satisfaction
• poorer mental health
• poorer physical health
• more relationship problems

People also report less academic and career success. For example, marijuana use is linked to a higher likelihood of dropping out of school. 18 It's also linked to more job absences, accidents, and injuries. 19

Is marijuana a gateway drug?

Use of alcohol, tobacco, and marijuana are likely to come before use of other drugs. 21,22 Animal studies have shown that early exposure to addictive substances, including THC, may change how the brain responds to other drugs. For example, when rodents are repeatedly exposed to THC when they're young, they later show an enhanced response to other addictive substances—such as morphine or nicotine—in the areas of the brain that control reward, and they're more likely to show addiction-like behaviors. 23,24

Although these findings support the idea of marijuana as a "gateway drug," the majority of people who use marijuana don't go on to use other "harder" drugs. It's also important to note that other factors besides biological mechanisms, such as a person’s social environment, are also critical in a person’s risk for drug use and addiction. Read more about marijuana as a gateway drug in our Marijuana Research Report .

Can a person overdose on marijuana?

An overdose occurs when a person uses enough of the drug to produce life-threatening symptoms or death. There are no reports of teens or adults dying from marijuana alone. However, some people who use marijuana can feel some very uncomfortable side effects, especially when using marijuana products with high THC levels. People have reported symptoms such as anxiety and paranoia, and in rare cases, an extreme psychotic reaction (which can include delusions and hallucinations) that can lead them to seek treatment in an emergency room.

While a psychotic reaction can occur following any method of use, emergency room responders have seen an increasing number of cases involving marijuana edibles. Some people (especially preteens and teens) who know very little about edibles don't realize that it takes longer for the body to feel marijuana’s effects when eaten rather than smoked. So they consume more of the edible, trying to get high faster or thinking they haven't taken enough. In addition, some babies and toddlers have been seriously ill after ingesting marijuana or marijuana edibles left around the house.

Is marijuana addictive?

Marijuana use can lead to the development of a substance use disorder, a medical illness in which the person is unable to stop using even though it's causing health and social problems in their life. Severe substance use disorders are also known as addiction. Research suggests that between 9 and 30 percent of those who use marijuana may develop some degree of marijuana use disorder. 25 People who begin using marijuana before age 18 are four to seven times more likely than adults to develop a marijuana use disorder. 26

Many people who use marijuana long term and are trying to quit report mild withdrawal symptoms that make quitting difficult. These include:

• grouchiness
• sleeplessness
• decreased appetite

What treatments are available for marijuana use disorder?

No medications are currently available to treat marijuana use disorder, but behavioral support has been shown to be effective. Examples include therapy and motivational incentives (providing rewards to patients who remain drug-free). Continuing research may lead to new medications that help ease withdrawal symptoms, block the effects of marijuana, and prevent relapse.

Points to Remember

• Marijuana refers to the dried leaves, flowers, stems, and seeds from the Cannabis sativa or Cannabis indica plant .
• The plant contains the mind-altering chemical THC and other related compounds.
• People use marijuana by smoking, eating, drinking, or inhaling it.
• Smoking and vaping THC-rich extracts from the marijuana plant (a practice called dabbing ) is on the rise.
• altered senses
• impaired memory and learning
• hallucinations and paranoia
• breathing problems
• possible harm to a fetus's brain in pregnant women
• The amount of THC in marijuana has been increasing steadily in recent decades, creating more harmful effects in some people.
• It's unlikely that a person will fail a drug test or get high from passive exposure by inhaling secondhand marijuana smoke.
• There aren’t any reports of teens and adults dying from using marijuana alone, but marijuana use can cause some very uncomfortable side effects, such as anxiety and paranoia and, in rare cases, extreme psychotic reactions.
• Marijuana use can lead to a substance use disorder, which can develop into an addiction in severe cases.
• No medications are currently available to treat marijuana use disorder, but behavioral support can be effective.

For more information about marijuana and marijuana use, visit our:

• Marijuana webpage
• Drugged Driving DrugFacts
• Substance Abuse Center for Behavioral Health Statistics and Quality. Results from the 2018 National Survey on Drug Use and Health: Detailed Tables. SAMHSA. https://www.samhsa.gov/data/report/2018-nsduh-detailed-tables . Accessed December 2019.
• Miech, R. A., Johnston, L. D., Patrick, M. E., O’Malley, P. M., Bachman, J. G., & Schulenberg J. E. (2023). Monitoring the Future National Survey Results on Drug Use, 1975-2022 . Monitoring the Future Monograph Series. Ann Arbor: Institute for Social Research, The University of Michigan.
• Bell C, Slim J, Flaten HK, Lindberg G, Arek W, Monte AA. Butane Hash Oil Burns Associated with Marijuana Liberalization in Colorado. J Med Toxicol Off J Am Coll Med Toxicol. 2015;11(4):422-425. doi:10.1007/s13181-015-0501-0.
• Romanowski KS, Barsun A, Kwan P, et al. Butane Hash Oil Burns: A 7-Year Perspective on a Growing Problem. J Burn Care Res Off Publ Am Burn Assoc. 2017;38(1):e165-e171. doi:10.1097/BCR.0000000000000334.
• Meier MH, Caspi A, Ambler A, et al. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc Natl Acad Sci U S A. 2012;109(40):E2657-E2664. doi:10.1073/pnas.1206820109.
• Jackson NJ, Isen JD, Khoddam R, et al. Impact of adolescent marijuana use on intelligence: Results from two longitudinal twin studies. Proc Natl Acad Sci U S A. 2016;113(5):E500-E508. doi:10.1073/pnas.1516648113.
• Mehmedic Z, Chandra S, Slade D, et al. Potency trends of Δ9-THC and other cannabinoids in confiscated cannabis preparations from 1993 to 2008. J Forensic Sci. 2010;55(5):1209-1217. doi:10.1111/j.1556-4029.2010.01441.x.
• National Academies of Sciences, Engineering, and Medicine. The Health Effects of Cannabis and Cannabinoids: Current State of Evidence and Recommendations for Research. Washington, DC: The National Academies Press; 2017.
• Young-Wolff KC, Tucker L-Y, Alexeeff S, et al. Trends in Self-reported and Biochemically Tested Marijuana Use Among Pregnant Females in California From 2009-2016. JAMA. 2017;318(24):2490. doi:10.1001/jama.2017.17225
• The National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Population Health and Public Health Practice, Committee on the Health Effects of Marijuana: An Evidence Review and Research Agenda. The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research. http://nationalacademies.org/hmd/Reports/2017/health-effects-of-cannabis-and-cannabinoids.aspx . Accessed January 19, 2017.
• Goldschmidt L, Day NL, Richardson GA. Effects of prenatal marijuana exposure on child behavior problems at age 10. Neurotoxicol Teratol. 2000;22(3):325-336.
• Richardson GA, Ryan C, Willford J, Day NL, Goldschmidt L. Prenatal alcohol and marijuana exposure: effects on neuropsychological outcomes at 10 years. Neurotoxicol Teratol. 2002;24(3):309-320.
• Perez-Reyes M, Wall ME. Presence of delta9-tetrahydrocannabinol in human milk. N Engl J Med. 1982;307(13):819-820. doi:10.1056/NEJM198209233071311.
• Galli JA, Sawaya RA, Friedenberg FK. Cannabinoid Hyperemesis Syndrome. Curr Drug Abuse Rev . 2011;4(4):241-249.
• Röhrich J, Schimmel I, Zörntlein S, et al. Concentrations of delta9-tetrahydrocannabinol and 11-nor-9-carboxytetrahydrocannabinol in blood and urine after passive exposure to Cannabis smoke in a coffee shop. J Anal Toxicol. 2010;34(4):196-203.
• Cone EJ, Bigelow GE, Herrmann ES, et al. Non-smoker exposure to secondhand cannabis smoke. I. Urine screening and confirmation results. J Anal Toxicol. 2015;39(1):1-12. doi:10.1093/jat/bku116.
• Herrmann ES, Cone EJ, Mitchell JM, et al. Non-smoker exposure to secondhand cannabis smoke II: Effect of room ventilation on the physiological, subjective, and behavioral/cognitive effects. Drug Alcohol Depend. 2015;151:194-202. doi:10.1016/j.drugalcdep.2015.03.019.
• McCaffrey DF, Pacula RL, Han B, Ellickson P. Marijuana Use and High School Dropout: The Influence of Unobservables. Health Econ. 2010;19(11):1281-1299. doi:10.1002/hec.1561.
• Zwerling C, Ryan J, Orav EJ. The efficacy of preemployment drug screening for marijuana and cocaine in predicting employment outcome. JAMA. 1990;264(20):2639-2643.
• Wang X, Derakhshandeh R, Liu J, et al. One Minute of Marijuana Secondhand Smoke Exposure Substantially Impairs Vascular Endothelial Function. J Am Heart Assoc. 2016;5(8). doi:10.1161/JAHA.116.003858.
• Secades-Villa R, Garcia-Rodríguez O, Jin CJ, Wang S, Blanco C. Probability and predictors of the cannabis gateway effect: a national study. Int J Drug Policy. 2015;26(2):135-142. doi:10.1016/j.drugpo.2014.07.011.
• Levine A, Huang Y, Drisaldi B, et al. Molecular mechanism for a gateway drug: epigenetic changes initiated by nicotine prime gene expression by cocaine. Sci Transl Med. 2011;3(107):107ra109. doi:10.1126/scitranslmed.3003062.
• Panlilio LV, Zanettini C, Barnes C, Solinas M, Goldberg SR. Prior exposure to THC increases the addictive effects of nicotine in rats. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2013;38(7):1198-1208. doi:10.1038/npp.2013.16.
• Cadoni C, Pisanu A, Solinas M, Acquas E, Di Chiara G. Behavioural sensitization after repeated exposure to Delta 9-tetrahydrocannabinol and cross-sensitization with morphine. Psychopharmacology (Berl). 2001;158(3):259-266. doi:10.1007/s002130100875.
• Hasin DS, Saha TD, Kerridge BT, et al. Prevalence of Marijuana Use Disorders in the United States Between 2001-2002 and 2012-2013. JAMA Psychiatry. 2015;72(12):1235-1242. doi:10.1001/jamapsychiatry.2015.1858.
• Winters KC, Lee C-YS. Likelihood of developing an alcohol and cannabis use disorder during youth: association with recent use and age. Drug Alcohol Depend. 2008;92(1-3):239-247. doi:10.1016/j.drugalcdep.2007.08.005.
• Corsi DJ, Walsh L, Weiss D, et al. Association Between Self-reported Prenatal Cannabis Use and Maternal, Perinatal, and Neonatal Outcomes. JAMA . Published online June 18, 2019322(2):145–152. doi:10.1001/jama.2019.8734

This publication is available for your use and may be reproduced in its entirety without permission from NIDA. Citation of the source is appreciated, using the following language: Source: National Institute on Drug Abuse; National Institutes of Health; U.S. Department of Health and Human Services.

This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 26 (2024)

Digital Interventions for Recreational Cannabis Use Among Young Adults: Systematic Review, Meta-Analysis, and Behavior Change Technique Analysis of Randomized Controlled Studies

Authors of this article:

• José Côté 1, 2, 3 , RN, PhD   ; 
• Gabrielle Chicoine 3, 4 , RN, PhD   ; 
• Billy Vinette 1, 3 , RN, MSN   ; 
• Patricia Auger 2, 3 , MSc   ; 
• Geneviève Rouleau 3, 5, 6 , RN, PhD   ; 
• Guillaume Fontaine 7, 8, 9 , RN, PhD   ; 
• Didier Jutras-Aswad 2, 10 , MSc, MD  

1 Faculty of Nursing, Université de Montréal, Montreal, QC, Canada

2 Research Centre of the Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada

3 Research Chair in Innovative Nursing Practices, Montreal, QC, Canada

4 Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

5 Department of Nursing, Université du Québec en Outaouais, Saint-Jérôme, QC, Canada

6 Women's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada

7 Ingram School of Nursing, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada

8 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Sir Mortimer B. Davis Jewish General Hospital, Montreal, QC, Canada

9 Kirby Institute, University of New South Wales, Sydney, Australia

10 Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

Corresponding Author:

José Côté, RN, PhD

Research Centre of the Centre Hospitalier de l’Université de Montréal

850 Saint-Denis

Montreal, QC, H2X 0A9

Phone: 1 514 890 8000

Email: [email protected]

Background: The high prevalence of cannabis use among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks. Digital modalities, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based interventions for young adults for cannabis use. However, existing reviews do not consider young adults specifically, combine cannabis-related outcomes with those of many other substances in their meta-analytical results, and do not solely target interventions for cannabis use.

Objective: We aimed to evaluate the effectiveness and active ingredients of digital interventions designed specifically for cannabis use among young adults living in the community.

Methods: We conducted a systematic search of 7 databases for empirical studies published between database inception and February 13, 2023, assessing the following outcomes: cannabis use (frequency, quantity, or both) and cannabis-related negative consequences. The reference lists of included studies were consulted, and forward citation searching was also conducted. We included randomized studies assessing web- or mobile-based interventions that included a comparator or control group. Studies were excluded if they targeted other substance use (eg, alcohol), did not report cannabis use separately as an outcome, did not include young adults (aged 16-35 y), had unpublished data, were delivered via teleconference through mobile phones and computers or in a hospital-based setting, or involved people with mental health disorders or substance use disorders or dependence. Data were independently extracted by 2 reviewers using a pilot-tested extraction form. Authors were contacted to clarify study details and obtain additional data. The characteristics of the included studies, study participants, digital interventions, and their comparators were summarized. Meta-analysis results were combined using a random-effects model and pooled as standardized mean differences.

Results: Of 6606 unique records, 19 (0.29%) were included (n=6710 participants). Half (9/19, 47%) of these articles reported an intervention effect on cannabis use frequency. The digital interventions included in the review were mostly web-based. A total of 184 behavior change techniques were identified across the interventions (range 5-19), and feedback on behavior was the most frequently used (17/19, 89%). Digital interventions for young adults reduced cannabis use frequency at the 3-month follow-up compared to control conditions (including passive and active controls) by −6.79 days of use in the previous month (95% CI −9.59 to −4.00; P <.001).

Conclusions: Our results indicate the potential of digital interventions to reduce cannabis use in young adults but raise important questions about what optimal exposure dose could be more effective, both in terms of intervention duration and frequency. Further high-quality research is still needed to investigate the effects of digital interventions on cannabis use among young adults.

Trial Registration: PROSPERO CRD42020196959; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=196959

Introduction

Cannabis use among young adults is recognized as a public health concern.

Young adulthood (typically the ages of 18-30 y) is a critical developmental stage characterized by a peak prevalence of substance use [ 1 , 2 ]. Worldwide, cannabis is a substance frequently used for nonmedical purposes due in part to its high availability in some regions and enhanced product variety and potency [ 3 , 4 ]. The prevalence of cannabis use (CU) among young adults is high [ 5 , 6 ], and its rates have risen in recent decades [ 7 ]. In North America and Oceania, the estimated past-year prevalence of CU is ≥25% among young adults [ 8 , 9 ].

While the vast majority of cannabis users do not experience severe problems from their use [ 4 ], the high prevalence of CU among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks [ 10 , 11 ]. These include impairment of cognitive function, memory, and psychomotor skills during acute intoxication; increased engagement in behaviors with a potential for injury and fatality (eg, driving under the influence); socioeconomic problems; and diminished social functioning [ 4 , 12 - 14 ]. Importantly, an extensive body of literature reveals that subgroups engaging in higher-risk use, such as intensive or repeated use, are more prone to severe and chronic consequences, including physical ailments (eg, respiratory illness and reproductive dysfunction), mental health disorders (eg, psychosis, depression, and suicidal ideation or attempts), and the potential development of CU disorder [ 4 , 15 - 17 ].

