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Cannabis Abuse

Cannabis Abuse refers to the problematic or harmful use of cannabis, also known as marijuana.
This condition encompasses the misuse, overuse, or dependence on cannabis products, which can lead to adverse physical, mental, and social consequences.
The description of this term aims to provide a concise, informative overview to assist researchers in optimizing their studies related to cannabis abuse and ensuring reliable, reproducible results.
Pubcompare.ai is an innovative tool that can enhance this research process by helping researchers locate protocols from literature, preprints, and patents using AI-driven comparisons to identify the best protocols and products, streamlining the research and ensuring accurate findings.

Most cited protocols related to «Cannabis Abuse»

1
Lifetime Cannabis Use Disorder Identification
Cited 19 times

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Publication 2020
Cannabis Cannabis Abuse Cannabis Dependence Diagnosis
2
Cannabis Use Disorder Assessment Protocol
Cited 13 times
Cannabis use quantity was assessed with a single item in each sample. Participants in Sample 1 were asked, “How much cannabis (grams) do you usually use per week?” (1=1 g, 2=2 g, 3=3–5 g, 4=6–8 g, 5=9–12 g, 6=more than 12 g), whereas participants in Sample 2 were asked, “On a typical day when you use cannabis, on average, how many cones, bongs, or joints do you normally have?”
The Cannabis Use Disorder Identification Test-Revised (CUDIT-R16 (link)) is an eight-item self-report questionnaire that assesses problematic cannabis use within the past 6 months (Appendix 1). Items assess consumption frequency, time spent stoned, cannabis abuse (e.g., use in hazardous situations) and cannabis dependence (e.g., not able to stop using, spending a lot of time obtaining, using, or recovering from use), negative consequences of use (e.g., problems with memory and concentration), and intention to cut down or stop use. Scores can range from 1 to 32, with a cut-off score of 13 indicative of a DSM-IV diagnosis of CUD (dependence).16 (link) Criteria for DSM-IV cannabis abuse are met if one or more of four symptoms are endorsed, and for dependence if three or more of seven symptoms are endorsed.
For Sample 1, past-6 month DSM-5 CUD was assessed via a self-report questionnaire derived from the Structured Clinical Interview, Non-Patient Version for DSM-IV (SCID-I-N/P26 ). Consistent with changes for DSM-5 criteria, a positive diagnosis of CUD could have also included withdrawal.19 (link) Though DSM-5 CUD scoring rules were utilized, assessment of CUD in this study did not include craving due to the timing of the study, thus we refer to these modified DSM-5 criteria (i.e., without craving) from here forward as DSM-5-M.
For Sample 2, the presence of past 6 month CUD was established by using an amended version of the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-IV27 (link)) supplemented with questions from the Comprehensive International Diagnostic Interview (CIDI28 (link)) to assess both the presence of craving and, independently, the presence of each of the seven signs and symptoms of cannabis withdrawal relevant to DSM-5, using the withdrawal criteria as specified in the DSM-5 (including withdrawal relief). Consistent with DSM-5 scoring rules, participants in both samples met criteria for CUD if they endorsed two or more symptoms (10 symptoms assessed in Sample 1; 11 symptoms in Sample 2).
Bonn-Miller M.O., Heinz A.J., Smith E.V., Bruno R, & Adamson S. (2016). Preliminary Development of a Brief Cannabis Use Disorder Screening Tool: The Cannabis Use Disorder Identification Test Short-Form. Cannabis and Cannabinoid Research, 1(1), 252-261.
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Publication 2016
Alcohol Use Disorder Cannabis Cannabis Abuse Cannabis Dependence Diagnosis Disabled Persons Joints Memory Deficits Patients Retinal Cone SCID Mice Withdrawal Symptoms
3
Comorbidity of Depression and Anxiety Disorders
Cited 13 times
The sample consisted of 28 individuals with current PD (i.e., PD only), 40 individuals with current MDD (i.e., MDD only), 58 individuals with current PD and current MDD (i.e., comorbids), and 65 controls (N = 191). All diagnoses were made via the Structured Clinical Interview for DSM–IV (SCID; First, Spitzer, Gibbon, & Williams, 1996 ). SCIDs were conducted by the first author and advanced clinical psychology doctoral students. Diagnosticians were trained to criterion by viewing the SCID-101 training videos (Biometrics Research Department, New York, NY), observing two or three joint SCID interviews with the first author, and completing three SCID interviews (observed by the first author or an advanced interviewer) in which diagnoses were in agreement with the observer. Twenty SCIDs were audio recorded and scored by a second rater blind to original diagnoses to determine reliability of diagnoses. Interrater reliability indicated perfect agreement for PD and MDD diagnoses (kappas = 1.00).
Both depressed groups were required to have an age of onset of first affective disorder (dysthymia or MDD) before 18 years as Shankman et al. (2007) (link) found that it was only those with an early onset depression who exhibited an abnormal frontal EEG asymmetry during the slot task. Participants in the MDD-only group were required to have no current or past history of an anxiety disorder (PD, social phobia, etc.). Participants in the PD-only and comorbid groups were allowed to meet criteria for additional current and past anxiety disorders. PD-only participants also met criteria for social phobia (n = 2), specific phobia (n = 6), PTSD (n = 3), GAD (n = 7), and obsessive– compulsive disorder (n = 1). Comorbid participants also met criteria for social phobia (n = 20), specific phobia (n = 11), PTSD (n = 18), GAD (n = 1), and obsessive– compulsive disorder (n = 9). Comorbid (63.8%) and PD-only (46.4%) participants did not differ in the rate of other lifetime anxiety disorders, χ2(1, N = 86) = 2.34, ns. Control participants were not allowed to have a lifetime diagnosis of Axis I psychopathology, with the exception of a past diagnosis of alcohol or cannabis abuse (but not dependence and not current; n = 4). Control participants were also required to have scores of less than 8 on both the 24-item Hamilton Rating Scale for Depression (HRSD; Hamilton, 1960 (link)) and Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988 (link)).
Participants were excluded from the study if they had a lifetime diagnosis of a psychotic disorder, bipolar disorder, or dementia; were unable to read or write English; had a history of head trauma with loss of consciousness; or were left-handed (as confirmed by the Edinburgh Handedness Inventory; range of laterality quotient: +20 to +100; Oldfield, 1971 (link)). Participants were recruited from the community (via fliers, Internet postings, etc.) and area mental health clinics. All procedures were approved by University of Illinois–Chicago Institutional Review Board.
Shankman S.A., Nelson B.D., Sarapas C., Robison-Andrew E.J., Campbell M.L., Altman S.E., McGowan S.K., Katz A.C, & Gorka S.M. (2012). A Psychophysiological Investigation of Threat and Reward Sensitivity in Individuals With Panic Disorder and/or Major Depressive Disorder. Journal of abnormal psychology, 122(2), 322-338.
Publication 2012
Anxiety Disorders Bipolar Disorder Cannabis Abuse Conditioning, Psychology Craniocerebral Trauma Dementia Diagnosis Dysthymic Disorder Epistropheus Ethanol Ethics Committees, Research Gibbons Interviewers Joints Mental Health Mood Disorders Obsessive-Compulsive Disorder Phobia, Social Phobia, Specific Physicians Post-Traumatic Stress Disorder Psychotic Disorders SCID Mice Severe Combined Immunodeficiency Student Visually Impaired Persons
4
Schizophrenia Diagnostic Validation Protocol
Cited 12 times

