Abstract Background and Aims Questions over the clinical significance of cannabis withdrawal have hindered its inclusion as a discrete cannabis induced psychiatric condition in the Diagnostic and Statistical Manual of Mental Disorders (DSM IV). This study aims to quantify functional impairment to normal daily activities from cannabis withdrawal, and looks at the factors predicting functional impairment. In addition the study tests the influence of functional impairment from cannabis withdrawal on cannabis use during and after an abstinence attempt. Methods and Results A volunteer sample of 49 non-treatment seeking cannabis users who met DSM-IV criteria for dependence provided daily withdrawal-related functional impairment scores during a one-week baseline phase and two weeks of monitored abstinence from cannabis with a one month follow up. Functional impairment from withdrawal symptoms was strongly associated with symptom severity (p = 0.0001). Participants with more severe cannabis dependence before the abstinence attempt reported greater functional impairment from cannabis withdrawal (p = 0.03). Relapse to cannabis use during the abstinence period was associated with greater functional impairment from a subset of withdrawal symptoms in high dependence users. Higher levels of functional impairment during the abstinence attempt predicted higher levels of cannabis use at one month follow up (p = 0.001). Conclusions Cannabis withdrawal is clinically significant because it is associated with functional impairment to normal daily activities, as well as relapse to cannabis use. Sample size in the relapse group was small and the use of a non-treatment seeking population requires findings to be replicated in clinical samples. Tailoring treatments to target withdrawal symptoms contributing to functional impairment during a quit attempt may improve treatment outcomes.

Citation: Allsop DJ, Copeland J, Norberg MM, Fu S, Molnar A, Lewis J, et al. (2012) Quantifying the Clinical Significance of Cannabis Withdrawal. PLoS ONE 7(9): e44864. https://doi.org/10.1371/journal.pone.0044864 Editor: Antonio Verdejo García, University of Granada, Spain Received: March 22, 2012; Accepted: August 9, 2012; Published: September 26, 2012 Copyright: © Allsop et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Dr. Allsop is supported by a project grant (1006036) from the National Health and Medical Research Council, Australia. Funding for the work was provided by the Australian Government Department of Health and Ageing. Dr. Budney's contribution was funded in part by the National Institute on Drug Abuse, DA15186 and DA23526. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Professor Alan Budney has provided consultation to GW Pharmaceuticals. Dr. Allsop, Professor Copeland, and Dr. Norberg are currently carrying out an investigator driven clinical trial using materials donated by GW Pharmaceuticals. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. All other authors report no competing interest exists.

Introduction The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) requires that a mental health diagnosis “..causes clinically significant distress or impairment in social, occupational, or other important areas of functioning” ([1], p.358) in order to reduce false positive diagnoses (i.e. incorrectly labelling somebody with a mental health disorder). For DSM-IV drug dependence, at least three of the following seven diagnostic markers must cause clinically significant functional impairment ([1], p.181–183): 1. tolerance to the substance, 2. consumption in larger amounts or for longer periods than intended, 3. a persistent desire or unsuccessful attempts to cut down, 4. a great deal of time spent obtaining, using or recovering from the substance, 5. important activities are given up or reduced because of the substance, 6. substance use is continued despite the knowledge that it causes problems, 7. the presence of characteristic withdrawal symptoms or use of substance to alleviate withdrawal. Cannabis however, unlike other drugs, does not currently include the seventh criterion of withdrawal for diagnosing a cannabis use disorder in the DSM-IV. This is due to debate about the clinical significance of the cannabis withdrawal syndrome. The evidence-base for cannabis withdrawal [2], [3], [4], [5], [6], [7] has led to a proposal to include it in the DSM-5 (see and [8], [9]), which could increase the prevalence of cannabis dependence diagnoses in the community [10]. Increases in the prevalence of any mental health disorder can have ramifications for treatment service provision, highlighting the importance of ensuring that cannabis withdrawal is clinically significant. To address this, a valid and reliable Cannabis Withdrawal Scale (CWS) is in the early phases of development, and the initial study validated the CWS via self-ratings of the intensity of withdrawal symptoms during cannabis abstinence [11]. While measurement of symptom intensity per se is a central tenet of clinical scales of alcohol and other drug withdrawal to date [12], [13], [14], [15], [16], intensity measures do not necessarily capture the clinical significance associated with each symptom or with the syndrome as a whole. In addition to measuring the intensity of withdrawal symptoms, a more direct method to assess their clinical significance would draw on the DSM definition, and explicitly quantify how much symptoms impair normal daily functioning such as required for work, family life, and social functioning. Research attempting to demonstrate the clinical significance of cannabis withdrawal has used two approaches: (a) linking withdrawal intensity to distress and/or