Interventions to Reduce Public Health Impact of Young Adult CU

Given the increased prevalence of lifetime and daily CU among young adults and the potential negative impact of higher-risk CU, various prevention and intervention programs have been implemented to help users reduce or cease their CU. These programs primarily target young adults regardless of their CU status [ 2 , 18 ]. In this context, many health care organizations and international expert panels have developed evidence-based lower-risk CU guidelines to promote safer CU and intervention options to help reduce risks of adverse health outcomes from nonmedical CU [ 4 , 16 , 17 , 19 ]. Lower-risk guidance-oriented interventions for CU are based on concepts of health promotion [ 20 - 22 ] and health behavior change [ 23 - 26 ] and on other similar harm reduction interventions implemented in other areas of population health (eg, lower-risk drinking guidelines, supervised consumption sites and services, and sexual health) [ 27 , 28 ]. These interventions primarily aim to raise awareness of negative mental, physical, and social cannabis-related consequences to modify individual-level behavior-related risk factors.

Meta-analyses have shown that face-to-face prevention and treatment interventions are generally effective in reducing CU in young adults [ 18 , 29 - 32 ]. However, as the proportion of professional help seeking for CU concerns among young adults remains low (approximately 15%) [ 33 , 34 ], alternative strategies that consider the limited capacities and access-related barriers of traditional face-to-face prevention and treatment facilities are needed. Digital interventions, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based programs for young adult cannabis users. These interventions address barriers such as long-distance travel, concerns about confidentiality, stigma associated with seeking treatment, and the cost of traditional treatments [ 35 - 37 ]. By overcoming these barriers, digital interventions have the potential to have a stronger public health impact [ 18 , 38 ].

State of Knowledge of Digital Interventions for CU and Young Adults

The literature regarding digital interventions for substance use has grown rapidly in the past decade, as evidenced by several systematic reviews and meta-analyses of randomized controlled trial (RCT) studies on the efficacy or effectiveness of these interventions in preventing or reducing harmful substance use [ 2 , 39 - 41 ]. However, these reviews do not focus on young adults specifically. In addition, they combine CU-related outcomes with those of many other substances in their meta-analytical results. Finally, they do not target CU interventions exclusively.

In total, 4 systematic reviews and meta-analyses of digital interventions for CU among young people have reported mixed results [ 42 - 45 ]. In their systematic review (10 studies of 5 prevention and 5 treatment interventions up to 2012), Tait et al [ 44 ] concluded that digital interventions effectively reduced CU among adolescents and adults at the posttreatment time point. Olmos et al [ 43 ] reached a similar conclusion in their meta-analysis of 9 RCT studies (2 prevention and 7 treatment interventions). In their review, Hoch et al [ 42 ] reported evidence of small effects at the 3-month follow-up based on 4 RCTs of brief motivational interventions and cognitive behavioral therapy (CBT) delivered on the web. In another systematic review and meta-analysis, Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce consumption. However, these authors indicated that the programs tested varied significantly across the studies considered and that statistical heterogeneity was attributable to the inclusion of studies of programs targeting more than one substance (eg, alcohol and cannabis) and both adolescents and young adults. Beneria et al [ 45 ] recommend that future work “establish the effectiveness of the newer generation of interventions as well as the key ingredients” of effective digital interventions addressing CU by young people. This is of particular importance because behavior change interventions tend to be complex as they consist of multiple interactive components [ 46 ].

Behavior change interventions refer to “coordinated sets of activities designed to change specified behavior patterns” [ 47 ]. Their interacting active ingredients can be conceptualized as behavior change techniques (BCTs) [ 48 ]. BCTs are specific and irreducible. Each BCT has its own individual label and definition, which can be used when designing and reporting complex interventions and as a nomenclature system when coding interventions for their content [ 47 ]. The Behavior Change Technique Taxonomy version 1 (BCTTv1) [ 48 , 49 ] was developed to provide a shared, standardized terminology for characterizing complex behavior change interventions and their active ingredients. Several systematic reviews with meta-regressions that used the BCTTv1 have found interventions with certain BCTs to be more effective than those without [ 50 - 53 ]. A better understanding of the BCTs used in digital interventions for young adult cannabis users would help not only to establish the key ingredients of such interventions but also develop and evaluate effective interventions.

In the absence of any systematic review of the effectiveness and active ingredients of digital interventions designed specifically for CU among community-living young adults, we set out to achieve the following:

• conduct a comprehensive review of digital interventions for preventing, reducing, or ceasing CU among community-living young adults,
• describe the active ingredients (ie, BCTs) in these interventions from the perspective of behavior change science, and
• analyze the effectiveness of these interventions on CU outcomes.

Protocol Registration

We followed the Cochrane Handbook for Systematic Reviews of Interventions [ 54 ] in designing this systematic review and meta-analysis and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines in reporting our findings (see Multimedia Appendix 1 [ 55 ] for the complete PRISMA checklist). This review was registered in PROSPERO (CRD42020196959).

Search Strategy

The search strategy was designed by a health information specialist together with the research team and peer reviewed by another senior information specialist before execution using Peer Review of Electronic Search Strategies for systematic reviews [ 56 ]. The search strategy revolved around three concepts:

• CU (eg, “cannabis,” “marijuana,” and “hashish”)
• Digital interventions (eg, “telehealth,” “website,” “mobile applications,” and “computer”)
• Young adults (eg, “emerging adults” and “students”)

The strategy was initially implemented on March 18, 2020, and again on October 13, 2021, and February 13, 2023. The full, detailed search strategies for each database are presented in Multimedia Appendix 2 .

Information Sources

We searched 7 electronic databases of published literature: CINAHL Complete, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Embase, MEDLINE, PubMed, and PsycINFO. No publication date filters or language restrictions were applied. A combination of free-text keywords and Medical Subject Headings was tailored to the conventions of each database for optimal electronic searching. The research team also manually screened the reference lists of the included articles and the bibliographies of existing systematic reviews [ 18 , 31 , 42 - 45 ] to identify additional relevant studies (snowballing). Finally, a forward citation tracking procedure (ie, searching for articles that cited the included studies) was carried out in Google Scholar.

Inclusion Criteria

The population, intervention, comparison, outcome, and study design process is presented in Multimedia Appendix 3 . The inclusion criteria were as follows: (1) original research articles published in peer-reviewed journals; (2) use of an experimental study design (eg, RCT, cluster RCT, or pilot RCT); (3) studies evaluating the effectiveness (or efficacy) of digital interventions designed specifically to prevent, reduce, or cease CU as well as promote CU self-management or address cannabis-related harm and having CU as an outcome measure; (4) studies targeting young adults, including active and nonactive cannabis users; (5) cannabis users and nonusers not under substance use treatment used as controls in comparator, waitlist, or delayed-treatment groups offered another type of intervention (eg, pharmacotherapy or psychosocial) different from the one being investigated or participants assessed only for CU; and (6) quantitative CU outcomes (frequency and quantity) or cannabis abstinence. Given the availability of numerous CU screening and assessment tools with adequate psychometric properties and the absence of a gold standard in this regard [ 57 ], any instrument capturing aspects of CU was considered. CU outcome measures could be subjective (eg, self-reported number of CU days or joints in the previous 3 months) or objective (eg, drug screening test). CU had to be measured before the intervention (baseline) and at least once after.

Digital CU interventions were defined as web- or mobile-based interventions that included one or more activities (eg, self-directed or interactive psychoeducation or therapy, personalized feedback, peer-to-peer contact, and patient-to-expert communication) aimed at changing CU [ 58 ]. Mobile-based interventions were defined as interventions delivered via mobile phone through SMS text message, multimedia messaging service (ie, SMS text messages that include　multimedia content, such as pictures, videos, or emojis), or mobile apps, whereas web-based interventions (eg, websites and digital platforms) were defined as interventions designed to be accessed on the web (ie, the internet), mainly via computers. Interventions could include self-directed and web-based interventions with human support. We defined young adults as aged 16 to 35 years and included students and nonstudents. While young adulthood is typically defined as covering the ages of 18 to 30 years [ 59 ], we broadened the range given that the age of majority and legal age to purchase cannabis differs across countries and jurisdictions. This was also in line with the age range targeted by several digital CU interventions (college or university students or emerging adults aged 15-24 years) [ 31 , 45 ]. Given the language expertise of the research team members and the available resources, only English- and French-language articles were retained.

Exclusion Criteria

Knowledge synthesis articles, study protocols, and discussion papers or editorials were excluded, as were articles with cross-sectional, cohort, case study or report, pretest-posttest, quasi-experimental, or qualitative designs. Mixed methods designs were included only if the quantitative component was an RCT. We excluded studies if (1) use of substances other than cannabis (eg, alcohol, opioids, or stimulants) was the focus of the digital intervention (though studies that included polysubstance users were retained if CU was assessed and reported separately); (2) CU was not reported separately as an outcome or only attitudes or beliefs regarding, knowledge of, intention to reduce, or readiness or motivation to change CU was measured; and (3) the data reported were unpublished (eg, conferences and dissertations). Studies of traditional face-to-face therapy delivered via teleconference on mobile phones and computers or in a hospital-based setting and informational campaigns (eg, web-based poster presentations or pamphlets) were excluded as well. Studies with samples with a maximum age of <15 years and a minimum age of >35 years were also excluded. Finally, we excluded studies that focused exclusively on people with a mental health disorder or substance use disorder or dependence or on adolescents owing to the particular health care needs of these populations, which may differ from those of young adults [ 1 ].

Data Collection

Selection of studies.

Duplicates were removed from the literature search results in EndNote (version X9.3.3; Clarivate Analytics) using the Bramer method for deduplication of database search results for systematic reviews [ 60 ]. The remaining records were uploaded to Covidence (Veritas Health Innovation), a web-based systematic review management system. A reviewer guide was developed that included screening questions and a detailed description of each inclusion and exclusion criterion based on PICO (population, intervention, comparator, and outcome), and a calibration exercise was performed before each stage of the selection process to maximize consistency between reviewers. Titles and abstracts of studies flagged for possible inclusion were screened first by 2 independent reviewers (GC, BV, PA, and GR; 2 per article) against the eligibility criteria (stage 1). Articles deemed eligible for full-text review were then retrieved and screened for inclusion (stage 2). Full texts were assessed in detail against the eligibility criteria again by 2 reviewers independently. Disagreements between reviewers were resolved through consensus or by consulting a third reviewer.

Data Extraction Process

In total, 2 reviewers (GC, BV, PA, GR, and GF; 2 per article) independently extracted relevant data (or informal evidence) using a data extraction form developed specifically for this review and integrated into Covidence. The form was pilot-tested on 2 randomly selected studies and refined accordingly. Data pertaining to the following domains were extracted from the included studies: (1) Study characteristics included information on the first and corresponding authors, publication year, country of origin, aims and hypotheses, study period, design (including details on randomization and blinding), follow-up times, data collection methods, and types of statistical analysis. (2) Participant characteristics included study target population, participant inclusion and exclusion criteria, sex or gender, mean age, and sample sizes at each data collection time point. (3) Intervention characteristics, for which the research team developed a matrix inspired by the template for intervention description and replication 12-item checklist [ 61 ] to extract informal evidence (ie, intervention descriptions) from the included studies under the headings name of intervention, purpose, underpinning theory of design elements, treatment approach, type of technology (ie, web or mobile) and software used, delivery format (ie, self-directed, human involvement, or both), provider characteristics (if applicable), intervention duration (ie, length of treatment and number of sessions or modules), material and procedures (ie, tools or activities offered, resources provided, and psychoeducational content), tailoring, and unplanned modifications. (4) Comparator characteristics were details of the control or comparison group or groups, including nature (passive vs active), number of groups or clusters (if applicable), type and length of the intervention (if applicable), and number of participants at each data collection time point. (5) Outcome variables, including the primary outcome variable examined in this systematic review, that is, the mean difference in CU frequency before and after the intervention and between the experimental and control or comparison groups. When possible, we examined continuous variables, including CU frequency means and SDs at the baseline and follow-up time points, and standardized regression coefficients (ie, β coefficients and associated 95% CIs). The secondary outcomes examined included other CU outcome variables (eg, quantity of cannabis used and abstinence) and cannabis-related negative consequences (or problems). Details on outcome variables (ie, definition, data time points, and missing data) and measurements (ie, instruments, measurement units, and scales) were also extracted.

In addition, data on user engagement and use of the digital intervention and study attrition rates (ie, dropouts and loss to follow-up) were extracted. When articles had missing data, we contacted the corresponding authors via email (2 attempts were made over a 2-month period) to obtain missing information. Disagreements over the extracted data were limited and resolved through discussion.

Data Synthesis Methods

Descriptive synthesis.

The characteristics of the included studies, study participants, interventions, and comparators were summarized in narrative and table formats. The template for intervention description and replication 12-item checklist [ 61 ] was used to summarize and organize intervention characteristics and assess to what extent the interventions were appropriately described in the included articles. As not all studies had usable data for meta-analysis purposes and because of heterogeneity, we summarized the main findings (ie, intervention effects) of the included studies in narrative and table formats for each outcome of interest in this review.

The BCTs used in the digital interventions were identified from the descriptions of the interventions (ie, experimental groups) provided in the articles as well as any supplementary material and previously published research protocols. A BCT was defined as “an observable, replicable, and irreducible component of an intervention designed to alter or redirect causal processes that regulate behavior” [ 48 ]. The target behavior in this review was the cessation or reduction of CU by young adults. BCTs were identified and coded using the BCTTv1 [ 48 , 49 ], a taxonomy of 93 BCTs organized into 16 hierarchical thematic clusters or categories. Applying the BCTTv1 in a systematic review allows for the comparison and synthesis of evidence across studies in a structured manner. This analysis allows for the identification of the explicit mechanisms underlying the reported behavior change induced by interventions, successful or not, and, thus, avoids making implicit assumptions about what works [ 62 ].

BCT coding was performed by 2 reviewers independently—BV coded all studies, and GC and GF coded a subset of the studies. All reviewers completed web-based training on the BCTTv1, and GF is an experienced implementation scientist who had used the BCTTv1 in prior work [ 63 - 65 ]. The descriptions of the interventions in the articles were read line by line and analyzed for the clear presence of BCTs using the guidelines developed by Michie et al [ 48 ]. For each article, the BCTs identified were documented and categorized using supporting textual evidence. They were coded only once per article regardless of how many times they came up in the text. Disagreements about including a BCT were resolved through discussion. If there was uncertainty about whether a BCT was present, it was coded as absent. Excel (Microsoft Corp) was used to compare the reviewers’ independent BCT coding and generate an overall descriptive synthesis of the BCTs identified. The BCTs were summarized by study and BCT cluster.

Statistical Analysis

Meta-analyses were conducted to estimate the size of the effect of the digital interventions for young adult CU on outcomes of interest at the posttreatment and follow-up assessments compared with control or alternative intervention conditions. The outcome variables considered were (1) CU frequency and other CU outcome variables (eg, quantity of cannabis used and abstinence) at baseline and the posttreatment time point or follow-up measured using standardized instruments of self-reported CU (eg, the timeline followback [TLFB] method) [ 66 ] and (2) cannabis-related negative consequences measured using standardized instruments (eg, the Marijuana Problems Scale) [ 67 ].

Under our systematic review protocol, ≥2 studies were needed for a meta-analysis. On the basis of previous systematic reviews and meta-analyses in the field of digital CU interventions [ 31 , 42 - 45 ], we expected between-study heterogeneity regarding outcome assessment. To minimize heterogeneity, we chose to pool studies with similar outcomes of interest based on four criteria: (1) definition of outcome (eg, CU frequency, quantity consumed, and abstinence), (2) type of outcome variable (eg, days of CU in the previous 90 days, days high per week in the previous 30 days, and number of CU events in the previous month) and measure (ie, instruments or scales), (3) use of validated instruments, and (4) posttreatment or follow-up time points (eg, 2 weeks or 1 month after the baseline or 3, 6, and 12 months after the baseline).