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Publication 2012
Abuse, Alcohol Abuse, Opioid Alcoholic Intoxication, Chronic Cannabis Abuse Cannabis Dependence Cocaine Abuse Cocaine Dependence Craniocerebral Trauma Diagnosis Drug Abuse Dysthymic Disorder Epistropheus Ethics Committees, Research Hallucinogens Nervous System Disorder Opiate Addiction Panic Disorder Pharmaceutical Preparations Phobia, Social Phobia, Specific Post-Traumatic Stress Disorder Schizoaffective Disorder Schizophrenia SCID Mice Substance Abuse
5
Comprehensive Substance Use Assessment Protocol
Cited 8 times
All participants completed a detailed semi-structured interview that incorporated methods of the time-line followback procedure and assessed patterns of substance use for 13 different substance classes, similar to methods employed in other studies (e.g., Gonzalez, 2004; Rippeth et al., 2004 (link)). For each substance queried, participants were asked about frequency and quantity of use across various epochs in their lifetime to arrive at estimates of cumulative lifetime use, as well as amount and frequency of use in the 12 months and in the 30 days prior to their evaluation. The substance use module of the Structured Clinical Interview for DSM-IV (SCID) was administered to diagnose the presence of alcohol and substance use disorders during participants’ lifetime and in the 30 days prior to their evaluation (First et al., 2002 ). We assessed for the presence and severity of symptoms associated with cannabis addiction with the Marijuana Severity Index (MSI; Alexander, 2003 (link)): a 31 yes/no forced-choice questionnaire on problematic patterns of cannabis use that a participant has “ever” experienced from cannabis use. We also quantified severity of cannabis addiction by tabulating the total number of current DSM-IV symptoms of cannabis abuse and dependence endorsed by a participant in the 30 days prior to their evaluation (DSM-IV CUD symptoms).
Gonzalez R., Schuster R.M., Mermelstein R.J., Vassileva J., Martin E.M, & Diviak K.R. (2012). Performance of Young Adult Cannabis Users on Neurocognitive Measures of Impulsive Behavior and their Relationship to Symptoms of Cannabis Use Disorders. Journal of clinical and experimental neuropsychology, 34(9), 962-976.
Publication 2012
Addictive Behavior Cannabis Cannabis Abuse Cannabis sativa Diagnosis EPOCH protocol Ethanol Substance Use Substance Use Disorders TimeLine