substance use [5], [6], [17], [18], [19], [20], [21], [22], and (b) demonstrating that cannabis withdrawal is of a similar magnitude and has similar consequences to nicotine withdrawal, a well accepted clinically valid syndrome [20], [23], [24]. In regards to linking withdrawal symptoms to cannabis use, two retrospective studies showed that craving was the most highly endorsed withdrawal symptom by people who relapsed, followed by irritability, anger and boredom [4], [5]. However the use of only relapse as a measure of clinical significance may mask the extent to which symptoms led to functional impairment, as those who maintained abstinence may still have experienced clinically significant negative consequences from cannabis withdrawal (e.g. relationship or work problems resulting from the withdrawal syndrome). Two studies have looked at the clinical significance of individual cannabis withdrawal symptoms using Likert scales to tease apart variation in the level of functional impairment. In a retrospective survey of adults who made a recent quit attempt, Budney et al. (2008) [20] used a 10-point Likert scale to show that the intensity of aggression, anger, anxiety, cravings, and depression symptoms contributed to cannabis relapse. Allsop and colleagues [11] used a 10-point Likert scale in a prospective study using a nonclinical outpatient population to measure withdrawal symptom intensity as well as the functional impairment caused by each symptom. The items causing the most impairment to normal daily functioning were: trouble getting to sleep, angry outbursts, imagining being stoned (cravings), loss of appetite, feeling easily irritated, and nightmares or strange dreams. The present study extends that work by exploring whether the functional impairment reported during abstinence is clinically significant, and what factors predict it. This study tested in a non clinical sample of non-treatment seekers, (1) whether the level of functional impairment during abstinence is predicted by severity of dependence, or pre-quit attempt cannabis use levels, whilst controlling for age and gender, and (2) what the relationship is between the intensity of cannabis withdrawal symptoms and the level of associated functional impairment. In addition the study had the following exploratory aims: (a) to test the hypothesis that relapse to cannabis use is associated with greater levels of functional impairment from cannabis withdrawal symptoms, (b) to test the hypothesis that greater functional impairment during the abstinence attempt is predictive of a greater amount of cannabis consumed during a one month follow-up period, and (c) to test what factors predict time to relapse.

Methods Participants Current cannabis users who were not seeking treatment for their cannabis use were recruited from Sydney, Australia using a targeted postcard campaign (http://www.webcitation.org/69yO6gGfy) and advertisements in local newspapers asking for people who were prepared to abstain from cannabis for a two-week period for research purposes. Inclusion criteria included: (a) cannabis use on five or more days per week over the previous three months; (b) current cannabis dependence; (c) previous experience of at least one cannabis withdrawal symptom; and (d) willingness to quit cannabis for two weeks. Exclusion criteria included: (a) moderate or severe dependence on other substances except caffeine and nicotine; (b) substance-related treatment in the last three months; and (c) pregnancy or planning on becoming pregnant during the study. After a complete description of the study to the participants, written informed consent was obtained. Measurements A phone screening interview was used to collect demographics, cannabis dependence severity using the Severity of Dependence Scale (SDS) [25], [26], and hazardous alcohol consumption using the Alcohol Use Disorders Identification Test (AUDIT) [27]. The SDS contains five items and uses a four-point response scale and is reported to have high internal consistency (Chronbach's alpha = 0.83, high test-retest reliability (Interclass Correlation Coefficient (ICC) = 0.88), and good concurrent validity [26], [28]. The AUDIT is a 10-item questionnaire developed by the World Health Organisation, each item is scored on a four-point scale, and different question groups measure hazardous consumption or dependence behaviour. The AUDIT's psychometric properties have been demonstrated to be excellent in a wide range of studies, with high internal consistency (Chronbach's alpha ∼0.83), test-retest reliability (ICC = 0.87–0.93) and good concurrent validity [29]. Telephone administration has proved efficient and successful for both the AUDIT [30], and the SDS [33], [34]. The Structured Clinical Interview for DSM Disorders-Research Version (SCID-RV) [35] was administered at the baseline laboratory visit by a trained psychologist to assess for Axis-I psychiatric disorders. The Timeline Followback (TLFB) [36], [37] was used to assess alcohol, tobacco and cannabis use at each laboratory visit. The major urinary metabolite of cannabis, 11-nor-Δ9-tetrahydrocannabinol-9-carboxylic acid (THC-COOH) was quantified by gas chromatography-mass spectrometry and normalized by urinary creatinine level (THC-COOH/creatinine) to validate self reported abstinence [38]. An online version of the CWS [11], including a functional impairment subscale, was used to quantify the impact of cannabis withdrawal symptoms on normal daily functioning using a 10-point Likert scale question asking how each symptom NEGATIVELY impacted getting through or completing normal daily activities, assessed alongside withdrawal intensity (see Table 1). The CWS has been