Only articles that reported sufficient statistics to compute a valid effect size with 95% CIs were included in the meta-analyses. In the case of articles that were not independent (ie, more than one published article reporting data from the same clinical trial), only 1 was included, and it was represented only once in the meta-analysis for a given outcome variable regardless of whether the data used to compute the effect size were extracted from the original paper or a secondary analysis paper. We made sure that the independence of the studies included in the meta-analysis of each outcome was respected. In the case of studies that had more than one comparator, we used the effect size for each comparison between the intervention and control groups.

Meta-analyses were conducted only for mean differences based on the change from baseline in CU frequency at 3 months after the baseline as measured using the number of self-reported days of use in the previous month. As the true value of the estimated effect size for outcome variables might vary across different trials and samples, we used a random-effects model given that the studies retained did not have identical target populations. The random-effects model incorporates between-study variation in the study weights and estimated effect size [ 68 ]. In addition, statistical heterogeneity across studies was assessed using I 2 , which measures the proportion of heterogeneity to the total observed dispersion; 25% was considered low, 50% was considered moderate, and 75% was considered high [ 69 ]. Because only 3 studies were included in the meta-analysis [ 70 - 72 ], publication bias could not be assessed. All analyses were completed using Stata (version 18; StataCorp) [ 73 ].

Risk-of-Bias Assessment

The risk of bias (RoB) of the included RCTs was assessed using the Cochrane RoB 2 tool at the outcome level [ 74 ]. Each distinct risk domain (ie, randomization process, deviations from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported results) was assessed as “low,” “some concerns,” or “high” based on the RoB 2 criteria. In total, 2 reviewers (GC and BV) conducted the assessments independently. Disagreements were discussed, and if not resolved consensually by the 2, the matter was left for a third reviewer (GF) to settle. The assessments were summarized by risk domain and outcome and converted into figures using the RoB visualization tool robvis [ 75 ].

Search Results

The database search generated a total of 13,232 citations, of which 7822 (59.11%) were from the initial search on March 18, 2020, and 2805 (21.2%) and 2605 (19.69%) were from the updates on October 13, 2021, and February 13, 2023, respectively. Figure 1 presents the PRISMA study flow diagram [ 76 ]. Of the 6606 unique records, 6484 (98.15%) were excluded based on title and abstract screening. Full texts of the remaining 1.85% (122/6606) of the records were examined, as were those of 25 more reports found through hand searching. Of these 147 records, 128 (87.1%) were excluded after 3 rounds of full-text screening. Of these 128 records, 39 (30.5%) were excluded for not being empirical research articles (eg, research protocols). Another 28.1% (36/128) were excluded for not meeting our definition of digital CU intervention. The remaining records were excluded for reasons that occurred with a frequency of ≤14%, including young adults not being the target population and the study not meeting our study design criteria (ie, RCT, cluster RCT, or pilot RCT). Excluded studies and reasons for exclusion are listed in Multimedia Appendix 4 . Finally, 19 articles detailing the results of 19 original studies were included.

Description of Studies

Study characteristics.

Multimedia Appendix 5 [ 70 - 72 , 77 - 92 ] describes the general characteristics of the 19 included studies. The studies were published between 2010 and 2023, with 58% (11/19) published in 2018 or later. A total of 53% (10/19) of the studies were conducted in the United States [ 77 - 86 ], 11% (2/19) were conducted in Canada [ 87 , 88 ], 11% (2/19) were conducted in Australia [ 71 , 89 ], 11% (2/19) were conducted in Germany [ 72 , 90 ], 11% (2/19) were conducted in Switzerland [ 70 , 91 ], and 5% (1/19) were conducted in Sweden [ 92 ]. A total of 79% (15/19) were RCTs [ 70 - 72 , 77 , 79 , 81 - 83 , 86 - 92 ], and 21% (4/19) were pilot RCTs [ 78 , 80 , 84 , 85 ].

Participant Characteristics

The studies enrolled a total of 6710 participants—3229 (48.1%) in the experimental groups, 3358 (50%) in the control groups, and the remaining 123 (1.8%) from 1 study [ 82 ] where participant allocation to the intervention condition was not reported. Baseline sample sizes ranged from 49 [ 81 ] to 1292 [ 72 ] (mean 352.89, SD 289.50), as shown in Multimedia Appendix 5 . Participant mean ages ranged from 18.03 (SD 0.31) [ 79 ] to 35.3 (SD 12.6) years [ 88 ], and the proportion of participants who identified as female ranged from 24.7% [ 91 ] to 84.1% [ 80 ].

Of the 19 included studies, 10 (53%) targeted adults aged ≥18 years, of which 7 (70%) studies focused on adults who had engaged in past-month CU [ 70 , 71 , 80 , 84 , 85 , 90 , 91 ], 2 (20%) studies included adults who wished to reduce or cease CU [ 72 , 89 ], and 1 (10%) study focused on noncollege adults with a moderate risk associated with CU [ 88 ]. Sinadinovic et al [ 92 ] targeted young adults aged ≥16 years who had used cannabis at least once a week in the previous 6 months. The remaining 8 studies targeted college or university students (aged ≥17 y) specifically, of which 7 (88%) studies focused solely on students who reported using cannabis [ 78 , 79 , 81 - 83 , 86 , 87 ] and 1 (12%) study focused solely on students who did not report past-month CU (ie, abstainers) [ 77 ].

Intervention Characteristics

The 19 included studies assessed nine different digital interventions: (1) 5 (26%) evaluated Marijuana eCHECKUP TO GO (e-TOKE), a commercially available electronic intervention used at colleges throughout the United States and Canada [ 77 , 78 , 81 - 83 ]; (2) 2 (11%) examined the internationally known CANreduce program [ 70 , 91 ]; (3) 2 (11%) evaluated the German Quit the Shit program [ 72 , 90 ]; (4) 2 (11%) assessed a social media–delivered, physical activity–focused cannabis intervention [ 84 , 85 ]; (5) 1 (5%) investigated the Swedish Cannabishjälpen intervention [ 92 ]; (6) 1 (5%) evaluated the Australian Grassessment: Evaluate Your Use of Cannabis website program [ 89 ]; (7) 1 (5%) assessed the Canadian Ma réussite, mon choix intervention [ 87 ]; (8) 1 (5%) examined the Australian Reduce Your Use: How to Break the Cannabis Habit program [ 71 ]; and (9) 4 (21%) each evaluated a unique no-name intervention described as a personalized feedback intervention (PFI) [ 79 , 80 , 86 , 88 ]. Detailed information regarding the characteristics of all interventions as reported in each included study is provided in Multimedia Appendix 6 [ 70 - 72 , 77 - 113 ] and summarized in the following paragraphs.

In several studies (8/19, 42%), the interventions were designed to support cannabis users in reducing or ceasing their consumption [ 70 , 72 , 80 , 87 , 89 - 92 ]. In 37% (7/19) of the studies, the interventions aimed at reducing both CU and cannabis-related consequences [ 79 , 81 - 85 , 88 ]. Other interventions focused on helping college students think carefully about the decision to use cannabis [ 77 , 78 ] and on reducing either cannabis-related problems among undergraduate students [ 86 ] or symptoms associated with CU disorder in young adults [ 71 ].

In 26% (5/19) of the studies, theory was used to inform intervention design along with a clear rationale for theory use. Of these 5 articles, only 1 (20%) [ 87 ] reported using a single theory of behavior change, the theory of planned behavior [ 114 ]. A total of 21% (4/19) of the studies selected only constructs of theories (or models) for their intervention design. Of these 4 studies, 2 (50%) evaluated the same intervention [ 72 , 90 ], which focused on principles of self-regulation and self-control theory [ 93 ]; 1 (25%) [ 70 ] used the concept of adherence-focused guidance enhancement based on the supportive accountability model of guidance [ 94 ]; and 1 (25%) [ 71 ] reported that intervention design was guided by the concept of self-behavioral management.

The strategies (or approaches) used in the delivery of the digital interventions were discussed in greater detail in 84% (16/19) of the articles [ 70 - 72 , 79 - 81 , 83 - 92 ]. Many of these articles (9/19, 47%) reported using a combination of approaches based on CBT or motivational interviewing (MI) [ 70 , 71 , 79 , 83 - 85 , 90 - 92 ]. PFIs were also often mentioned as an approach to inform intervention delivery [ 7 , 71 , 79 , 86 - 88 ].

More than half (13/19, 68%) of all the digital interventions were asynchronous and based on a self-guided approach without support from a counselor or therapist. The study by Côté et al [ 87 ] evaluated the efficacy of a web-based tailored intervention focused on reinforcing a positive attitude toward and a sense of control over cannabis abstinence through psychoeducational messages delivered by a credible character in short video clips and personalized reinforcement messages. Lee et al [ 79 ] evaluated a brief, web-based personalized feedback selective intervention based on the PFI approach pioneered by Marlatt et al [ 95 ] for alcohol use prevention and on the MI approach described by Miller and Rollnick [ 96 ]. Similarly, Rooke et al [ 71 ] combined principles of MI and CBT to develop a web-based intervention delivered via web modules, which were informed by previous automated feedback interventions targeting substance use. The study by Copeland et al [ 89 ] assessed the short-term effectiveness of Grassessment: Evaluate Your Use of Cannabis, a brief web-based, self-complete intervention based on motivational enhancement therapy that included personalized feedback messages and psychoeducational material. In the studies by Buckner et al [ 80 ], Cunningham et al [ 88 ], and Walukevich-Dienst et al [ 86 ], experimental groups received a brief web-based PFI available via a computer. A total of 16% (3/19) of the studies [ 77 , 78 , 82 ] applied a program called the Marijuana eCHECKUP TO GO (e-TOKE) for Universities and Colleges, which was presented as a web-based, norm-correcting, brief preventive and intervention education program designed to prompt self-reflection on consequences and consideration of decreasing CU among students. Riggs et al [ 83 ] developed and evaluated an adapted version of e-TOKE that provided participants with university-specific personalized feedback and normative information based on protective behavioral strategies for CU [ 97 ]. Similarly, Goodness and Palfai [ 81 ] tested the efficacy of eCHECKUP TO GO-cannabis, a modified version of e-TOKE combining personalized feedback, norm correction, and a harm and frequency reduction strategy where a “booster” session was provided at 3 months to allow participants to receive repeated exposure to the intervention.

In the remaining 32% (6/19) of the studies, which examined 4 different interventions, the presence of a therapist guide was reported. The intervention evaluated by Sinadinovic et al [ 92 ] combined principles of psychoeducation, MI, and CBT organized into 13 web-based modules and a calendar involving therapist guidance, recommendations, and personal feedback. In total, 33% (2/6) of these studies evaluated a social media–delivered intervention with e-coaches that combined principles of MI and CBT and a harm reduction approach for risky CU [ 84 , 85 ]. Schaub et al [ 91 ] evaluated the efficacy of CANreduce, a web-based self-help intervention based on both MI and CBT approaches, using automated motivational and feedback emails, chat with a counselor, and web-based psychoeducational modules. Similarly, Baumgartner et al [ 70 ] investigated the effectiveness of CANreduce 2.0, a modified version of CANreduce, using semiautomated motivational and adherence-focused guidance-based email feedback with or without a personal online coach. The studies by Tossman et al [ 72 ] and Jonas et al [ 90 ] used a solution-focused approach and MI to evaluate the effectiveness of the German Quit the Shit web-based program that involves weekly feedback provided by counselors.

In addition to using different intervention strategies or approaches, the interventions were diverse in terms of the duration and frequency of the program (eg, web-based activities, sessions, or modules). Of the 12 articles that provided details in this regard, 2 (17%) on the same intervention described it as a brief 20- to 45-minute web-based program [ 77 , 78 ], 2 (17%) on 2 different interventions reported including 1 or 2 modules per week for a duration of 6 weeks [ 71 , 92 ], and 7 (58%) on 4 different interventions described them as being available over a longer period ranging from 6 weeks to 3 months [ 70 , 72 , 79 , 84 , 85 , 87 , 90 , 91 ].

Comparator Types

A total of 42% (8/19) of the studies [ 72 , 77 - 80 , 85 , 87 , 92 ] used a passive comparator only, namely, a waitlist control group ( Multimedia Appendix 5 ). A total of 26% (5/19) of the studies used an active comparator only where participants were provided with minimal general health feedback regarding recommended guidelines for sleep, exercise, and nutrition [ 81 , 82 ]; strategies for healthy stress management [ 83 ]; educational materials about risky CU [ 88 ]; or access to a website containing information about cannabis [ 71 ]. In another 21% (4/19) of the studies, which used an active comparator, participants received the same digital intervention minus a specific component: a personal web-based coach [ 70 ], extended personalized feedback [ 89 ], web-based chat counseling [ 91 ], or information on risks associated with CU [ 86 ]. A total of 21% (4/19) of the studies had more than one control group [ 70 , 84 , 90 , 91 ].

Outcome Variable Assessment and Summary of Main Findings of the Studies

The methodological characteristics and major findings of the included studies (N=19) are presented in Multimedia Appendix 7 [ 67 , 70 - 72 , 77 - 92 , 115 - 120 ] and summarized in the following sections for each outcome of interest in this review (ie, CU and cannabis-related consequences). Of the 19 studies, 11 (58%) were reported as efficacy trials [ 7 , 77 , 79 , 81 - 83 , 86 - 88 , 91 , 92 ], and 8 (42%) were reported as effectiveness trials [ 70 - 72 , 78 , 84 , 85 , 89 , 90 ].

Across all the included studies (19/19, 100%), participant attrition rates ranged from 1.6% at 1 month after the baseline [ 77 , 78 ] to 75.1% at the 3-month follow-up [ 70 ]. A total of 37% (7/19) of the studies assessed and reported results regarding user engagement [ 71 , 78 , 84 , 85 , 90 - 92 ] using different types of metrics. In one article on the Marijuana eCHECKUP TO GO (e-TOKE) web-based program [ 78 ], the authors briefly reported that participation was confirmed for 98.1% (158/161) of participants in the intervention group. In 11% (2/19) of the studies, which were on a similar social media–delivered intervention [ 84 , 85 ], user engagement was quantified by tallying the number of comments or posts and reactions (eg, likes and hearts) left by participants. In both studies [ 84 , 85 ], the intervention group, which involved a CU-related Facebook page, displayed greater interactions than the control groups, which involved a Facebook page unrelated to CU. One article [ 84 ] reported that 80% of participants in the intervention group posted at least once (range 0-60) and 50% posted at least weekly. In the other study [ 85 ], the results showed that intervention participants engaged (ie, posting or commenting or clicking reactions) on average 47.9 times each over 8 weeks. In total, 11% (2/19) of the studies [ 90 , 91 ] on 2 different web-based intervention programs, both consisting of web documentation accompanied by chat-based counseling, measured user engagement either by average duration or average number of chat sessions. Finally, 16% (3/19) of the studies [ 71 , 91 , 92 ], which involved 3 different web-based intervention programs, characterized user engagement by the mean number of web modules completed per participant. Overall, the mean number of web modules completed reported in these articles was quite similar: 3.9 out of 13 [ 92 ] and 3.2 [ 91 ] and 3.5 [ 71 ] out of 6.