Most recents protocols related to «Cannabis Abuse»

1
Marathon Runners' Cognition & Plasticity
We included 51 marathon runners (of the initial 100 participants) of the longitudinal observational ReCaP-Study [Running effects on cognition and plasticity; details published previously (Roeh et al., 2019 (link))] in our analyses. Participants were aged between 18 to 60 years, successfully registered for the Munich Marathon 2017, completed at least one half-marathon prior to the event, had sufficient German language skills and provided written informed consent. Participants with relevant neurological, cardiac or psychiatric diseases, pregnancy, cannabis abuse, and BMI >30 were excluded.
Schoenfeld J., Roeh A., Holdenrieder S., von Korn P., Haller B., Krueger K., Falkai P., Halle M., Hasan A, & Scherr J. (2023). High-mobility group box 1 protein, receptor for advanced glycation end products and nucleosomes increases after marathon. Frontiers in Physiology, 14, 1118127.
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Publication 2023
Cannabis Abuse Cognition Heart Marathon composite resin Mental Disorders Pregnancy
2
Binge Drinking Among University Students
A sample of 70 first-year students (18–19 years old) from the University of Santiago de Compostela (Spain) participated in this study, which forms part of a broader research project on binge drinking among university students and has been approved by the Bioethics Committee of the University. Initially, 1,328 volunteers participating in the epidemiological phase of the research completed a classroom-administered questionnaire composed of the Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993 (link)), with additional questions regarding consumption of alcohol and other substances (illegal drugs and medications) and also sociodemographic information for epidemiological purposes (such as type of household or parents’ level of education).
The students were screened on the basis of their responses to the questionnaire, and 200 of the respondents, who fulfilled the initial selection criteria, and who also provided contact information, were interviewed. These students signed informed consent and received compensation (10 euros) for their participation. The semi-structured interview included the Mini International Neuropsychiatric Interview, Spanish version 5.0.0 (Ferrando et al., 2000 ), and the Symptom Checklist-90-R (SCL-90-R) (Derogatis, 1983 ) for assessing history or current psychopathological symptomatology. It also included a detailed questionnaire about substances use, based on the Cannabis Abuse Screening Test (CAST) (Cuenca-Royo et al., 2012 (link)), the Nicotine Dependence Syndrome Scale, short version (NDSS-S) (Shiffman et al., 2004 (link)) and a diary of alcohol consumption based on the revised version of the Alcohol Use Questionnaire proposed by Townshed and Ducka (Townshend and Duka, 2002 (link)).
Selection for neurocognitive assessment was based primarily on the drinking pattern. Subjects with an alcohol consumption of six or more drinks per episode at least once a month (AUDIT item 3) and a drinking rate of at least three drinks per hour in these episodes were selected as binge drinkers; this criterion was defined to approximate the level of consumption to the NIAAA definition of binge drinking (National Institute on Alcohol Abuse and Alcoholism, 2016 ). Subjects were included in the control group if they never partook in 6-drink episodes and never drank more than two drinks per hour. Inclusion and exclusion criteria, applied on the basis of information obtained in the interview, are summarised in Table 1.
Seventy-four subjects (100% Caucasian) who fulfilled these criteria consented to take part in the neurocognitive assessment. These participants received 20 euros for their collaboration. Four of the participants were later excluded because of the low quality of the EEG recording. Of the final 70 subjects, 32 (17 females) were included in the binge-drinking (BD) group and 38 (18 females) in the control (CN) group. Demographics and substance use characteristics are summarised in Table 2.
Rodríguez Holguín S., Folgueira-Ares R., Crego A., López-Caneda E., Corral M., Cadaveira F, & Doallo S. (2023). Neurocognitive effects of binge drinking on verbal episodic memory. An ERP study in university students. Frontiers in Pharmacology, 14, 1034248.
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Publication 2023