shown to have excellent psychometric properties, with high internal reliability (Chronbach's alpha = 0.91) and test-retest reliability (ICC = 0.95) [11]. Whilst the withdrawal symptoms on the CWS do not represent functional impairment per se (e.g. not being able to socialise), the negative impact component on the CWS anchored to each symptom specifically addresses this question by having patients give an indication of the magnitude of impairment to normal daily functioning caused by each symptom. PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. The cannabis withdrawal scale. https://doi.org/10.1371/journal.pone.0044864.t001 Study procedures and cannabis use The University of New South Wales Human Research Ethics Committee approved all procedures (Approval number: HREC 09152). Study participants filled out the CWS online daily during a one-week baseline “smoking as usual” period, and a two-week cannabis abstinence attempt. The cannabis abstinence attempt was supported with a one-hour psychological intervention and contingency management payments totalling AU$450 for adherence to study protocol, including the provision of urine samples indicating no cannabis use during the two week abstinence period. Participants in the study visited the research facility five times over the course of their involvement in the study: once at baseline, once after a week of smoking as usual, once after the first week of abstinence, and again at the end of the second week of abstinence. A final visit for follow-up interviews was performed one month after the end of the experimental abstinence period. Study procedures, including eligibility screening, and a full documentation of face to face interview schedules, the content of the 1 hour psychological intervention at the beginning of the quit attempt and monitoring of study adherence (including confirmation of cannabis abstinence) are described in a previous report [11]. If participants used cannabis in the first abstinence week, they were offered an opportunity to restart the abstinence period. If they restarted, data from their first abstinence attempt, up to the day of cannabis use, is used in the current functional impairment analysis. If participants used cannabis during the second week of abstinence, any post cannabis use withdrawal data were discarded and all functional impairment data collected prior to cannabis use was retained for analysis. Participants were considered to have used cannabis if they self-reported cannabis use or if their THC-COOH∶creatinine ratios showed any increase during the abstinence phase relative to their one week ‘smoking as usual’ baseline phase levels [39]. Data from two participants were removed from all analyses due to a conflict between their self-reported cannabis use and urinalysis tests at weeks 1 and 2 of the abstinence period. All other participants' cannabis abstinence reports were validated by urinalysis. Participant's cannabis use during the month following the end of their abstinence period was monitored by self report (TLFB) and confirmed with a single urinalysis at the one month follow up interview requiring THC-COOH∶creatinine levels to be below 50 ng/ml to be classed as abstinent. Analysis Descriptive statistics were reported as frequency and means with standard deviations and ranges (except where non-parametric analyses were performed, where continuous variables were described using medians and interquartile range). Analysis of Variance, Pearson Chi Square and Fishers exact test were used to compare clinical characteristics of: (1) participants who relapsed to those who didn't relapse, and (2) participants who were lost to follow up to those who were not lost to follow-up. Aim 1: Identify if the level of functional impairment during abstinence is predicted by severity of dependence, or pre-quit attempt cannabis use levels, whilst controlling for age and gender To explore whether the level of functional impairment could be predicted by cannabis use, severity of dependence, age or gender, a General Linear Mixed Model (GLMM) was constructed with total daily functional impairment scores (summed across all 19 valid items in a day) as the dependent variable. The dependent variable represents repeated measurements, and the GLMM allows explicit modelling of covariance between daily measures within individual subjects (using an autoregressive covariance structure of order 1). The model was constructed in a hierarchical manner, with the null model consisting of the intercept only. Step one explored the effect of time in abstinence on functional impairment scores, as abstinence represents the primary and most fundamental independent variable generating withdrawal phenomena. Step two added the non-cannabis use related covariates (age and gender) in order to ensure they are controlled for ahead of adding the cannabis use variables, which are the hypothesised drivers of withdrawal related functional impairment. Step three added cannabis-related variables (pre-quit cannabis use levels and scores on the SDS), to test their relative explanatory power having controlled for other variables. SDS scores were analysed as continuous variables as dichotomising loses valuable statistical information and power and obscures any nonlinearities between variables [40], [41]. However for graphical purposes the data were split into high and low SDS groups. As mixed-effects models do not generate traditional R2 values, the variance in withdrawal related functional impairment explained by the variables at each step of the model was estimated using a pseudo R2 calculated from the log likelihood ratios output from mixed models (termed R2 LR ) [42]. Because R2 