Assessment of CU

As presented in Multimedia Appendix 7 , the included studies differed in terms of how they assessed CU, although all used at least one self-reported measure of frequency. Most studies (16/19, 84%) measured frequency by days of use, including days of use in the preceding week [ 91 ] or 2 [ 80 ], days of use in the previous 30 [ 70 - 72 , 78 , 84 - 86 , 88 - 90 ] or 90 days [ 79 , 81 , 82 ], and days high per week [ 83 ]. Other self-reported measures of CU frequency included (1) number of CU events in the previous month [ 87 , 90 ], (2) cannabis initiation or use in the previous month (ie, yes or no) [ 77 ], and (3) days without CU in the previous 7 days [ 92 ]. In addition to measuring CU frequency, 42% (8/19) of the studies also assessed CU via self-reported measures of quantity used, including estimated grams consumed in the previous week [ 92 ] or 30 days [ 72 , 85 , 90 ] and the number of standard-sized joints consumed in the previous 7 days [ 91 ] or the previous month [ 70 , 71 , 89 ].

Of the 19 articles included, 10 (53%) [ 70 - 72 , 80 , 84 - 86 , 89 , 90 , 92 ] reported using a validated instrument to measure CU frequency or quantity, including the TLFB instrument [ 66 ] (n=9, 90% of the studies) and the Marijuana Use Form (n=1, 10% of the studies); 1 (10%) [ 79 ] reported using CU-related questions from an adaptation of the Global Appraisal of Individual Needs–Initial instrument [ 115 ]; and 30% (3/10) [ 81 , 82 , 91 ] reported using a questionnaire accompanied by a calendar or a diary of consumption. The 19 studies also differed with regard to their follow-up time measurements for assessing CU, ranging from 2 weeks after the baseline [ 80 ] to 12 months after randomization [ 90 ], although 12 (63%) of the studies included a 3-month follow-up assessment [ 70 - 72 , 79 , 81 , 82 , 84 , 85 , 88 , 90 - 92 ].

Of all studies assessing and reporting change in CU frequency from baseline to follow-up assessments (19/19, 100%), 47% (9/19) found statistically significant differences between the experimental and control groups [ 70 - 72 , 80 , 81 , 83 , 85 , 87 , 91 ]. Importantly, 67% (6/9) of these studies showed that participants in the experimental groups exhibited greater decreases in CU frequency 3 months following the baseline assessment compared with participants in the control groups [ 70 - 72 , 81 , 85 , 91 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 weeks after the baseline assessment [ 71 , 83 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 months following the baseline assessment [ 81 , 85 ], 11% (1/9) of the studies showed greater decreases in CU frequency at 2 weeks after the baseline [ 80 ], and 11% (1/9) of the studies showed greater decreases in CU frequency at 2 months after treatment [ 87 ].

In the study by Baumgartner et al [ 70 ], a reduction in CU days was observed in all groups, but the authors reported that the difference was statistically significant only between the intervention group with the service team and the control group (the reduction in the intervention group with social presence was not significant). In the study by Bonar et al [ 85 ], the only statistically significant difference between the intervention and control groups at the 3- and 6-month follow-ups involved total days of cannabis vaping in the previous 30 days. Finally, in the study by Buckner et al [ 80 ], the intervention group had less CU than the control group 2 weeks after the baseline; however, this was statistically significant only for participants with moderate or high levels of social anxiety.

Assessment of Cannabis-Related Negative Consequences

A total of 53% (10/19) of the studies also assessed cannabis-related negative consequences [ 78 - 84 , 86 , 88 , 92 ]. Of these 10 articles, 8 (80%) reported using a validated self-report instrument: 4 (50%) [ 81 , 82 , 86 , 88 ] used the 19-item Marijuana Problems Scale [ 67 ], 2 (25%) [ 78 , 79 ] used the 18-item Rutgers Marijuana Problem Index [ 121 , 122 ], and 2 (25%) [ 80 , 84 ] used the Brief Marijuana Consequences Questionnaire [ 116 ]. Only 10% (1/10) of the studies [ 92 ] used a screening tool, the Cannabis Abuse Screening Test [ 117 , 118 ]. None of these 10 studies demonstrated a statistically significant difference between the intervention and control groups. Of note, Walukevich-Dienst et al [ 86 ] found that women (but not men) who received an web-based PFI with additional information on CU risks reported significantly fewer cannabis-related problems than did women in the control group at 1 month after the intervention ( B =−1.941; P =.01).

Descriptive Summary of BCTs Used in Intervention Groups

After the 19 studies included in this review were coded, a total of 184 individual BCTs targeting CU in young adults were identified. Of these 184 BCTs, 133 (72.3% ) were deemed to be present beyond a reasonable doubt, and 51 (27.7%) were deemed to be present in all probability. Multimedia Appendix 8 [ 48 , 70 - 72 , 77 - 92 ] presents all the BCTs coded for each included study summarized by individual BCT and BCT cluster.

The 184 individual BCTs coded covered 38% (35/93) of the BCTs listed in the BCTTv1 [ 48 ]. The number of individual BCTs identified per study ranged from 5 to 19, with two-thirds of the 19 studies (12/19, 63%) using ≤9 BCTs (mean 9.68). As Multimedia Appendix 8 shows, at least one BCT fell into 13 of the 16 possible BCT clusters. The most frequent clusters were feedback monitoring , natural consequences , goal planning , and comparison of outcomes .

The most frequently coded BCTs were (1) feedback on behavior (BCT 2.2; 17/19, 89% of the studies; eg, “Once a week, participants receive detailed feedback by their counselor on their entries in diary and exercises. Depending on the involvement of each participant, up to seven feedbacks are given” [ 90 ]), (2) social support (unspecified) (BCT 3.1; 15/19, 79% of the studies; eg, “The website also features [...] blogs from former cannabis users, quick assist links, and weekly automatically generated encouragement emails” [ 71 ]), and (3) pros and cons (BCT 9.2; 14/19, 74% of the studies; eg, “participants are encouraged to state their personal reasons for and against their cannabis consumption, which they can review at any time, so they may reflect on what they could gain by successfully completing the program” [ 70 ]). Other commonly identified BCTs included social comparison (BCT 6.2; 12/19, 63% of the studies) and information about social and environmental consequences (BCT 5.3; 11/19, 58% of the studies), followed by problem solving (BCT 2.1; 10/19, 53% of the studies) and information about health consequences (BCT 5.1; 10/19, 53% of the studies).

RoB Assessment

Figure 2 presents the overall assessment of risk in each domain for all the included studies, whereas Figure 3 [ 70 - 72 , 77 - 92 ] summarizes the assessment of each study at the outcome level for each domain in the Cochrane RoB 2 [ 74 ].

Figure 2 shows that, of the included studies, 93% (27/29) were rated as having a “low” RoB arising from the randomization process (ie, selection bias) and 83% (24/29) were rated as having a “low” RoB due to missing data (ie, attrition bias). For bias due to deviations from the intended intervention (ie, performance bias), 72% (21/29) were rated as having a “low” risk, and for selective reporting of results, 59% (17/29) were rated as having a “low” risk. In the remaining domain regarding bias in measurement of the outcome (ie, detection bias), 48% (14/29) of the studies were deemed to present “some concerns,” mainly owing to the outcome assessment not being blinded (eg, self-reported outcome measure of CU). Finally, 79% (15/19) of the included studies were deemed to present “some concerns” or were rated as having a “high” RoB at the outcome level ( Figure 3 [ 70 - 72 , 77 - 92 ]). The RoB assessment for CU and cannabis consequences of each included study is presented in Multimedia Appendix 9 [ 70 - 72 , 77 - 92 ].

Meta-Analysis Results

Due to several missing data points and despite contacting the authors, we were able to carry out only 1 meta-analysis of our primary outcome, CU frequency. Usable data were retrieved from only 16% (3/19) [ 70 - 72 ] of the studies included in this review. These 3 studies provided sufficient information to calculate an effect size, including mean differences based on change-from-baseline measurements and associated 95% CIs (or SE of the mean difference) and sample sizes per intervention and comparison conditions. The reasons for excluding the other 84% (16/19) of the studies included heterogeneity in outcome variables or measurements, inconsistent results, and missing data ( Multimedia Appendix 10 [ 77 - 92 ]).

Figure 4 [ 70 - 72 ] illustrates the mean differences and associated 95% CIs of 3 unique RCTs [ 70 - 72 ] that provided sufficient information to allow for the measurement of CU frequency at 3 months after the baseline relative to a comparison condition in terms of the number of self-reported days of use in the previous month using the TLFB method. Overall, the synthesized effect of digital interventions for young adult cannabis users on CU frequency, as measured using days of use in the previous month, was −6.79 (95% CI −9.59 to −4.00). This suggests that digital CU interventions had a statistically significant effect ( P <.001) on reducing CU frequency at the 3-month follow-up compared with the control conditions (both passive and active controls). The results of the meta-analysis also showed low between-study heterogeneity ( I 2 =48.3%; P =.12) across the 3 included studies.

The samples of the 3 studies included in the meta-analysis varied in size from 225 to 1292 participants (mean 697.33, SD 444.11), and the mean age ranged from 24.7 to 31.88 years (mean 26.38, SD 3.58 years). These studies involved 3 different digital interventions and used different design approaches to assess intervention effectiveness. One study assessed the effectiveness of a web-based counseling program (ie, Quit the Shit) against a waitlist control [ 72 ], another examined the effectiveness of a fully self-guided web-based treatment program for CU and related problems (ie, Reduce Your Use: How to Break the Cannabis Habit) against a control condition website consisting of basic educational information on cannabis [ 71 ], and the third used a 3-arm RCT design to investigate whether the effectiveness of a minimally guided internet-based self-help intervention (ie, CANreduce 2.0) might be enhanced by implementing adherence-focused guidance and emphasizing the social presence factor of a personal e-coach [ 70 ].

Summary of Principal Findings

The primary aim of this systematic review was to evaluate the effectiveness of digital interventions in addressing CU among community-living young adults. We included 19 randomized controlled studies representing 9 unique digital interventions aimed at preventing, reducing, or ceasing CU and evaluated the effects of 3 different digital interventions on CU. In summary, the 3 digital interventions included in the meta-analysis proved superior to control conditions in reducing the number of days of CU in the previous month at the 3-month follow-up.

Our findings are consistent with those of 2 previous meta-analyses by Olmos et al [ 43 ] and Tait et al [ 44 ] and with the findings of a recently published umbrella review of systematic reviews and meta-analyses of RCTs [ 123 ], all of which revealed a positive effect of internet- and computer-based interventions on CU. However, a recent systematic review and meta-analysis by Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce CU. Beneria et al [ 45 ] included studies with different intervention programs that targeted diverse population groups (both adolescents and young adults) and use of more than one substance (eg, alcohol and cannabis). In our systematic review, a more conservative approach was taken—we focused specifically on young adults and considered interventions targeting CU only. Although our results indicate that digital interventions hold great promise in terms of effectiveness, an important question that remains unresolved is whether there is an optimal exposure dose in terms of both duration and frequency that might be more effective. Among the studies included in this systematic review, interventions varied considerably in terms of the number of psychoeducational modules offered (from 2 to 13), time spent reviewing the material, and duration (from a single session to a 12-week spread period). Our results suggest that an intervention duration of at least 6 weeks yields better results.

Another important finding of this review is that, although almost half (9/19, 47%) of the included studies observed an intervention effect on CU frequency, none reported a statistically significant improvement in cannabis-related negative consequences, which may be considered a more distal indicator. More than half (10/19, 53%) of the included studies investigated this outcome. It seems normal to expect to find an effect on CU frequency given that reducing CU is often the primary objective of interventions and because the motivation of users’ is generally focused on changing consumption behavior. It is plausible to think that the change in behavior at the consumption level must be maintained over time before an effect on cannabis-related negative consequences can be observed. However, our results showed that, in all the included studies, cannabis-related negative consequences and change in behavior (CU frequency) were measured at the same time point, namely, 3 months after the baseline. Moreover, Grigsby et al [ 124 ] conducted a scoping review of risk and protective factors for CU and suggested that interventions to reduce negative CU consequences should prioritize multilevel methods or strategies “to attenuate the cumulative risk from a combination of psychological, contextual, and social influences.”

A secondary objective of this systematic review was to describe the active ingredients used in digital interventions for CU among young adults. The vast majority of the interventions were based on either a theory or an intervention approach derived from theories such as CBT, MI, and personalized feedback. From these theories and approaches stem behavior change strategies or techniques, commonly known as BCTs. Feedback on behavior , included in the feedback monitoring BCT cluster, was the most common BCT used in the included studies. This specific BCT appears to be a core strategy in behavior change interventions [ 125 , 126 ]. In their systematic review of remotely delivered alcohol or substance misuse interventions for adults, Howlett et al [ 53 ] found that feedback on behavior , problem solving , and goal setting were the most frequently used BCTs in the included studies. In addition, this research group noted that the most promising BCTs for alcohol misuse were avoidance/reducing exposure to cues for behavior , pros and cons , and self-monitoring of behavior, whereas 2 very promising strategies for substance misuse in general were problem solving and self-monitoring of behavior . In our systematic review, in addition to feedback on behavior , the 6 most frequently used BCTs in the included studies were social support , pros and cons , social comparison , problem solving , information about social and environmental consequences , and information about health consequences . Although pros and cons and problem solving were present in all 3 studies of digital interventions included in our meta-analysis, avoidance/reducing exposure to cues for behavior was reported in only 5% (1/19) of the articles, and feedback on behavior was more frequently used than self-monitoring of behavior. However, it should be noted that the review by Howlett et al [ 53 ] examined digital interventions for participants with alcohol or substance misuse problems, whereas in this review, we focused on interventions that targeted CU from a harm reduction perspective. In this light, avoidance/reducing exposure to cues for behavior may be a BCT better suited to populations with substance misuse problems. Lending support to this, a meta-regression by Garnett et al [ 127 ] and a Cochrane systematic review by Kaner et al [ 128 ] both found interventions that used behavior substitution and credible source to be associated with greater reduction in excessive alcohol consumption compared with interventions that used other BCTs.

Beyond the number and types of BCTs used, reflecting on the extent to which each BCT in a given intervention suits (or does not suit) the targeted determinants (ie, behavioral and environmental causes) is crucial for planning intervention programs [ 26 ]. It is important when designing digital CU interventions not merely to pick a combination of BCTs that have been associated with effectiveness. Rather, the active ingredients must fit the determinants that the interventionists seek to influence. For example, action planning would be more relevant as a BCT for young adults highly motivated and ready to take action on their CU than would pros and cons , which aims instead to bolster motivation. Given that more than half of all digital interventions are asynchronous and based on a self-guided approach and do not offer counselor or therapist support, a great deal of motivation is required to engage in intervention and behavior change. Therefore, it is essential that developers consider the needs and characteristics of the targeted population to tailor intervention strategies (ie, BCTs) for successful behavior change (eg, tailored to the participant’s stage of change). In most of the digital interventions included in this systematic review, personalization was achieved through feedback messages about CU regarding descriptive norms, motives, risks and consequences, and costs, among other things.

Despite the high number of recent studies conducted in the field of digital CU interventions, most of the included articles in our review (17/19, 89%) reported on the development and evaluation of web-based intervention programs. A new generation of health intervention modalities such as mobile apps and social media has drawn the attention of researchers in the past decade and is currently being evaluated. In this regard, the results from a recently published scoping review [ 129 ], which included 5 studies of mobile apps for nonmedical CU, suggested that these novel modes of intervention delivery demonstrated adequate feasibility and acceptability. Nevertheless, the internet remains a powerful and convenient medium for reaching young adults with digital interventions intended to support safe CU behaviors [ 123 , 130 ].

Quality of Evidence

The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach [ 131 - 133 ] was used to assess the quality of the evidence reviewed. It was deemed to be moderate for the primary outcome of this review, that is, CU frequency in terms of days of use in the previous month (see the summary of evidence in Multimedia Appendix 11 [ 70 , 72 ]). The direction of evidence was broadly consistent—in all 3 RCT studies [ 70 - 72 ] included in the meta-analysis, participants who received digital CU interventions reduced their consumption compared with those who received no or minimal interventions. The 3 RCTs were similar in that they all involved a web-based, multicomponent intervention program aimed at reducing or ceasing CU. However, the interventions did differ or vary in terms of several characteristics, including the strategies used, content, frequency, and duration. Given the small number of studies included in the meta-analysis, we could not conclude with certainty which intervention components, if any, contributed to the effect estimate observed.