Alcohol Use Disorder Cannabis Abuse Caucasoid Races Females Hispanic or Latino Households Illicit Drugs Nicotine Dependence Parent Pharmaceutical Preparations Student Substance Use Syndrome Voluntary Workers
3
Substance Use Assessment in Assault Survivors
Tobacco, alcohol, delta-9-tetrahydrocannabinol, and benzodiazepine use since the assault is assessed at each follow-up (6 weeks, 3 months, 6 months, and 1 year) by inquiring whether participants use each substance and whether their consumption has increased or decreased since the assault [53 ]. If participants report using these substances, follow-up questionnaires are administered to assess whether their use of each substance is problematic. This assessment is performed using the 2-item simplified Fagerström test [54 ], a French version of the Heaviness of Smoking Index (HSI) [55 (link)], the French version of the Cut Annoyed Guilty Eye-Opener (CAGE) test (Diminuer Entourage Trop Alcool; DETA) [56 (link)], the French version of the Cannabis Abuse Screening Test (CAST) [57 (link)], and the Echelle Cognitive d’Attachement au Benzodiazépine (Benzodiazepine Cognitive Attachment Scale; ECAB) [58 ]. All of these instruments are validated and recommended by the French High Authority for Health.
Fedele E., Trousset V., Schalk T., Oliero J., Fovet T, & Lefevre T. (2023). Identification of Psycho-Socio-Judicial Trajectories and Factors Associated With Posttraumatic Stress Disorder in People Over 15 Years of Age Who Recently Reported Sexual Assault to a Forensic Medical Center: Protocol for a Multicentric Prospective Study Using Mixed Methods and Artificial Intelligence. JMIR Research Protocols, 12, e46652.
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Publication 2023
Benzodiazepines Cannabis Abuse Cognition DEET Dronabinol Ethanol Guilt Substance Use Tobacco Products
4
Ketamine for Treatment-Resistant Depression
Participants were recruited on an ongoing basis from the Douglas Ketamine service between November 2021 and May 2022. As is common in Montreal, participants were either primary French or English speaking. Inclusion criteria for the study were: 1) age >18, <75 years old; 2) received at least one ketamine infusion at the ketamine service for an episode of unipolar or bipolar depression diagnosed by a trained psychiatrist (according to DSM-5), which had not responded to at least two adequate trials of psychotropic drugs with level 1 evidence against bipolar and/or unipolar depression; 3) at least one long-term (>6 month) active BZDR prescription at the time of the first ketamine psychiatric evaluation; 4) no medication changes 2-weeks before and during treatment (except for BZDR reduction); and 5) provision of written informed consent. Otherwise, no exclusion criteria were utilized for this study, though all eligible patients had been accepted for ketamine treatments and thus met our service’s criteria, provided in the supplement information. Two noteworthy exclusion criteria are: current or recent history (i.e., in the past 12 months) of alcohol or cannabis abuse or dependence, and current or lifetime history of substance abuse or dependence (including all substances except for caffeine or nicotine), as defined by DSM-5 criteria [30 ].
A chronological, retrospective chart review of all patients of the ketamine-TRD service identified eligible patients who were initially contacted by telephone (by a research assistant) to introduce the study and to seek informed consent. Consenting patients were enrolled into the study’s prospective long-term follow-up phase and BZDR use-patterns were evaluated at multiple timepoints as detailed below.
Garel N., Greenway K.T., Dinh-Williams L.A., Thibault-Levesque J., Jutras-Aswad D., Turecki G., Rej S, & Richard-Devantoy S. (2023). Intravenous ketamine for benzodiazepine deprescription and withdrawal management in treatment-resistant depression: a preliminary report. Neuropsychopharmacology, 48(12), 1769-1777.
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Publication 2023
Caffeine Cannabis Abuse Depression, Bipolar Ethanol Ketamine Nicotine Patients Pharmaceutical Preparations Psychiatrist Psychotropic Drugs Substance Abuse Unipolar Depression
5
Genetics of Bipolar Disorder and Cannabis Use
