Carillon J Skrzynski, Raeghan L Mueller, L Cinnamon Bidwell, Angela D Bryan, Kent E Hutchison
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The present study thus sought to compare cannabis use metrics and their associations with biomarkers and examine whether these associations were moderated by sex or age.</p><p><strong>Design: </strong>Observational study using data from five larger studies.</p><p><strong>Setting: </strong>Data collection and recruitment occurred in the greater Boulder/Denver metropolitan area in Colorado, USA.</p><p><strong>Participants: </strong>Individuals (n = 1090, M<sub>age</sub> = 32.89, SD = 12.97; 78.35% White; 51.56% female) included regular cannabis users (M<sub>use</sub> = 16 days/past month and 4 times/day).</p><p><strong>Measurements: </strong>Participants completed assessments of typical quantity and frequency of cannabis use via an in-house survey (Cannabis Quantity and Frequency Scale; CQFS) versus past month use via the Timeline Follow Back (TLFB). Cannabis biomarkers were also collected, including blood levels of delta-9 tetrahydrocannabinol (THC) after immediate use and baseline levels of the primary THC metabolite, 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH) FINDINGS: Separate mixed effects models using TLFB versus CQFS cannabis metric predictors of the two biomarkers including moderators of sex and age resulted in higher adjusted R<sup>2</sup> values for the CQFS versus TLFB model predicting THC-COOH (0.30 vs 0.27, respectively) and for the TLFB versus CQFS model predicting THC (0.24 vs 0.21, respectively). Additionally, greater CQFS total times of any cannabis/day (i.e. total times/day) was associated with increased THC-COOH [B = 5.85, 95% confidence interval (CI) = 0.66-11.03, P = 0.03] while it was associated with increased THC among males only (B<sub>interaction</sub> = 7.80, 95% CI = 0.25-15.34, P = 0.04). TLFB days/month was statistically significantly, positively related with both THC-COOH and THC (B = 2.39, 95% CI = 1.00-3.79, P < 0.001 and B = 4.18, 95% CI = 0.98-7.38, P = 0.01, respectively) with no statistically significant interactions.</p><p><strong>Conclusions: </strong>The Cannabis Quantity and Frequency Scale (CQFS) measurement of total times/day appears to be a better predictor of general cannabis use than the Timeline Follow Back (TLFB) method. In contrast, TLFB appears to be better at predicting acute use of cannabis than the CQFS.</p>","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cannabis use measurement: Identifying the optimal metric for broad research applications.\",\"authors\":\"Carillon J Skrzynski, Raeghan L Mueller, L Cinnamon Bidwell, Angela D Bryan, Kent E Hutchison\",\"doi\":\"10.1111/add.70205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and aims: </strong>Cannabis legalization has increased product availability and use, subsequently necessitating efficient measurements of cannabis use that accurately reflect biomarkers of cannabis exposure (e.g. blood cannabinoid levels). The present study thus sought to compare cannabis use metrics and their associations with biomarkers and examine whether these associations were moderated by sex or age.</p><p><strong>Design: </strong>Observational study using data from five larger studies.</p><p><strong>Setting: </strong>Data collection and recruitment occurred in the greater Boulder/Denver metropolitan area in Colorado, USA.</p><p><strong>Participants: </strong>Individuals (n = 1090, M<sub>age</sub> = 32.89, SD = 12.97; 78.35% White; 51.56% female) included regular cannabis users (M<sub>use</sub> = 16 days/past month and 4 times/day).</p><p><strong>Measurements: </strong>Participants completed assessments of typical quantity and frequency of cannabis use via an in-house survey (Cannabis Quantity and Frequency Scale; CQFS) versus past month use via the Timeline Follow Back (TLFB). Cannabis biomarkers were also collected, including blood levels of delta-9 tetrahydrocannabinol (THC) after immediate use and baseline levels of the primary THC metabolite, 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH) FINDINGS: Separate mixed effects models using TLFB versus CQFS cannabis metric predictors of the two biomarkers including moderators of sex and age resulted in higher adjusted R<sup>2</sup> values for the CQFS versus TLFB model predicting THC-COOH (0.30 vs 0.27, respectively) and for the TLFB versus CQFS model predicting THC (0.24 vs 0.21, respectively). Additionally, greater CQFS total times of any cannabis/day (i.e. total times/day) was associated with increased THC-COOH [B = 5.85, 95% confidence interval (CI) = 0.66-11.03, P = 0.03] while it was associated with increased THC among males only (B<sub>interaction</sub> = 7.80, 95% CI = 0.25-15.34, P = 0.04). 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引用次数: 0