Although inconsistency, indirectness, and imprecision were not major issues in the body of evidence, we downgraded the evidence from high to moderate quality on account of RoB assessments at the outcome level. The 3 RCT studies included in the meta-analysis were rated as having “some concerns” of RoB, mainly due to lack of blinding, which significantly reduced our certainty relative to subjective outcomes (ie, self-reported measures of CU frequency). A positive feature of these digital intervention trials is that most procedures are fully automated, and so there was typically a low RoB regarding randomization procedures, allocation to different conditions, and intervention delivery. It is impossible to blind participants to these types of behavior change interventions, and although some researchers have made attempts to counter the impact of this risk, performance bias is an inescapable issue in RCT studies of this kind. Blinding of intervention providers was not an issue in the 3 RCTs included in the meta-analysis because outcome data collection was automated. However, this same automated procedure made it very difficult to ensure follow‐up. Consequently, attrition was another source of bias in these RCT studies [ 70 - 72 ]. The participants lost to follow-up likely stopped using the intervention. However, there is no way of determining whether these people would have benefited more or less than the completers if they had seen the trial through.

The 3 RCTs included in the meta-analysis relied on subjective self-reported measures of CU at baseline and follow‐up, which are subject to recall and social desirability bias. However, all 3 studies used a well-validated instrument of measurement to determine frequency of CU, the TLFB [ 66 ]. This is a widely used, subjective self-report tool for measuring frequency (or quantity) of substance use (or abstinence). It is considered a reliable measure of CU [ 134 , 135 ]. Finally, it should be pointed out that any potential bias related to self‐reported CU frequency would have affected both the intervention and control groups (particularly in cases in which control groups received cannabis‐related information), and thus, it was unlikely to account for differential intervention effects. Moreover, we found RoB due to selective reporting in some studies owing mainly to the absence of any reference to a protocol. Ultimately, these limitations may have biased the results of the meta-analysis. Consequently, future research is likely to further undermine our confidence in the effect estimate we observed and report considerably different estimates.

Strengths and Limitations

Our systematic review and meta-analysis has a number of strengths: (1) we included only randomized controlled studies to ensure that the included studies possessed a rigorous research design, (2) we focused specifically on cannabis (rather than combining multiple substances), (3) we assessed the effectiveness of 3 different digital interventions on CU frequency among community-living young adults, and (4) we performed an exhaustive synthesis and comparison of the BCTs used in the 9 digital interventions examined in the 19 studies included in our review based on the BCTTv1.

Admittedly, this systematic review and meta-analysis has limitations that should be recognized. First, although we searched a range of bibliographic databases, the review was limited to articles published in peer-reviewed journals in English or French. This may have introduced publication bias given that articles reporting positive effects are more likely to be published than those with negative or equivocal results. Consequently, the studies included in this review may have overrepresented the statistically significant effects of digital CU interventions.

Second, only a small number of studies were included in the meta-analyses because many studies did not provide adequate statistical information for calculating and synthesizing effect sizes, although significant efforts were made to contact the authors in case of missing data. Because of the small sample size used in the meta-analysis, the effect size estimates may not be highly reflective of the true effects of digital interventions on CU frequency among young adults. Furthermore, synthesizing findings across studies that evaluated different modalities of web-based intervention programs (eg, fully self-guided vs with therapist guidance) and types of intervention approaches (eg, CBT, MI, and personalized feedback) may have introduced bias in the meta-analytical results due to the heterogeneity of the included studies, although heterogeneity was controlled for using a random-effects model and our results indicated low between-study heterogeneity.

Third, we took various measures to ensure that BCT coding was carried out rigorously throughout the data extraction and analysis procedures: (1) all coders received training on how to use the BCTTv1; (2) all the included articles were read line by line so that coders became familiar with intervention descriptions before initiating BCT coding; (3) the intervention description of each included article was double coded after a pilot calibration exercise with all coders, and any disagreements regarding the presence or absence of a BCT were discussed and resolved with a third party; and (4) we contacted the article authors when necessary and possible for further details on the BCTs they used. However, incomplete reporting of intervention content is a recognized issue [ 136 ], which may have resulted in our coding BCTs incorrectly as present or absent. Reliably specifying the BCTs used in interventions allows their active ingredients to be identified, their evidence to be synthesized, and interventions to be replicated, thereby providing tangible guidance to programmers and researchers to develop more effective interventions.

Finally, although this review identified the BCTs used in digital interventions, our approach did not allow us to draw conclusions regarding their effectiveness. Coding BCTs simply as present or absent does not consider the frequency, intensity, and quality with which they were delivered. For example, it is unclear how many individuals should self‐monitor their CU. In addition, the quality of BCT implementation may be critical in digital interventions where different graphics and interface designs and the usability of the BCTs used can have considerable influence on the level of user engagement [ 137 ]. In the future, it may be necessary to develop new methods to evaluate the dosage of individual BCTs in digital health interventions and characterize their implementation quality to assess their effectiveness [ 128 , 138 ]. Despite its limitations, this review suggests that digital interventions represent a promising avenue for preventing, reducing, or ceasing CU among community-living young adults.

Conclusions

The results of this systematic review and meta-analysis lend support to the promise of digital interventions as an effective means of reducing recreational CU frequency among young adults. Despite the advent and popularity of smartphones, web-based interventions remain the most common mode of delivery for digital interventions. The active ingredients of digital interventions are varied and encompass a number of clusters of the BCTTv1, but a significant number of BCTs remain underused. Additional research is needed to further investigate the effectiveness of these interventions on CU and key outcomes at later time points. Finally, a detailed assessment of user engagement with digital interventions for CU and understanding which intervention components are the most effective remain important research gaps.

Acknowledgments

The authors would like to thank Bénédicte Nauche, Miguel Chagnon, and Paul Di Biase for their valuable support with the search strategy development, statistical analysis, and linguistic revision, respectively. This work was supported by the Ministère de la Santé et des Services sociaux du Québec as part of a broader study aimed at developing and evaluating a digital intervention for young adult cannabis users. Additional funding was provided by the Research Chair in Innovative Nursing Practices. The views and opinions expressed in this manuscript do not necessarily reflect those of these funding entities.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

JC contributed to conceptualization, methodology, formal analysis, writing—original draft, supervision, and funding acquisition. GC contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. BV contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, and visualization. PA contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. GR contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. GF contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. DJA contributed to conceptualization, methodology, formal analysis, writing—review and editing, and funding acquisition.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Detailed search strategies for each database.

Population, intervention, comparison, outcome, and study design strategy.

Excluded studies and reasons for exclusion.

Study and participant characteristics.

Description of intervention characteristics in the included articles.

Summary of methodological characteristics and major findings of the included studies categorized by intervention name.

Behavior change techniques (BCTs) coded in each included study summarized by individual BCT and BCT cluster.

Risk-of-bias assessment of each included study for cannabis use and cannabis consequences.

Excluded studies and reasons for exclusion from the meta-analysis.

Summary of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation tool.

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Abbreviations

Edited by T Leung, G Eysenbach; submitted 30.11.23; peer-reviewed by H Sedrati; comments to author 02.01.24; revised version received 09.01.24; accepted 08.03.24; published 17.04.24.

©José Côté, Gabrielle Chicoine, Billy Vinette, Patricia Auger, Geneviève Rouleau, Guillaume Fontaine, Didier Jutras-Aswad. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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0400 Comparing the Effectiveness of Cannabis to Treat Sleep Impairments Between Those with and Without Sleep Disorders

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Allison Herens, Erin Kelly, Emily Hajjar, Gregory Garber, Brooke Worster, 0400 Comparing the Effectiveness of Cannabis to Treat Sleep Impairments Between Those with and Without Sleep Disorders, Sleep , Volume 47, Issue Supplement_1, May 2024, Page A172, https://doi.org/10.1093/sleep/zsae067.0400

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People are increasingly turning to cannabis to treat sleep disorders and symptoms. Despite mixed results in prior research, there is a small body of research indicating that cannabis could have a positive impact on sleep disorders, as well as people who have sleep issues that haven’t arisen to a clinical level warranting diagnosis. It’s important to understand if there are differences in the effectiveness of these therapies between those who have a sleep disorder diagnosis versus those who do not.

Using an online cross-sectional survey, individuals across Pennsylvania self-reported how they used cannabis to treat sleep disturbances (N=1034). Participants self-reported demographics, employment status and hours, sleep diagnoses, mental health diagnoses, cannabis use for sleep, symptoms of Cannabis Use Disorder using the Cannabis Use Disorder Identification Test Short Form (CUDIT-SF, range: 0-12), and their sleep disturbances using the Insomnia Severity Index (ISI, range: 0-28). To determine if a formal sleep diagnosis is associated with insomnia severity while using cannabis, a one-way ANCOVA was used to compare the difference between those with and without sleep diagnoses on the ISI on days that cannabis was used. Analyses controlled for age, gender, race, number of mental health diagnoses, cannabis use disorder, employment status, and job shifts that may negatively impact sleep.

While using cannabis, both those with a diagnosis (M=9.56, SD=4.39) and without a diagnosis (M=8.56, SD=4.06) reported scores well below the clinical cutoff of 15 on the ISI. There was a significant effect of formal sleep disorders on ISI scores after controlling for stated covariates [F(1, 827)=6.69, p=.01]. While most participants used cannabis for sleep daily, a subset of the sample did not (n =222). Using a paired t-test, we compared if insomnia severity was significantly reduced on days when using cannabis compared to when not using. There was a significant increase in ISI scores on days that individuals did not use cannabis (M=13.43, SD=4.82) compared to days they used cannabis (M=8.76, SD=4.15), t(221)=14.85, p<.001.

While patients with formal sleep disorder diagnoses still report greater sleep issues, both groups showed significant improvement in their levels of impairment related to sleep while using cannabis.

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April 18, 2024

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Cannabis legalization and rising sales have not contributed to increase in substance abuse, study finds

by Boston College

The results of a new study, utilizing the most recent data on adolescent substance use to evaluate the effect of recreational cannabis legalization and retail sales on youth's use of cannabis, tobacco, and alcohol, appear in JAMA Pediatrics .

The study authors—co-principal investigators Rebekah Levine Coley, a Lynch School of Education and Human Development professor; School of Social Work Professor Summer Sherburne Hawkins; and Christopher F. Baum, chair of the Economics Department—are among the first to evaluate associations between recreational cannabis legislation and recreational cannabis retail sales through 2021.

"Although studies of early-enacting states and Canada reported few effects of recreational cannabis legislation on adolescent substance abuse , experts have highlighted the need to further assess policy outcomes in youth as legislation and retail availability spread, and other policies targeting youth substance use shift," the authors said. "We found limited associations between recreational cannabis legalization and retail sales with adolescent substance use, extending previous findings."

Since 2012, 24 states and Washington, D.C. have enacted recreational cannabis legislation, and 18 states have implemented recreational cannabis sales.

According to the researchers, recreational cannabis legalization was associated with modest decreases in cannabis, alcohol, and e-cigarette use, while retail sales were associated with lower e-cigarette use, and a lower likelihood, but also increased frequency of cannabis use among youth consumers, leading to no overall change in cannabis use.

"The results suggest that legalization and greater control over cannabis markets have not facilitated adolescents' entry into substance use," noted the study co-authors.

The researchers analyzed data from nearly 900,000 high school students in 47 states over a10-year period between 2011-2021.

According to the Pew Research Center, 54% of Americans live in a state where the recreational use of marijuana is legal, while 74% of Americans live in a state where marijuana is legal for either recreational or medical use. Also, 79% of Americans live in a county with at least one cannabis dispensary; as of February 2024, there are nearly 15,000 dispensaries operating in the U.S.

Researchers who contributed to the study included Naoka Carey, a doctoral candidate in the Applied Developmental and Educational Psychology department of the Lynch School; and Claudia Kruzik, a postdoctoral research associate at the University of Maryland-College Park.

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Cannabis, cannabinoids, and health

La cannabis, los cannabinoides y la salud, cannabis, cannabinoïdes et santé, genevieve lafaye.

Author affiliations: AP-HP, GH Paris-Sud, Hopital Paul Brousse, Dpt Addictologie, F94800 Villejuif, France; INSERM U1178, F94800 Villejuif, France

Laurent Karila

Lisa blecha, amine benyamina.

Cannabis (also known as marijuana) is the most frequently used illicit psychoactive substance in the world. Though it was long considered to be a “soft” drug, studies have proven the harmful psychiatric and addictive effects associated with its use. A number of elements are responsible for the increased complications of cannabis use, including the increase in the potency of cannabis and an evolution in the ratio between the two primary components, Δ 9 -tetrahydrocannabinol (Δ 9 -THC) and cannabidiol (toward a higher proportion of Δ 9 -THC), Synthetic cannabinoid (SC) use has rapidly progressed over the last few years, primarily among frequent cannabis users, because SCs provide similar psychoactive effects to cannabis. However, their composition and pharmacological properties make them dangerous substances. Cannabis does have therapeutic properties for certain indications. These therapeutic applications pertain only to certain cannabinoids and their synthetic derivatives. The objective of this article is to summarize current developments concerning cannabis and the spread of SCs. Future studies must further explore the benefit-risk profile of medical cannabis use.

La sustancia psicoactiva ilícita que se emplea con mayor frecuencia en el mundo es la cannabis. Aunque se consideró por largo tiempo una droga “suave”, los estudios han probado los efectos dañinos asociados con su empleo. Hay algunos elementos que son responsables del aumento de las complicaciones del empleo de cannabis, como el incremento en la potencia de ella y una evolución en la relación entre los dos componentes principales, el delta-9-tetrahidrocannabinol y el cannabidiol (con un porcentaje más importante de delta-9-tetrahidrocannabinol). El empleo de cannabinoides sintéticos (CS) ha tenido un rápido aumento en los últimos años, especialmente entre los usuarios frecuentes de cannabis, ya que tienen la ventaja de producir efectos psicoactivos similares a esta droga. La composición y las propiedades farmacológicas de los CS los hacen sustancias peligrosas. Para ciertas indicaciones la cannabis también tiene propiedades terapéuticas, pero esto se aplica sólo para ciertos cannabinoides y sus derivados sintéticos. El objetivo de este artículo es resumir el progreso actual relacionado con la cannabis y la difusión de los CS. Se requieren futuros estudios que promuevan la exploración del perfil de riesgo-beneficio del empleo medicinal de la marihuana.

Le cannabis (connu aussi sous le nom de marijuana) est la substance psychoactive la plus fréquemment utilisée dans le monde. Longtemps considérée comme une drogue « douce », des études ont prouvé les effets addictifs et psychiatriques nocifs associés à son utilisation. Un certain nombre d'éléments sont responsables de l'augmentation des complications liées à l'utilisation du cannabis, comme l'augmentation de sa puissance et une évolution du rapport entre les deux principaux composants, le Δ9-tetrahydrocannabinol (Δ9-THC) et le cannabidiol (avec une proportion plus importante de Δ9-THC). L'utilisation des cannabinoïdes synthétiques (CS) a rapidement progressé ces dernières années, principalement parmi les utilisateurs fréquents de cannabis, les CS apportant des effets psychoactifs similaires à ceux du cannabis. Cependant, leur composition et leurs propriétés pharmacologiques en font des substances dangereuses. Le cannabis a bien des propriétés thérapeutiques pour certaines indications. Ces applications thérapeutiques concernent seulement certains cannabinoïdes et leurs dérivés synthétiques. L'objectif de cet article est de résumer les développements actuels concernant le cannabis et la progression de l'utilisation des CS. Il faut entreprendre de nouvelles études pour mieux étudier le profit bénéfice-risque de l'usage médical du cannabis.