The Collaborative Study on the Genetics of Alcoholism (COGA) is a family pedigree investigation which enrolled treatment-seeking alcohol-dependent probands who initially met the DSM-IV for alcohol dependence [33 ]. Six medical centers in the USA recruited the initial probands plus first-degree family members. The only exclusion criteria include life-threatening medical disorders, repeated intravenous drug use, and an inability to speak English. Written informed consent to participate in the study was obtained from all subjects. Participants and their relatives were interviewed at baseline using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which focuses on demography, substance use patterns, and the assessment of 17 axis I DSM-IV diagnoses, as well as characteristics of bipolar disorder [16 (link),34 ].
While the SSAGA was developed prior to the publication of the DSM-IV criteria, all criteria symptoms for the DSM-IV diagnosis were assessed ages of onset and remission of symptoms [35 (link)]. Only the original probands or comparison subjects, their first-degree relatives, and offspring aged ≤20 years in the participating families were eligible for follow-up. Of all eligible subjects, the follow-up rate was 60% in probands, 65% in family members, and 78% in controls [35 (link)].
The interview also assessed past episodes of affective disorders, including depressive and manic episodes and the characteristics of the most severe episode. To receive a DSM-IV bipolar I disorder diagnosis, subjects had to report a lifetime diagnosis of both major depression and mania or any lifetime diagnosis of a manic episode. Individuals who had at least one major depression and hypomanic episode were considered to have bipolar II disorder.
N = 180 subjects with bipolar I or II disorder were identified. Of these n = 65 (36.1%) had an additional diagnosis of DSM IV CUD (cannabis dependence and cannabis abuse, CUD in 23 of 77 (29.8%) individuals, in bipolar II subjects and 40.8% (42 of 103 individuals in bipolar I subjects). Any CUDs (dependence and abuse) was found in 36.1% of bipolar I and II individuals.
Subjects with a bipolar II disorder without comorbid CUD but abstinent or with social CU were included into group 1 (n = 54) while group 2 (n = 23) consists of individuals with comorbid bipolar II and CUD diagnoses. Group 3 included subjects with a bipolar I diagnosis without CUD (n = 61) but either abstinence or social CU and group 4 were bipolar I subjects with a comorbid CUD (n = 42).
The probands and appropriate relatives were re-assessed at a mean of 5.72 years (±1.1 years) after the initial interview.
Preuss U.W., Hesselbrock M.N, & Hesselbrock V.M. (2023). A Prospective Comparison of Bipolar I and II Subjects with and without Comorbid Cannabis Use Disorders from the COGA Dataset. Brain Sciences, 13(8), 1130.
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Publication 2023
Alcoholic Intoxication, Chronic Bipolar Disorder Bipolar Disorder Type 2 Cannabis Abuse Cannabis Dependence Diagnosis Drug Abuse Epistropheus Ethanol Family Member Hypomanic Episode Major Depressive Disorder Mania Manic Episode Mood Disorders Pharmaceutical Preparations Substance Use

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More about "Cannabis Abuse"

Cannabis Abuse, also known as Marijuana Abuse, refers to the problematic or harmful use of cannabis products.
This condition encompasses the misuse, overuse, or dependence on cannabis, which can lead to adverse physical, mental, and social consequences.
It is a complex issue that has been studied extensively using various statistical software tools, such as SPSS (Statistical Package for the Social Sciences) version 20 and 16.0, Stata/SE version 15.1, and SPSS version 21.
These software packages have been utilized to analyze data and understand the factors contributing to cannabis abuse, as well as the impact on individuals and society.
The research process can be enhanced by innovative tools like PubCompare.ai, which helps researchers locate protocols from literature, preprints, and patents using AI-driven comparisons.
This streamlines the research process and ensures accurate, reproducible findings.
By identifying the best protocols and products, researchers can optimize their studies related to cannabis abuse and gain reliable insights.
Understanding the nuances of cannabis abuse is crucial, as it encompasses a range of related terms and subtopics, such as cannabis dependence, cannabis withdrawal, and cannabis use disorder.
Additionally, the use of cannabis can be associated with various health consequences, including respiratory issues, cardiovascular problems, and mental health concerns.
Researchers must consider these interconnected aspects to develop effective prevention and treatment strategies.
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