values are known to increase with the number of variables in a model, irrespective of their predictive power, the model also presents Akaikes Information Criteria [43] as a measure of model fit for each step, as this value penalises models for increased complexity. Because of sample size restrictions the analysis was not powered to look at the interactions in this longitudinal analysis. Aim 2: Identify if a relationship exists between withdrawal related functional impairment and the severity of cannabis withdrawal symptoms In order to examine the relationship between withdrawal severity and functional impairment, it was determined that all other possible drivers of functional impairment should first be controlled for. Hence the full model from Aim 1, examining the predictors of functional impairment was retained, with the addition of a final step. In this final step, the effect of adding CWS symptom severity scores on the explained observed variance in functional impairment was examined. Exploratory Aim 1: Relapse to cannabis use is associated with greater levels of functional impairment from cannabis withdrawal symptoms To assess if participants who relapsed during the abstinence period had higher levels of impairment from cannabis withdrawal, each symptom's functional impairment score was analysed separately using a univariate approach. Rank transformed functional impairment scores were used as dependent variables in a series of non parametric two way repeated measures Analysis of Variance [44], with time as the repeated measure (baseline week vs. abstinence) and relapse group as the between subject factor. Symptoms were then sorted (separately for each SDS group [45]) on their univariate F-values for the interaction between time and relapse group. Withdrawal symptoms significant in the univariate analyses were then entered as independent variables in a multivariate logistic regression [44] by subtracting impairment scores from abstinence week 1 from baseline smoking as usual scores to create ‘change’ variables. Membership of the relapse group (or not) was the binary dependent variable. To fully explore the withdrawal parameter space contributing to relapse, the selected withdrawal symptoms were grouped into either somatic or negative affect symptoms. Exploratory Aim 2: Greater functional impairment during the abstinence attempt is predictive of a greater amount of cannabis consumed during a one-month follow-up period To test the impact of functional impairment during abstinence on levels of cannabis use at one-month follow up, a linear regression (Generalized Linear Model – GLM) was constructed with average weekly cannabis use at follow-up as the dependent variable. The independent predictor was the CWS sum total functional impairment score, calculated by averaging daily scores across the two-week abstinence period. Pre-study cannabis use levels and SDS scores were controlled for as covariates. CWS functional impairment data was normalized with a square root transformation (as the data had a long positive tail). Exploratory Aim 3: What factors predict time to relapse? A Generalised Linear Model was used to analyse the time taken to relapse (in days) (dependent variable), with the average change in functional impairment scores between baseline and abstinence as the independent variable. The analysis controlled for age, gender, SDS scores and the mean weekly cannabis use prior to entering the study. All analyses were carried out using SPSS version 20.

Discussion Consistent with previous work on withdrawal severity [11], higher levels of dependence on cannabis were associated with higher levels of functional impairment from cannabis withdrawal (Table 3 and Figure 2). The strongest predictor of functional impairment to normal daily activities from cannabis withdrawal was the severity of the cannabis withdrawal symptoms (Table 3). As tobacco use increased during abstinence compared to the baseline ‘smoking as usual’ week (Table 2), it is unlikely that the observed impairment was due to nicotine withdrawal. Relapse to cannabis use was associated with higher levels of functional impairment in the high SDS user group (Table 4). Despite the fact that members of the low SDS group also relapsed during the abstinence attempt, their relapse was not associated with significant levels of functional impairment from withdrawal (Table 5). Whilst the univariate analysis showed a subset of withdrawal symptoms were associated with increased functional impairment in those who relapsed (I had trouble getting to sleep, I had no appetite, I felt anxious, Life felt like an uphill struggle, I felt physically tense, I had mood swings and I felt depressed; Table 4), the multivariate predictive model indicated that only “physical tension” remained a significant predictor of relapse for the whole group (Table 6). These findings may suggest that somatic and negative affect symptoms respond similarly during a quit attempt, but somatic withdrawal symptoms may be more pertinent to predicting relapse in this sample of non-treatment seekers. If the same were observed in a clinical sample, this may be useful for counselling cannabis smokers on what changes to expect during their quit attempt. However it is important to stress that the multivariate models may suggest which withdrawal symptoms integrate relapse risk information efficiently, rather than revealing specific causal paths. It is of note that the average level of functional impairment caused by cannabis withdrawal symptoms was relatively mild (the highest median total CWS functional impairment scores during abstinence were 60 out of a possible 190 during week 1 of abstinence in high SDS