Introduction

Cannabis (also known as marijuana) is a psychoactive plant that contains more than 500 components, of which 104 cannabinoids have presently been identified. 1 Two of these have been the subject of scientific investigation into their pharmacological properties: Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD). Cannabis potency is primarily evaluated according to a sample's THC concentration. This is the primary psychoactive cannabinoid in cannabis. The adverse effects after acute or regular cannabis use are in direct relation to THC concentrations in the product. 2

Over the last few years, many studies have shown that CBD levels may also have an important impact. CBD may have a protective effect against certain negative psychological effects from THC. It may also be capable of antagonizing at least some of the adverse effects related to THC. 3

Various cannabis preparations are available on the illicit drug market: hashish, herbal cannabis (leaves and flowers), and oils. Real-time monitoring of confiscated cannabis preparations has enabled scientists to measure the potency of currently used products. Changes can then be compared with the prevalence of negative health consequences in users. Certain authors speculate that an increase in cannabis potency and in the ratio of the psychoactive component (Δ9-THC) to CBD may be the reason behind increases in harmful effects associated with cannabis use.

Furthermore, the last few years have seen a substantial rise in the use of synthetic cannabinoids (SCs), especially in frequent cannabis users. The attraction of SCs may be that whereas they provide psychoactive effects that are similar to cannabis and are also easily obtained, they are undetected through usual screenings. However, their composition and pharmacological properties make them potentially dangerous substances.

Here, we summarize current developments concerning cannabis and the spread of SCs. Despite increasing detrimental issues arising from cannabis use, studies have shown that this drug and some SCs may have a number of therapeutic effects, depending on the specific posology.

Cannabis today

Evolution of thc:cbd ratios.

Recent reports indicate that cannabis production is increasing and that cannabinoid formulations have been changing over the last two decades, especially with regard to their THC and CBD concentrations. This trend has been observed not only in the United States, but also in several European countries, such as the Netherlands and Italy. 4 - 6 Results are comparable since they use a similar validated methodology: samples seized by law enforcement officials are analyzed using gas chromatography with a flame ionization detector.

In a study by ElSohly et al, 4 38 681 samples seized in the United States between January 1, 1995 and December 31, 2014 were analyzed. The results showed an 8% increase in average THC levels during that period. In parallel, CBD concentration decreased from 0.5% to less than 0.2%. The resulting TIIC:CBD ratios increased from 14 in 1995 to 80 in 2014. 4 Elsewhere, Zamengo et al 5 analyzed 4962 cannabis products seized in the Venice area (Italy) from 2010 to 2013. Median THC content showed a significant increasing trend from about 6.0% to 9.5%, especially between 2012 and 2013, with the total median THC content showing an increase of about 16.7%. This increase pertained particularly to herbal materials (+25%), whereas resin materials increased by about +9.7%. Interestingly, the average and median THC content of handmade cigarettes, which was determined by calculating the percentage of THC in the whole tobacco-cannabis mixture, and which can be a relevant indicator when studying patterns of cannabis use, also showed significant increases in 2013: +28% and +45%, respectively.

Another study performed in the Netherlands in 2015 confirmed these results with a different trend. 6 Indeed, from 1999 to 2004, the THC content increased from 8.5% to 20%. In the years 2005 to 2015, they found a small but statistically significant decline in THC concentration: a 0.22% decrease per year. Thus, in the Netherlands, the THC content has remained stable during the last 10 years. This study emphasized the fact that global increases in THC levels and decreases in CBD levels are largely linked to the spread of indoor cultivation practices. On average, cultivars from the Netherlands are twice as potent as imported products. The high THC concentrations obtained from the various cannabis varieties result from technical advances in production, such as genetic manipulations, cross-breeding, and improvements in indoor hydroponic cultivation. As advanced techniques and more potent seeds have become more widely available, this has contributed to the steadily increasing THC concentrations in cannabis. 4

These changes may have significant real-world clinical consequences because the chances of detrimental psychological effects seem to increase when cannabis with high concentrations of THC is consumed. 7 , 8 CBD:THC ratio also appears to be an important factor. 7 , 9 Epidemiological studies have shown that cannabis use during adolescence is an important risk factor in the development of schizophrenia later in life. 4 These studies seem to show a risk of psychotic effects that is proportional to THC concentrations and inversely proportional to CBD concentrations. Some data also suggest that the CBD:THC ratio may play a role in the risk of addiction. 2 , 7 - 9

Emerging market of synthetic drugs: synthetic cannabinoids

Synthetic cannabinoids (SCs) emerged in the 1970s when researchers were first exploring the endocannabinoid system and attempting to develop new treatments for cancer pain. Around the year 2000, SC appeared on the illicit drug market, where their prevalence had long been underestimated. Since then, their place in the market has steadily increased. More than 560 synthetic psychoactive substances have been identified on the illicit market. There has been a steep rise over the last 5 years with the appearance of 380 new synthetic drugs. Since 2008, more than 160 SCs have been identified in various products, 24 of which appeared in 2015. 10 Most SCs are manufactured by chemical companies located in Asia (China, South Korea). Today, intra-European production is closely monitored. 10 , 11 Current legislation is frequently defeated and outwitted by manufacturers who regularly modify their chemical formulations, resulting in rapid turnover of SCs. Indeed, each SC is replaced by newer analogs within a year or two. 12

SC use varies a great deal between different countries and populations. 13 For example, in Spain in 2012, there was a low percentage (1.4%) of use of “Spice” and its derivatives among youth between 14 and 18 years of age. In 2013 in Germany, a survey conducted with students between 15 and 18 years of age showed that 5% of them had used herbal blends. In 2016, the European Monitoring Center for Drugs and Drug Addiction's (EMCDDAs) 2015 European School Survey Project on Alcohol and Other Drugs (ESPAD) report showed that approximately 4% of all youths between 15 and 16 years of age had used an SC at least once in their lifetime; few differences were noted between boys and girls.

Compared with other new drugs on the market, the increase in consumption of SCs was particularly remarkable. 13 Generally, these products are offered as herbal blends. They may also be sold as tablets, capsules, or powders. 14 Frequently, they are smoked by pipe or as a joint. 15 Recently, newer liquid formulations have appeared that can be vaped via electronic cigarette. 16

SCs have different pharmacological properties than cannabis. These molecules are particularly lipophilic 15 and are full agonists of CBD receptor 1 (CB1) and CBD receptor 2 (CB2). 17 Their potential binding affinity for these receptors is also much stronger than that of THC, thus causing much more pronounced psychoactive effects. They also do not contain any CBD whatsoever, contrary to cannabis, where it is present in varying concentrations. 18

Products of the same brand and sold under the same name have highly variable product compositions and concentrations. 19 , 20 SC effects depend on the type of product used and its dose. Similarly, the pharmacokinetics depends on the administration route. In some cases, the onset of psychoactive effects and physical symptoms begins a few minutes after smoking. 15 The effects are comparable to those observed after high doses of THC, and the high efficacy — as well as differences from batch to batch — results in the risk of accidental overdosing. 21 Anxiety is frequently reported. Some users have described feeling limited in their movements, whereas no motor deficits are objectively observed. On average, the effects last for about 6 hours, steadily decreasing until the next day. 15 , 21 , 22

Herbal formulations containing SC that are smoked, known as Spice, imitate the psychoactive effects of THC. 19 Several studies have shown that a large majority of SC users are also frequent cannabis consumers, 23 , 24 especially among adolescents. 25 It is possible such use is influenced by the fact that whereas SCs provide similar psychoactive effects to cannabis, they are not detected during routine screening. 26 Some consumers may also use SC in order to decrease their cannabis use or to diminish symptoms of cannabis withdrawal. 26

Certain serious complications

Evolution of thc:cbd ratios and psychosis risk.

Almost 30 years ago, Andreasson et al showed an association between cannabis use and the later emergence of schizophrenia. 27 Since then, numerous prospective, longitudinal studies have been published. Despite confounding factors, sufficient proof currently exists showing that cannabis use increases the risk of psychotic disorders. 28

Over the last 5 decades, increasing THC concentrations have been observed in products available in many countries. In the 1970s, the THC concentration in cannabis found in England and in the Netherlands was less than 3%. Current varieties contain on average 16% in England and 20% in the Netherlands. New cannabis preparation techniques have led to products containing THC levels of up to 40%. Traditional hashish (resin) contains THC and CBD in similar proportions. However, newer varieties and forms, such as sinsemilla, have high THC levels but contain almost no CBD. 29

Some studies have indicated that CBD may have antipsychotic properties. 30 , 31 One recent case-control study revealed that the use of cannabis with high levels of THC may be associated with an increased risk of psychosis, especially when its CBD levels are low. 32 Certain recent epidemiological studies have shown an increased incidence of schizophrenia in countries such as England and the Netherlands where highly THC-concentrated cannabis is regularly used versus in Italy where more traditional cannabis varieties with lower concentrations of THC are used. 29 High THC cannabis may increase the risk of earlier psychosis onset. One study has suggested an association between dose and response, showing that daily users of high-dose cannabis begin their first psychotic episode an average of 6 months earlier than those who had never used cannabis. 7 A recent meta-analysis has also shown that continued use may have a negative impact on schizophrenia outcome. Psychotic patients who continue to use cannabis had a significantly greater number of relapses than patients who had stopped using cannabis or had never used. 33

Based on studies examining the evolution of THC levels in cannabis over the last few decades, one hypothesis is that previous studies may have underestimated the impact of cannabis on existing psychosis. In fact, ecological proof seems to argue in favor of greater psychosis risk among youths who have recently been exposed to high-dose cannabis than in former generations exposed to lower THC doses. Such an analysis, however, has yet to be performed. 34 Future research would need to show that different cannabis varieties are associated with different psychosis risks.

It is too soon to confirm this hypothesis. Current clinical data are insufficient to justify prevention measures concerning cannabis use or restriction of highly concentrated varieties. Estimates that integrate data from different countries have shown that between 8% and 24% of all psychotic disorders could be avoided if use of highly concentrated cannabis were prevented. 32

Psychiatric, addictive, and physical consequences of SC use

Numerous complications have been observed in SC users. 35 Because of its pharmacological characteristics, SC may be the source of more serious adverse effects than those seen with cannabis. 15 , 17 , 18

Anxious symptoms, such as ruminations, anxiety, and panic attacks, are often seen following SC use. Sleep disorders, hyperactivity, agitation, and irritability have also been reported. Acute intoxication may be associated with cognitive disorders such as short-term memory loss. There have also been cases of paranoia, flashbacks, and suicidal ideation. 36 , 37 In one case report, manic symptoms were noted to have followed a single use of SC. 38

Although SCs have a similar mechanism of action to THC, the different pharmacological properties, such as higher affinity for CB1 and CB2 receptors, higher efficacy, as well as the absence of CBD, result in different physiological and toxicological effects, especially concerning its pro-psychotic effects. The psychotogenic effects of SC are increasingly alarming, with numerous reports of individuals who become psychotic after SC use. 39 , 40

Delirious symptoms, acoustico-verbal hallucinations, and dissociative elements have all been described in individuals without a history of psychosis. 41 , 42 Two cases of catatonia after SC use have also been reported in patients with no history of psychosis. 43 SC may also worsen psychotic symptoms in patients who were previously stabilized or cause transitory psychotic episodes in healthy, but vulnerable individuals. 15

SCs are potentially addictogenic because these substances can increase dopamine secretion within the nucleus accumbens and the ventral tegmental area. 18 , 19 , 44 - 46 Symptoms of tolerance and of withdrawal resulting from long-term use have also been described. 36 , 47 , 48 The symptoms associated with ceasing SC use are similar to cannabis-withdrawal syndrome: sleep disturbances, intense dreams, severe anxiety, nausea, restlessness or leg cramps, sweating, shaking, and loss of appetite. 49 Nacca et al observed that withdrawal symptoms lasted an average of 6 days after quitting. Intense and severe cravings have also been reported. 50

An increasing number of nonfatal intoxications, as well as deaths, after presentation to the emergency room or in consultation have been reported, especially in young people. 51 SC use has been associated with the following physical complications: cardiac, pulmonary, neurological, digestive, renal, and even dermatological. 13 These consequences may be severe and potentially fatal, as reported in a number of cases in the literature. 52

Therapeutic applications of cannabis and cannabinoids

THC is the psychoactive principle of cannabis, inducing the cannabis inebriation sought by many users. Its addictive potential and negative consequences are now well known. 53 The effects of CBD are distinct and, in many cases, the opposite of THC's effects. CBD seems not to induce euphoria and seems to have antipsychotic, anxiolytic, antiepileptic, and anti-inflammatory properties. 54

According to an evaluation (in 1999) by the Institute of Medicine in the United States on cannabis as a medication, the future of medical cannabis resides in isolating its cannabinoid components and their synthetic derivatives. The variable composition within the raw cannabis plant and especially the differing THC/CBD ratios make therapeutic applications of these products quite complex. 55

Various forms of cannabis have been studied to ascertain the therapeutic properties of cannabis. Currently, three molecules have been approved by the US Food and Drug Administration (FDA); a single molecule in Canada and Europe. 56 Dronabinol, a synthetic THC, has been approved by the FDA in the treatment of anorexia in patients suffering from AIDS and as a second-line treatment in nausea and vomiting induced by cancer chemotherapies. Nabiximols, a combination of synthetic THC and CBD in equal proportions, is delivered in spray form. It has been approved in several countries (Canada, Europe), but not in the United States, as an adjunctive therapy in the treatment of spastic pain in patients with neurological disorders. 56

A 2015 meta-analysis reviewed randomized clinical trials worldwide of medical cannabis and cannabinoids from 1974 through 2014. 57 This study analyzed the results from 79 clinical trials performed in various domains: chronic pain, nausea and vomiting induced by chemotherapy, spasticity in multiple sclerosis or in paraplegics, orexigenic effects in patients with human immunodeficiency virus (HIV) or AIDS, sleep disorders, Tourette syndrome, psychosis, anxiety disorders, intraocular pressure from glaucoma, and depression. The most frequently studied cannabinoid forms were medications produced by pharmaceutical companies: nabilone, nabiximols, and dronabinol. The other evaluated cannabinoids included THC, CBD, and a combination THC/CBD. This study included only two trials using plant-based cannabis (smoked and vaped).

The results of this meta-analysis revealed moderate-quality proofs in favor of nabiximols, nabilone, dronabinol, or THC/CBD in treating spasticity from multiple sclerosis. The same level of proof was shown for nabiximols or smoked THC in the treatment of chronic cancer pain and neuropathic pain. Proofs of lesser quality were found in favor of dronabinol or nabiximols in treating nausea and vomiting induced by chemotherapy and in weight gain in HIV/AIDS patients; for nabilone and nabiximols in treating sleep disorders; and for THC capsules in treating Tourette syndrome. This meta-analysis showed that CBD was not significantly more efficient in treating psychosis than a usual antipsychotic, such as amisulpiride, or depression compared with nabiximols. Finally, one very small crossover trial with six patients was not able to detect an effect of cannabinoids on intraocular pressure. 57

A systematic review by the American Academy of Neurology examined publications from 1948 through November 2013 concerning the use of cannabinoids in the treatment of multiple sclerosis, movement disorders, and epilepsy. 58 Only oral cannabis extracts (combined THC/CBD or CBD alone) had a sufficient level of proof in treating spasticity from multiple sclerosis and central pain. The other formulations seemed to be effective in these indications, but with lower levels of proof. Proof was insufficient to conclude as to the efficacy of smoked cannabis. In other neurological indications, such as Huntington disease and Tourette syndrome, proofs were judged insufficient.