摘要
背景和目的：大麻合法化增加了产品的供应和使用，因此有必要对大麻使用情况进行有效测量，以准确反映大麻暴露的生物标志物（例如血液大麻素水平）。因此，本研究试图比较大麻使用指标及其与生物标志物的关联，并检查这些关联是否受性别或年龄的影响。设计：观察性研究，使用来自五个大型研究的数据。环境：数据收集和招聘发生在美国科罗拉多州的大博尔德/丹佛大都市区。参与者：个体（n = 1090, Mage = 32.89, SD = 12.97； 78.35%白人；51.56%女性）包括常规大麻使用者（Muse = 16天/过去一个月和4次/天）。测量：参与者通过内部调查（大麻数量和频率量表；CQFS）完成了对大麻使用的典型数量和频率的评估，通过时间轴跟踪（TLFB）对比过去一个月的使用情况。还收集了大麻生物标志物，包括立即使用后血液中δ -9四氢大麻酚（THC）的水平和THC主要代谢物- 11-不-9-羧基四氢大麻酚（THC- cooh）的基线水平。使用TLFB和CQFS两种生物标志物（包括性别和年龄调节因子）的大麻计量预测因子的单独混合效应模型导致CQFS和TLFB模型预测THC- cooh的调整R2值更高（分别为0.30和0.27），TLFB和CQFS模型预测THC的调整R2值更高（分别为0.24和0.21）。此外，任何大麻的CQFS总次数/天（即总次数/天）与THC- cooh的增加有关[B = 5.85, 95%可信区间(CI) = 0.66-11.03, P = 0.03]，而仅与男性THC的增加有关（B交互作用= 7.80,95% CI = 0.25-15.34, P = 0.04）。TLFB天数/月与THC- cooh和THC呈正相关（B = 2.39, 95% CI = 1.00-3.79， P）结论：大麻数量和频率量表（CQFS）测量的总次数/天似乎比时间线追踪法（TLFB）更能预测一般大麻使用情况。相比之下，TLFB似乎比CQFS更能预测大麻的急性使用。
Cannabis use measurement: Identifying the optimal metric for broad research applications.
Background and aims: Cannabis legalization has increased product availability and use, subsequently necessitating efficient measurements of cannabis use that accurately reflect biomarkers of cannabis exposure (e.g. blood cannabinoid levels). The present study thus sought to compare cannabis use metrics and their associations with biomarkers and examine whether these associations were moderated by sex or age.
Design: Observational study using data from five larger studies.
Setting: Data collection and recruitment occurred in the greater Boulder/Denver metropolitan area in Colorado, USA.
Measurements: Participants completed assessments of typical quantity and frequency of cannabis use via an in-house survey (Cannabis Quantity and Frequency Scale; CQFS) versus past month use via the Timeline Follow Back (TLFB). Cannabis biomarkers were also collected, including blood levels of delta-9 tetrahydrocannabinol (THC) after immediate use and baseline levels of the primary THC metabolite, 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH) FINDINGS: Separate mixed effects models using TLFB versus CQFS cannabis metric predictors of the two biomarkers including moderators of sex and age resulted in higher adjusted R2 values for the CQFS versus TLFB model predicting THC-COOH (0.30 vs 0.27, respectively) and for the TLFB versus CQFS model predicting THC (0.24 vs 0.21, respectively). Additionally, greater CQFS total times of any cannabis/day (i.e. total times/day) was associated with increased THC-COOH [B = 5.85, 95% confidence interval (CI) = 0.66-11.03, P = 0.03] while it was associated with increased THC among males only (Binteraction = 7.80, 95% CI = 0.25-15.34, P = 0.04). TLFB days/month was statistically significantly, positively related with both THC-COOH and THC (B = 2.39, 95% CI = 1.00-3.79, P < 0.001 and B = 4.18, 95% CI = 0.98-7.38, P = 0.01, respectively) with no statistically significant interactions.
Conclusions: The Cannabis Quantity and Frequency Scale (CQFS) measurement of total times/day appears to be a better predictor of general cannabis use than the Timeline Follow Back (TLFB) method. In contrast, TLFB appears to be better at predicting acute use of cannabis than the CQFS.
期刊介绍：
Addiction publishes peer-reviewed research reports on pharmacological and behavioural addictions, bringing together research conducted within many different disciplines.
Its goal is to serve international and interdisciplinary scientific and clinical communication, to strengthen links between science and policy, and to stimulate and enhance the quality of debate. We seek submissions that are not only technically competent but are also original and contain information or ideas of fresh interest to our international readership. We seek to serve low- and middle-income (LAMI) countries as well as more economically developed countries.
Addiction’s scope spans human experimental, epidemiological, social science, historical, clinical and policy research relating to addiction, primarily but not exclusively in the areas of psychoactive substance use and/or gambling. In addition to original research, the journal features editorials, commentaries, reviews, letters, and book reviews.
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