users who relapsed; Table 4) among this sample of non-treatment seeking users. Several factors should be considered when interpreting these data. First, the data represent only one aspect of the withdrawal syndrome – that being cannabis users' perception of the impact of withdrawal symptoms on carrying out their normal daily activities. Functional impairment received a uniformly lower endorsement for all of the symptoms surveyed relative to symptom intensity scores [11]. Second, the focus on average values across all of the participants in the study masks the variation in functional impairment experienced between people. As can be seen from the ‘interquartile range’ data presented in Tables 4 and 5, some study participants reported that cannabis withdrawal symptoms caused very high levels of functional impairment. Third, the study population consisted of non-treatment seekers, so it is reasonable to expect that higher levels of withdrawal-related functional impairment would be reported by treatment seekers, and this will be a fruitful avenue for future research. Finally, whilst the cannabis withdrawal syndrome is mild for most users, it appears comparable with tobacco withdrawal [20], [24], [46] which is of well established clinical significance. This study has several notable limitations. The sample size is small, which can lead to inflated Type I errors in the analyses, and precludes conduct of factor analyses on the CWS to test any a priori predictions on the underlying structure of the cannabis withdrawal syndrome. The ad hoc analyses grouping selected symptoms into somatic and negative affect variables used in this present work would benefit from more rigorous factor analytical methods with larger sample sizes. The relapse analysis was by necessity opportunistic (hence we did not set out a formal a priori power calculation for this analysis), and the small numbers of participants in the relapse group suggest that any findings relating to relapse would benefit from further research. The post-hoc power analysis of total withdrawal scores suggests that ∼64 participants (32 in each group), would be required to detect a difference of the magnitude observed in this study. However effect size calculation from small sample sizes is prone to error [47], further supporting the need to follow up the relapse findings with larger datasets. As mentioned previously, a clear limitation of the present findings is that the study population was generally non-treatment seeking, so it may represent a conservative account of the findings in a treatment delivery context. Performing the same study in a clinical treatment seeking group may be expected to find more severe withdrawal having greater negative consequences to daily life, with the potential for greater levels of relapse. Examining withdrawal in such clinical samples will be a fruitful area of future research. It is also worthy of note that Table 4 shows measureable levels of functional impairment at baseline (before abstinence from cannabis) for the high SDS group. This is consistent with previous studies of both cannabis [48] and tobacco [46] withdrawal, and is expected as each individual will have their own baseline level of functioning, for example a mild usual sleep problem or a usual mildly depressed mood, which may become more substantial during abstinence. Finally there was no external corroboration of the self-reported functional impairment, or use of an alternative functional impairment measure. In conclusion, cannabis withdrawal is clinically significant because it is associated with elevated functional impairment to normal daily activities, and the more severe the withdrawal is, the more severe the functional impairment is. Elevated functional impairment from a cluster of cannabis withdrawal symptoms is associated with relapse in more severely dependent users. Those participants with higher levels of functional impairment from cannabis withdrawal also consumed more cannabis in the month following the end of the experimental abstinence period. Higher levels of cannabis dependence (scores on the SDS) predicted greater functional impairment from cannabis withdrawal. These findings suggest that higher SDS scores can be used to predict problematic withdrawal requiring more intense treatment that can be monitored closely using the Cannabis Withdrawal Scale (Table 1) [11]. Finally and speculatively, the finding that lower levels of cannabis dependence predict lower levels of functional impairment from withdrawal (and thus lower levels of relapse) may indicate that stepped reductions in cannabis use prior to a quit attempt could reduce dependence, and thus reduce levels of withdrawal related functional impairment, improving chances of achieving and maintaining abstinence. Targeting the withdrawal symptoms that contribute most to functional impairment during a quit attempt might be a useful treatment approach (e.g. stress management techniques to relieve physical tension and possible pharmacological interventions for alleviating the physical aspects of withdrawal such as loss of appetite and sleep dysregulation).

Acknowledgments Jennifer Mackenzie and Lisa Robins performed the Structured Clinical Interviews. John Saunders, Robert Ali and Michelle Dailey provided comments on the development of the study. Tim Slade, Barbara Toson, Mathew Crowther and Jennifer Chalmers provided statistical advice and discussion. David Burke helped prepare the images.

Author Contributions Conceived and designed the experiments: DJA JC MMN AJB. Performed the experiments: DJA. Analyzed the data: DJA. Contributed reagents/materials/analysis tools: SF AM JL. Wrote the paper: DJA JC MN AJB.