Cannabinoids would seem to have some therapeutic interest in the following indications: epilepsy, addictions, psychotic disorders, anxiety, and sleep disorders. However, there are currently insufficient levels of proof. Indeed, a Cochrane review from 2014, for example, concluded that there were insufficient levels of proof for cannabinoids in the treatment of epilepsy. 59 Nevertheless, cannabis-based treatments continue to elicit great interest. They remain the subject of preclinical and human research. In animal studies, CBD has shown significant antiepileptic activity, reducing seizure severity. Recent studies in young patients suffering from severe, treatment-resistant epilepsy have shown that CBD may have a specific indication in these forms. 60 , 61

Due to its implications in the reward system, endocannabinoid signaling represents a potential therapeutic target in treating addictions. The results from randomized, controlled trials suggest that CB1 receptor agonists such as dronabinol and nabiximols may be effective in treating cannabis withdrawal. Dronabinol may also decrease opioid withdrawal symptoms. Rimonabant, an inverse agonist of CB1 receptors, has shown promising effects in tobacco cessation; it also causes adverse psychiatric effects. Few clinical trials have examined the effect of cannabinoids in treating alcohol-use disorder; those examining rimonabant have shown negative results. 62 A systematic review has examined the preclinical and clinical data on the impact of CBD on addictive behaviors. Fourteen studies were found, nine in animals and five in humans. Some preclinical studies suggest that CBD may have some therapeutic properties in treating opioid-, cocaine-, and psychostimulant-use disorders. Some preliminary data suggest that it could be advantageous in treating cannabis and tobacco-use disorder in humans. 63 , 64

One randomized, double -blind clinical trial compared the use of CBD versus amisulpride for 4 weeks in, respectively, 20 and 19 patients with psychosis. This study showed comparable efficacy between amisulpride and CBD (Positive and Negative Syndrome Scale [PANSS], Brief Psychiatric Rating Scale [BPRS]). A potential advantage for CBD is its milder side effects: fewer extrapyramidal symptoms, less weight gain, and no hyperprolactinemia. 65

Contrary to the effects of THC, several preclinical studies have shown that CBD may have anxiolytic effects. 66 , 67 In individuals with social anxiety, CBD 400 mg considerably decreases anxiety measures versus placebo; measures were correlated with decreased activity within the limbic and paralimbic areas on functional magnetic resonance imaging (fMRI). 67 One clinical trial in healthy volunteers has shown that acute CBD administration (300-600 mg) seems to decrease experimentally induced anxiety without modifying baseline anxiety levels; it would also seem to decrease social phobias. 68

The understanding of the relationship between sleep and cannabinoids has been obscured by significant methodological differences resulting in mitigated results. The results from the literature seem to favor a beneficial effect of acute cannabis intoxication on sleep. On the other hand, regular cannabis use seems to have a negative impact on sleep quality. Different cannabinoids seem to have a differential impact on sleep. One study has suggested a therapeutic potential for dronabinol and nabilone on sleep disorders and nightmares. 69 Studies specifically examining CBD have shown that when used at small doses, it may have some stimulant effects. 70 At medium-to-high doses, it seems to have a more sedative effect and thus may improve sleep quality. 71 When CBD is associated with THC, it seems to reduce slow-wave sleep. 72

Thus, there is preclinical evidence and some clinical evidence for therapeutic properties regarding a number of diseases. However, larger controlled clinical trials are needed to show efficacy and safety for each disorder.

Cannabis use and its negative consequences have increased over the last several years in parallel with increasing cannabis potencies. SCs seem to be particularly popular among cannabis users. This emerging market represents a specific public health problem in light of the severe complications in relation to their use. What the risks are of developing a psychotic disorder after SC administration remains a fundamental question.

This is an emerging area of research in which more robust epidemiological studies must be developed. These must provide detailed information concerning not only the quantity and the frequency of cannabis use, but also, and more importantly, the type of cannabis used. Longitudinal studies including precise THC and CBD measurements must be established in order to clarify the impact of THC/CBD ratios on psychosis risk. The use of SCs must also be more largely examined in light of the severe consequences associated with their use.

The legislative policies that have been established to reduce the risks in relation to cannabis have long represented an obstacle to research concerning medical cannabis use. Improved knowledge of the endocannabinoid system and of exocannabinoids has proven that cannabis may have significant therapeutic effects. Despite sparse research, certain countries, such as the United States, have authorized the use of plant-based medical cannabis. 73 Future studies must further explore the benefit-risk profile of medical cannabis use.

Acknowledgments

The authors have no conflicts of interest to declare.

• Original research
• Open access
• Published: 23 June 2021

Cannabidiol use and effectiveness: real-world evidence from a Canadian medical cannabis clinic

• Lucile Rapin   ORCID: orcid.org/0000-0003-3603-4168 1 ,
• Rihab Gamaoun 1 ,
• Cynthia El Hage 1 ,
• Maria Fernanda Arboleda 1 &
• Erin Prosk 1  

Journal of Cannabis Research volume  3 , Article number:  19 ( 2021 ) Cite this article

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Cannabidiol (CBD) is a primary component in the cannabis plant; however, in recent years, interest in CBD treatments has outpaced scientific research and regulatory advancement resulting in a confusing landscape of misinformation and unsubstantiated health claims. Within the limited results from randomized controlled trials, and lack of trust in product quality and known clinical guidelines and dosages, real-world evidence (RWE) from countries with robust regulatory frameworks may fill a critical need for patients and healthcare professionals. Despite growing evidence and interest, no real-world data (RWD) studies have yet investigated patients’ reports of CBD impact on symptom control in the common expression of pain, anxiety, depression, and poor wellbeing. The objective of this study is to assess the impact of CBD-rich treatment on symptom burden, as measured with a specific symptom assessment scale (ESAS-r).

This retrospective observational study examined pain, anxiety, depression symptoms, and wellbeing in 279 participants over 18 years old, prescribed with CBD-rich treatment at a network of clinics dedicated to medical cannabis in Quebec, Canada. Data were collected at baseline, 3 (FUP1), and 6 (FUP2) month after treatment initiation. Groups were formed based on symptom severity (mild vs moderate/severe) and based on changes to treatment plan at FUP1 (CBD vs THC:CBD). Two-way mixed ANOVAs were used to assess ESAS-r scores differences between groups and between visits.

All average ESAS-r scores decreased between baseline and FUP1 (all p s < 0.003). The addition of delta-9-tetrahydrocannabinol (THC) during the first follow-up had no effect on symptom changes. Patients with moderate/severe symptoms experienced important improvement at FUP1 (all p s < 0.001), whereas scores on pain, anxiety, and wellbeing of those with mild symptoms actually increased. Differences in ESAS-r scores between FUP1 and FUP2 were not statistically different.

This retrospective observational study suggests CBD-rich treatment has a beneficial impact on pain, anxiety, and depression symptoms as well as overall wellbeing only for patients with moderate to severe symptoms; however, no observed effect on mild symptoms. The results of this study contribute to address the myths and misinformation about CBD treatment and demand further investigation.

Cannabidiol (CBD) is one of the primary cannabinoids found in significant but variable concentrations in cannabinoid-based medicines (CBM). While structurally similar to Δ9-tetrahydrocannabinol (THC), CBD does not cause intoxication or euphoria (Russo 2017 ) and has showed considerable tolerability in humans with a low abuse potential (Chesney et al. 2020 ). This favorable safety profile has led to the recent mitigation of legal and regulatory barriers surrounding purified CBD products in several countries and recent increased interest in CBD treatments. While recent rulings clarified that CBD is not a drug under the 1961 United Nations as Single Convention on Narcotic Drugs, regulatory status in the USA remains extremely confusing. When derived from cannabis, CBD is a schedule 1 drug but when derived from “industrial hemp” plants it may be lawful federally (Corroon and Kight 2018 ; Corroon et al. 2020 ). In Canada, CBD is controlled under the Cannabis Act as are all cannabinoids, cannabis, and cannabis-derived products (Canada Go 2021 ). This regulatory status imparts restrictions and access obstacles for researchers.

CBD is widely touted as a panacea for a wide range of health problems and has been marketed as a dietary and “wellness” product (Russo 2017 ; Khalsa et al. 2020 ; Eisenstein 2019 ). CBD’s potential effects as an add-on therapy have been studied for social anxiety disorders, schizophrenia, non-motor symptoms in Parkinson’s disease, and substance use disorders (Bergamaschi et al. 2011 ; Crippa et al. 2019 ; McGuire et al. 2018 ; Millar et al. 2019 ; Prud’homme et al. 2015 ; Thiele et al. 2019 ; Leehey et al. 2020 ). However, the evidence of its effectiveness for indications other than drug-resistant pediatric epilepsy conditions remains very limited (Larsen and Shahinas 2020 ; Franco et al. 2020 ) and safety considerations such as drug-drug interactions associated with unsupervised use remain (Chesney et al. 2020 ; Freeman et al. 2019 ). Randomized controlled trials (RCTs) are limited in their rigorous design, population sample, and duration of observation making generalization of results and long-term data scarce. Therefore, real-world evidence (RWE) provides valuable insights and supplemental information about the use, safety, and effectiveness of CBD-based treatments (Graham et al. 2020 ).

RWE from retrospective analyses and patient registries shows that CBMs are used for pain (chronic, neuropathic), mental health conditions, cancer-related symptoms (nausea, fatigue, weakness), HIV/AIDS, and neurological conditions (Bonn-Miller et al. 2014 ; Gulbransen et al. 2020 ; Lintzeris et al. 2020 ; Lucas and Walsh 2017 ; Sexton et al. 2016 ; Waissengrin et al. 2015 ). Symptom control is the primary reason for use of CBM, with most patients looking to address unalleviated symptoms, perceived symptom intensity, and burden on health-related quality of life independently of primary diagnosis (Sexton et al. 2016 ; Waissengrin et al. 2015 ; Baron et al. 2018 ; Purcell et al. 2019 ; Swift et al. 2005 ; Webb and Webb 2014 ). The Edmonton Symptom Assessment Scale-revised version (ESAS-r) is a validated scale to assess symptom burden developed for use in oncology and palliative care (Hui and Bruera 2017 ), it has relevance to medical cannabis care as patients are often treated for similar symptom management (Good et al. 2019 ; Pawasarat et al. 2020 ). Specifically, studies showed self-perceived improvement in ESAS-r emotional symptoms (anxiety and depression) scores following CBM treatment in oncology patients, while pain and wellbeing symptoms showed no improvement (Good et al. 2019 ; Pawasarat et al. 2020 ). Yet, RWE on CBD-rich products is scarce (Goodman et al. 2020 ; Shannon et al. 2019 ). In addition, although careful titration and treatment adjustment after initiation is critical to symptom improvement and adverse effects care, current literature has failed to address this issue.

In this study, we investigated treatment with CBD-rich products within a dedicated clinical setting in Quebec, Canada, and the effects on a very common clinical symptom expression of pain and comorbid anxiety and depression symptoms, as well as the effect on overall wellbeing. We also examined the relevant clinical effects that were observed when CBD-rich treatments were replaced by THC:CBD-balanced products at subsequent follow-up visits.

Study population

This study is a retrospective examination of patients who were prescribed CBD-rich products by physicians at a clinic dedicated to CBM treatments operating at four locations across Quebec, Canada. All data are collected as part of standard clinical procedures during the initial visit and during 3 (FUP1) and 6 (FUP2) month follow-up visits and extracted from electronic medical records (EMR) (Prosk et al., 2021 ). All data were anonymized following extraction from the EMR and no identifiers linking to original data were maintained. A waiver of consent was required and approved by Advarra Ethics Committee, who also approved the study protocol, and by the provincial privacy commission ( La commission d ’ accès à l ’ information du Quebec ).

Adult patients, at least 18 years of age, who were initially treated exclusively with CBD-rich products from 1 October 2017 to 31 May 2019 and for whom outcome scores and product information were recorded at FUP1 were included in this study. Patients were generally referred by primary-care physicians and specialists for an assessment on the suitability of medical cannabis to treat refractory symptoms. A complete medical history, including primary and secondary diagnoses, was collected at baseline visit. Medical cannabis treatment decisions are determined at the discretion of a clinic physician according to a standardized clinical procedure, including symptom identification, selection of product format, cannabinoid profile, and dosage based on existing evidence (MacCallum and Russo 2018 ; Cyr et al. 2018 ), but also to minimize risk of adverse effects. Patient and physician preference may also indicate initiation with products that have higher CBD and lower THC concentration in order to limit use of THC and its inherent potential adverse events. The follow-up visits serve to assess treatment compliance, safety, and effectiveness.

CBD-rich products in Canada

CBD-rich products are administered in various methods and formats, but most commonly as oral plant-derived extracts or oils and as inhaled dried flowers. In the Canadian medical cannabis program, CBD-rich cannabis oils contain approximately 0.5–1 mg of THC/mL and 20–25 mg of CBD/mL depending on the product manufacturer. Table 1 provides cannabinoid content and THC:CBD ratio for the three most common oil products (over 85% of patients) authorized at the clinic. Furthermore, product details in this study sample are described in Table 3 . The clinic procedure dictates that all products with a ratio of CBD (mg) to THC (mg) higher than 10 are considered CBD-rich products.

Treatment adjustments occur at follow-up visits as a result of lack of effectiveness, presentation of adverse effects, or social or economic barriers. Adjustments may include a change of the recommended CBD-rich product, method of administration, dosage, or a change in product formulation such as the introduction of THC:CBD-balanced or THC-rich products. We investigated the change from CBD-rich to THC:CBD products during FUP1 by forming two groups based on their product adjustment at FUP1 (CBD-rich vs THC:CBD). Products at FUP1 reflect those recommended at the visit. Therefore, the adjusted treatment affects only the evaluation at FUP2.

Patients age, sex, and diagnosis were recorded at baseline. Patients completed the ESAS-r (Edmonton Symptom Assessment System-revised version) at each visit. The ESAS-r is a self-administered scale, rating the severity of symptoms from 0 (absence of symptom) to 10 (worst possible severity) at the time of assessment (Hui and Bruera 2017 ). Symptoms evaluated include six physical- (pain, tiredness, nausea, drowsiness, lack of appetite, and shortness of breath), two emotional- (depression, anxiety), and one overall wellbeing-related symptoms. ESAS scores can be categorized as mild (score 0 to 3) moderate (score 4 to 6) or high (score 7 and above) (Butt et al. 2008 ) and the threshold for clinically significant improvement is a decrease of 1 point (Hui et al. 2015 ). Since pain and mental health issues represent the most common symptoms for patients and physicians seeking medical cannabis treatments, we investigated effects on pain, depression, and anxiety symptoms as well as overall wellbeing. For each symptom, two groups of patients were formed: moderate-severe severity group in which a baseline score of 4 or more was recorded and a mild severity group with baseline score of 0 to 3.

Mean scores and standard deviation (SD), as well as percentage, where appropriate are presented for each variable. All analyses were performed on each ESAS-r symptom separately through the data analytics software R v4.0.2. An initial analysis compared the overall ESAS-r scores between each visit no matter the severity of the group, and looked at the role of product group (CBD/THC:CBD vs CBD/CBD group) (between-factor). Tukey HSD post hoc test was used to confirm where the differences occurred between groups.

To determine whether CBD-based treatments have different effectiveness based on the severity of patient symptoms, two-way mixed ANOVAs with severity group as between-factor and visit as a within-factor were conducted to assess the change in ESAS-r scores between visits. Paired t-tests were subsequently performed to assess the difference in mean scores within each severity group between baseline and FUP1. Significant p value was set at 0.05 and all analyses were two-tailed. Partial eta-squared (η 2 p ) are reported to indicate magnitude of differences between groups.

A total of 1095 patients were seen at the four clinic sites during the study period. Out of those, 715 were eligible for the study (at least 18 years old and initially treated exclusively with CBD-rich products). A total of 279 patients with ESAS-r scores and product information at FUP1 were analyzed (190 (68%) female, mean age = 61.1, SD = 16.6). The analyzed sample did not differ from the study-eligible group in terms of age, sex, or THC and CBD initial doses (all p s > 0.4). Table 2 outlines patient sample size and demographic information for each symptom and treatment group. Two hundred and ten (75%) patients were prescribed CBD-rich products to treat chronic pain, 19 (7%) for cancer-related symptoms, 21 (7.5%) to treat neurological disorders (Parkinson’s disease, multiple sclerosis, and drug-resistant epilepsy among others), 8 patients for inflammatory disease (arthritis), 10 for gastrointestinal disorders (Chron’s disease, inflammatory bowel syndrome, ulcerative colitis), 2 for anxiety, 1 for depression, 2 for headaches, and 6 unclassified. The chronic pain category included all medical indications for which pain was the main symptom such as but not limited to fibromyalgia, spinal stenosis, and chronic low back pain. Overall, 116 (41.6%) patients adjusted their prescription by adding THC at FUP1 (either to a THC:CBD-balanced combination or a THC-rich treatment). Two hundred and three (73%) patients had moderate/severe ESAS-r scores on at least 2 of the examined symptoms, 57 (20%) on three, and 75 (27%) on all four symptoms. Twenty-nine (10%) patients report no moderate/severe symptoms; these people may use CBD for other ESAS-r symptoms not examined here (shortness of breath, tiredness, nausea, drowsiness, appetite). There was no statistical difference on either age, sex, or THC and CBD initial doses between the patients who completed one FUP versus those who completed two FUP (all p s > 0.1).

CBD-rich products characteristics

The baseline average daily doses for CBD and THC are presented in Table 3 . The maximum initial CBD dose recorded (156 mg) was prescribed for the treatment of pain of one patient. The maximum THC dose recorded at FUP1 (90 mg) was prescribed for two patients for the treatment of pain.

Outcome of CBD treatment

Mean ESAS-r scores of pain, anxiety, depression symptoms, and overall wellbeing at baseline, FUP1, and FUP2 are described in Table 4 and Fig. 1 .

CBD-rich treatment effectiveness on pain, anxiety, depression symptoms, and on overall wellbeing in 279 patients. FUP1, follow-up visit at 3 month; FUP2, follow-up visit at 6 month. Mixed ANOVAs revealed a significant effect of visit on symptom reduction between baseline and FUP1 but not between FUP1 and FUP2

All average ESAS-r scores decreased between baseline and FUP1 and FUP2. This was further demonstrated by ANOVAs which revealed a significant effect of visit on mean ESAS-r scores for each symptom assessed (pain: F(2,634) = 4.9, p < 0.008; anxiety: F(2,624) = 8.36, p < 0.001, depression: F(2,629) = 5.36, p < 0.004; wellbeing: F(2,613) = 8.31, p < 0.001; all η 2 p between 0.008 and 0.02). In all assessed symptoms, no significant main effect of adding THC at FUP1, nor visit-by-product interaction, were observed (all p s > 0.2). Post hoc tests revealed ESAS-r mean scores significantly decreased between baseline and FUP1 (all ps < 0.003) for all symptoms, between baseline and FUP2 for anxiety and wellbeing (both ps < 0.03), but not between FUP1 and FUP2 for any symptoms (all ps > 0.5). This suggests statistical improvement recorded at FUP1 is still present at FUP2 in all symptoms independently from treatment adjustment at FUP1.

CBD treatment impact according to symptom severity

From Table 2 , moderate or severe scores at baseline were most common for pain (205 patients, 73.5%) and poor wellbeing (202 patients, 72.4%).

Clinical effect (difference of 1.3 to 2.5 points) observed in all symptoms for patients with moderate/severe symptoms between baseline and FUP1; however, there was no clinical effect for patients with mild symptoms (from − 0.3 to − 1.8) (Fig. 2 ). No clinical effect was observed in any symptoms between FUP1 and FUP2 for patients with moderate/severe symptoms (− 0.4 to 0.5) as well as for patients with mild symptoms (from − 0.7 to 0.4).

CBD-rich treatment effect according to symptom severity: mild or moderate/severe in 279 patients. FUP1, follow-up visit at 3 month; FUP2, follow-up visit at 6 month. a Mean ESAS-r scores for the pain symptom, b mean ESAS-r scores for the anxiety symptom, c mean ESAS-r scores for the depression symptom, and d mean ESAS-r scores for overall wellbeing. According to mixed ANOVAs, patients with moderate/severe symptoms reported symptom reduction whereas patients with mild symptoms reported symptom deterioration from baseline to FUP1. No effect was statistically significant between FUP1 and FUP2

The ANOVA revealed that all main and interaction effects were significant at the 0.001 level with effect sizes large for severity (η 2 p = 0.29), medium for visit (η 2 p = 0.06), and small for the interaction (η 2 p = 0.03). Post hoc tests revealed a significant score difference between baseline and FUP1 and FUP2 (both ps < 0.05) but not between FUP1 and FUP2 ( p = 0.98). Patients with moderate/severe symptoms on pain experienced important improvement at FUP1 (t(194) = 7.61, p < 0.001) whereas ESAS-r scores for patients with mild symptoms actually increased (t(64) = − 2.03, p < 0.05) (Fig. 2 a).

There were significant effects of visit, severity group, and visit by group interaction (all p s < 0.001; η 2 p = 0.006, η 2 p = 0.4, η 2 p = 0.1, respectively). Post hoc tests revealed a significant score difference between baseline and FUP1 and FUP2 (both p s < 0.001) but not between FUP1 and FUP2 ( p = 0.38). Although there was a large improvement for patients with moderate to severe anxiety symptoms (t(131) = 9.36, p < 0.001), the anxiety scores of patients with mild symptoms increased (t(119) = − 3.19, p < 0.01) from baseline to FUP1 (Fig. 2 b).

The ANOVA showed main effects of visit, severity group (both ps < 0.001 with η 2 p = 0.04 and η 2 p = 0.4, respectively) and a significant group-by-visit interaction (F(2,620) = 34.47, p < 0.001; η 2 p = 0.1). Post hoc tests revealed a significant score difference between baseline and FUP1 and FUP2 (both p s < 0.01) but not between FUP1 and FUP2 ( p = 0.85). Specifically, the scores of moderate/severe group decreased notably (t(110) = 9.56, p < 0.001) between baseline and FUP1 but the scores of the group with mild depression symptoms did not ( p = 0.07) (Fig. 2 c).

The ANOVA showed main effects of visit, severity group (both ps < 0.001 with η 2 p =0.04 and η 2 p =0.3 respectively) and a significant group-by-visit interaction (F(2,597) = 36.53, p < 0.001; η 2 p = 0.11). Post hoc tests revealed a significant main score difference between baseline and FUP1 and FUP2 (both p s < 0.01) but not between FUP1 and FUP2 ( p = 0.89). Precisely, the scores of the group reporting good wellbeing increased (t(182) = 8.8, p < 0.001) whereas scores of patients with worst wellbeing notably decreased (t(59) = − 5.08, p < 0.001) between FUP1 and FUP2 (Fig. 2 d).

This retrospective study explored the use of CBD-rich products in a medical cannabis clinical setting in Canada and associated effectiveness on a common symptom cluster presentation of pain, anxiety, depression, and poor sense of wellbeing, as measured by ESAS-r.

Patients treated with CBD-rich products were mainly women in their sixties, seeking predominantly chronic pain management.

Our findings show that overall effectiveness of CBD treatment is primarily by patients with moderate to severe symptoms. A deficiency in the endocannabinoid system (ECS) may provide a possible explanation for this result (Russo 2016 ). The ECS could be more deficient in patients with moderate/severe symptoms compared to mild symptoms leading to increased improvement in the first group. The absence of significant improvement for patients with mild symptoms at baseline may be explained by a smaller margin for symptom improvement. In such patients, CBD treatments may have been targeted to other clinical symptoms not assessed in the current study. There is a probable placebo effect; however, there were no differences in initial CBD doses between the severity groups. Furthermore, associated placebo effect would likely be decreased by FUP3M, also considering the significant treatment cost. The distinct beneficial impact of CBD treatment observed for patients with moderate-severe symptoms could elucidate discrepancies found in the literature.

RCTs on CBM and pain symptoms provide inconclusive results; however, several report that treatments of THC and CBD have some benefit for pain management (Häuser et al. 2018 ; Russo 2008 ; Prosk et al. 2020 ). Our results are largely novel as research on the effect of CBD on pain control is very limited (Boyaji et al. 2020 ). The reduction in reported anxiety may also contribute to the improvement in pain perception.

Discrepancies still exist regarding the anxiolytic effect of CBD. Some RCTs indicate an anxiolytic effect of CBD upon experimentally induced scenarios (Bergamaschi et al. 2011 ; Zuardi et al. 2017 ; Bhattacharyya et al. 2010 ; Skelley et al. 2020 ); however, these findings are difficult to replicate (Larsen and Shahinas 2020 ; Hundal et al. 2018 ; Crippa et al. 2012 ). This reinforces our findings that CBD may have a differential effect depending on anxiety severity. Regarding the effects of CBD on depression symptoms, further research is required to draw conclusions (Khalsa et al. 2020 ; Schier et al. 2014 ; Turna et al. 2017 ).

The addition of THC to CBD during FUP1 did not produce any effect on ESAS-r scores at FUP2 in this analysis; however, the magnitude of the difference between groups is small. The examination of treatment regimen has been seldom addressed in the literature and further development is required to inform guidelines for prescription and refinement of clinical practice.

Furthermore, a significant discrepancy is observed between the recorded dosages of oral CBD in RCTs and dosages in real-world settings. The average daily CBD dosage authorized at our clinic (11.5 mg) is closer to other observational studies (Gulbransen et al. 2020 ) compared to what is seen in RCTs (up to 1000 mg for a single dose) (Larsen and Shahinas 2020 ). The presence of THC and other cannabinoids in CBD-rich products may affect the outcomes in this study. The majority of RCTs investigated single-dose administration of CBD making it difficult to compare observed treatment outcomes with chronic dosing clinical settings. Importantly, medical cannabis products are generally not covered by most insurers and patients rely on out-of-pocket payments. The cost of CBD remains very high globally, approximately $CAD 5–20 per 100 mg (Canada Go 2021 ; Eisenstein 2019 ; Canada 2020 ). Availability of reliable cannabinoid testing in certain international jurisdictions is also limited. The gap between effective doses demonstrated in RCTs and the actual affordable doses demonstrated by RWE mandate the need for a precise pricing and marketing strategy at the initiation of any drug development process.

Limitations

Limitations are common in real-world data (RWD), especially in retrospective studies. In this study, with no control group, no causality effect can be drawn between CBD-rich treatment and symptom improvement. Most patients treated with CBM present with multiple severe symptoms and the analyses presented here are limited to identify the treatment outcomes for such patients. Further studies can investigate the use of CBD to treat several symptoms simultaneously.

The self-reported subjective assessment used may be biased by the patient’s positive expectation of treatment, which could lead to a possible placebo effect. This perceived effectiveness bias may also be increased by social and economic barriers. The current context of medical cannabis access, including social stigma, high cost, and lack of universal insurance coverage can increase the patient selection bias. Self-selection bias is increased by the significant patient interest in medical cannabis as these patients must be motivated to access the non-traditional medication system. This bias limits the generalizability of results but is common across international medical cannabis regimens and should not discount the observed results. The heterogeneity of the patient population with a variety of diagnoses and the diversity of medical cannabis preparations also affects the external validity of the study. However, clinical findings from within Canada’s controlled regulatory program do provide important models for international consideration. Future research is required in controlled clinical settings to examine these factors in order to provide a more complete account of CBD effectiveness.

Also, there was a large drop of sample size (53% loss) due to missing data. Additionally, there was an important loss to follow-up at the 6-month visit (FUP2) due to missed appointment and cost barriers, limiting the power of the findings. The total treatment cost has significant impact on treatment continuation. Improved patient retention and more robust, harmonized data collection methods will improve future observational studies and allow for long-term assessment. Collection of detailed, accurate product information is a challenge, especially with inhaled products (Corroon et al. 2020 ). There are opportunities for administration devices and other technology advancements to improve this limitation. Lastly, this study did not include safety data assessment, future studies should investigate safety considerations of CBD (Chesney et al. 2020 ). Collection of high-quality RWD will require improvements in patient retention, data monitoring, and more robust data collection methods within a controlled clinical setting.

This study on CBD-rich products demonstrates the potential of RWE for the advancement of medical cannabis research and practice guidelines, especially in a world where CBD use is exponentially increasing but scientific data are limited. It revealed that CBD-rich treatments have a beneficial impact on patients with self-reported moderate or severe symptoms of pain, anxiety, or depression and overall wellbeing but not in patients with mild symptoms. Further investigation is clearly required, but as of now the hyped, and often illegal, marketed claims of CBD as a wellness product are unsubstantiated. Our findings have important and novel implications to clinical practice, especially the examination of treatment plan adjustment during the first follow-up after initiation with CBD treatments. Improvements in access regimes, oversight, and clarification from regulatory agencies are also needed to improve the validity of RWE and assessment of the use of CBD-rich products.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Cannabidiol

Cannabinoid-based medicines

Electronic medical record

Edmonton Symptom Assessment System-revised version

Follow-up visit

Real-world data

Real-world evidence

Standard deviation

Δ9-Tetrahydrocannabinol

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Acknowledgements

The authors would like to thank the participants to this study. The authors would like to acknowledge Santé Cannabis co-founder Dr. Michael Dworkind and key clinical leaders Dr. Antonio Vigano, Dr. Howard Mitnick, Dr. Alain Watier, and Youri Drozd, clinical data assistant, for his contribution to data technical help.

This research was funded internally by Santé Cannabis clinic.

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Lucile Rapin, Rihab Gamaoun, Cynthia El Hage, Maria Fernanda Arboleda & Erin Prosk

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All authors contributed to conception and design, interpretation of data, manuscript writing, and final approval. LR and RG conducted the analysis of data. All authors agreed to be accountable for their own contributions. The author(s) read and approved the final manuscript.

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Correspondence to Lucile Rapin .

Ethics declarations

Ethics approval and consent to participate.

A waiver of consent was required and approved by Advarra Ethics Committee, who also approved the study protocol, and by the provincial privacy commission (La commission d’accès à l’information du Quebec).

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Not applicable.

Competing interests

L.R.: Clinical research associate, employee at Santé Cannabis.

R.G.: Epidemiologic and statistic consultant for Santé Cannabis.

C.EH.: Director of Research and Innovation, employee at Santé Cannabis.

MF.A.: Associate Research Director of Santé Cannabis.

E.P.: President and co-founder of Santé Cannabis.

Santé Cannabis is a medical clinic, research, and training center dedicated to medical cannabis. The views expressed are those of the authors. This is a retrospective, observational study which took place at Santé Cannabis; therefore, the design and conduct of the study was executed by Santé Cannabis clinic staff. C.EH. and E.P. had a supporting role, in the retrospective protocol development. The authors had no role in the conduct of the study and collection of data. None of the authors are involved in the care of patients or in treatment decisions. The authors acted independently, and Santé Cannabis had no role in the analysis of the study, nor the writing of the manuscript or decision to publish. There is no financial gain for Santé Cannabis or for the authors to publish. The authors, while connected to Santé Cannabis, do not have a financial or professional incentive for the decision to publish.

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Rapin, L., Gamaoun, R., El Hage, C. et al. Cannabidiol use and effectiveness: real-world evidence from a Canadian medical cannabis clinic. J Cannabis Res 3 , 19 (2021). https://doi.org/10.1186/s42238-021-00078-w

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Received : 15 January 2021

Accepted : 07 June 2021

Published : 23 June 2021

DOI : https://doi.org/10.1186/s42238-021-00078-w

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